1 Introduction

Scheduling of Autonomous Mobile Robots (AMRs) for just-in-time delivery to the main assembly line poses a significant problem. The challenges associated with this issue include the size of the fleet to schedule (De Ryck et al. 2020), as well as ensuring the robots’ ability to complete their pick-up/delivery tasks within specified time windows (Yao et al. 2020), as well as handling scalability concerns. Moreover, depending on the robots’ size and the plant layout’s geometrical features, areas may exist within the plant where simultaneous transit of two or more robots leads to conflicts (Pratissoli et al. 2023), i.e., instances where the robots obstruct each other. Lastly, indoor-operating robots are typically powered by batteries, which possess limited operating ranges and require non-negligible charging times (Abderrahim et al. 2020).

A suitable scheduler for such a problem should simultaneously satisfy the following requirements: (i) assign one or more tasks to each robot, ensuring each task is executed within its designated time window; (ii) calculate a path for each robot to carry out its assigned tasks; (iii) schedule the robots along their respective paths to prevent conflicts; and (iv) guarantee that the robots never run out of charge.

Moreover, an industrial plant is a dynamic environment where frequent changes require re-scheduling. Therefore it is necessary that a new schedule can be computed quickly (from seconds to minutes, depending on the specific application). Consequenty, the scheduler must be sufficiently fast to schedule a possibly large fleet in the limited available time.

Let us focus on the requirements from the previous paragraphs one at the time. (i) Assigning all tasks to robots equates to design Routes for the robots. Typically, the robots are stationed at one or multiple depots and tasks are assigned to them in a specified order. Therefore the robots leave their depot, travel from one task to the next in their ordered assignment, and return to the depot, thus defining a closed route. (ii) There may be several paths in a plant to travel from one point to another. The choice of one path over another may have consequences in terms of travelled length or possibility of conflicting with other robots. (iii) the road segments, intersections, depots, and workstations are characterized by a capacity based on the number of robots that can be simultaneously in these areas. Therefore capacity constraints are defined in order to produce conflict-free schedules. (iv) a robot may not have enough charge to serve all customers it is assigned to. Hence, after executing some tasks, it may need to return to its depot and recharge the battery, before it can continue executing the remaining tasks.

Fig. 1
figure 1

Simplified flowchart of ComSat

In Roselli et al. (2021) we formalized the problem described above and named it the Conflict-Free Electric Vehicle Routing Problem (CF-EVRP); we listed all its requirements in detail and formulated a mathematical model for it. Also, we used the Satisfiability Modulo Theory (SMT) (Barrett et al. 2009; De Moura and Bjørner 2011) solver Z3 (De Moura and Bjørner 2008) to solve generated problem instances of the CF-EVRP and found that it was not suitable to schedule large fleets, as it took several minutes to solve instances involving only few vehicles. Therefore, in Roselli et al. (2022) we presented a compositional algorithm, ComSat, for solving the CF-EVRP. ComSat breaks down the CF-EVRP into sub-problems and iteratively solves them to find a feasible solution to the overall problem. Experimental and analytical evaluation shows that ComSat generates high-quality but not necessarily optimal solutions.

Briefly, ComSat (see Fig. 1) solves the Routing Problem, i.e., computes routes to execute all the tasks, by calling the Router algorithm, and assigns vehicles to the routes (the Assignment Problem) by calling the Assign algorithm; this part of ComSat is henceforth called Feasible Routes Search (FRS). Then, ComSat solves the Capacity Verification Problem, i.e., schedules the vehicles along their paths (initially Dijkstra’s algorithm (Dijkstra 1959) is used to compute such paths) by calling the CapacityVerifier algorithm (\( CV \)), so that no conflicts arise among them. However, it may be that a feasible schedule cannot be found because, using the current paths, it is impossible to avoid conflicts and meet the time windows; then alternative paths have to be computed. This is handled in the Conflict-Free Paths Search (CFPS) where the Paths-Changer algorithm (\( PC \)) solves the Paths Changing Problem, i.e., finds alternative sets of paths if the current schedule has conflicts, and the CapacityVerifier finds, if possible, a conflict-free schedule.

Experiments show two bottlenecks in the algorithm, where a large number of iterations may be required in order to find feasible solutions to the CF-EVRP;

  • In the FRS, the number of possible routes grows exponentially with the number of tasks, therefore even small problem instances can lead to a large number of possible solutions to the Routing Problem. Many of these sets of routes may not satisfy the Assignment Problem though, leading to a large number of unnecessary iterations between these two sub-problems.

  • When a schedule has conflicts, the Paths-Changer algorithm will find an alternative set of paths. Since the Paths-Changer has no information about why the previous paths led to conflicts among the vehicles, the new paths may lead to the same conflict, or at least some of them. It may therefore take several iterations to find conflict-free paths.

In this paper we focus on improving the performance of ComSat by ruling out infeasible solutions before they are computed, hence keeping the number of iterations between sub-problems low. In our previous work (Roselli et al. 2022), we tackled the bottleneck in the CFPS by formulating improved versions of the \( PC \) and \( CV \). In this work, we generalize such improvements so that they are applicable to any instance of the CF-EVRP. Moreover, we present a tighter formulation of the Routing Problem that prevents the generation of solutions that will be infeasible when solving the Assignment Problem. We also present a further improvement where the Routing and Assignment problems are combined into the E-Routing Problem. This approach allows to avoid iterating between the two sub-problems, and experiments show that the computation time required to solve the new sub-problem is not larger than the time required to solve the Routing Problem only.

The sub-problems in ComSat are modelled as Mixed Integer Linear Programming (MILP) (Kondili et al. 1993), and SMT problems. For the CFPS, polynomial time algorithms exist to find paths in graphs (Arumugam et al. 2016). However, modelling the Paths Changing Problem as an SMT problem is beneficial as it allows us to define problem-specific requirements, such as not returning solutions that are already proven infeasible because they violate the capacity constraints. Moreover, when a problem is infeasible, SMT solvers have the ability to return a Minimal Unsatisfiable Core (MUC) (Cimatti et al. 2011), i.e., one of the (possibly many) smallest subsets of constraints that make the problem infeasible. The MUC can provide useful information about why a problem is infeasible and can therefore be used to guide the search towards a feasible solution (Selsam and Bjørner 2019). Therefore, when dealing with the CF-EVRP, the MUC can be extracted when the Capacity Verification Problem is infeasible, and used to define additional constraints for the Paths Changing Problem to increase the chances of finding a feasible schedule.

The contributions in this paper are: (i) improvements on the FRS; (ii) exploitation of MUC to extract information about the infeasibility of an SMT formula representing a conflicting schedule for a VRP and use of such information to find conflict-free schedules; (iii) performance comparison between the different versions of ComSat discussed in the paper over a set of the CF-EVRP problem instances.

The remainder of the paper is organized as follows. Section 2 presents an overview on the topic and introduces existing publications this work is built on. Section 3 provides preliminaries, together with a full description of ComSat. Sections 4 and 5 present the mathematical models of the sub-problems that form the FRS and CFPS, respectively, how they are improved to reduce the number of iterations and the computation time. Proof of soundness and completeness of the procedures is provided in Section 6. In Section 7, the results of the analysis over a set of problem instances are presented. Finally, conclusions are drawn in Section 8.

2 Literature review

The CF-EVRP is an extension of the well-known Vehicle Routing Problem (VRP) (Dantzig and Ramser 1959), a combinatorial optimization problem that involves designing routes for vehicles to visit specific locations, with the objective of minimizing the total length of these routes. Several extensions of the original VRP exist (Kucukoglu et al. 2021) that tackle different aspects of the problem and originate from different real world applications. For instance, the VRP with Time Windows (Desrochers et al. 1992) extends the original problem to include, for each task, an earliest and latest arrival time i.e., a time window in which the task has to be executed. Another extension is the Multi Depot VRP (Lim and Wang 2005) where there can be several depots the vehicles are dispatched from.

For battery powered vehicles, there has been a growing attention to formulating VRPs that account for limited operating range and non negligible charging time. In Schneider et al. (2014) the Electric VRP (E-VRP) is first introduced. In this work, vehicles can return to the depot to fully recharge their batteries (partial charging is tackled in subsequent works Keskin and Çatay 2016; Cortés-Murcia et al. 2019) after which they may be dispatched again to complete more tasks. The authors present a hybrid heuristic that combines neighbourhood (Hansen and Mladenovi 2005) and tabu (Glover 1989) search to solve problem instances involving up to 500 tasks.

Thus far, the VRPs discussed are characterized by the ability of travelling directly from any task’s location to any other, and the movement of one vehicle is unaffected by the presence of other vehicles. These VRPs are suitable for applications such as routing of cars or trucks on public roads, where in most cases vehicles can avoid obstructing each other’s path simply by adhering to the traffic rules. Furthermore, vehicles are still widely operated by humans, whose decision-making ability to resolve conflicts can help in those cases where traffic rules alone are insufficient.

On the other hand, a plant layout can be a challenging environment to drive in, due to less space available to maneuver. Also, AMRs typically do not have the same decision-making skills as human drivers. Therefore the problem of computing conflict-free schedules for fleets of AMRs is addressed by several works. There have been attempts to find exact solutions to conflict-free routing problems by means of general purpose solvers. For instance, in Murakami (2020), a MILP formulation to design conflict-free routes for capacitated vehicles is presented. An SMT formulation is introduced in Roselli et al. (2018). The size of such formulation in terms of the number of variables and constraints grows exponentially with the number of vehicles, tasks, and the size of the plant; only very small instances can be solved to optimality within reasonable time.

Alternatively, approximate methods can be used to solve larger problem instances. One early such work (Krishnamurthy et al. 1993) uses column generation to compute conflict-free routes for vehicles on a bidirectional network. An ant colony algorithm is applied to the problem of job shop scheduling and conflict free routing of vehicles by Saidi-Mehrabad et al. (2015); in Yuan et al. (2016), paths are computed for one vehicle at a time using a modified \(A^*\) (Hart et al. 1968) to find paths that overlap as little as possible with the paths already computed for other vehicles. This, in combination with collision resolution rules, allow to relieve traffic congestion. A heuristic approach to solve the conflict-free routing problem with storage allocation is presented by Thanos et al. (2019). In Zhong et al. (2020) is presented a hybrid evolutionary algorithm to deal with conflict-free vehicles scheduling in automated container terminals; finally, in Daugherty et al. (2018) the problem of conflict-free routing of vehicles is handled by a heuristic algorithm based on local search.

A typical way to trade optimality for computational speed, is to decompose the problem into sub-problems. For example, in Corréa et al. (2007), the problem is divided into first assigning the tasks to the vehicles, and then route the vehicles through the plant. ComSat  (Roselli et al. 2022) is another example of a compositional algorithm to solve this problem. A main difference with Corréa et al. (2007) is that ComSat accounts for the recharge of the vehicles.

In conclusion, the CF-EVRP involves a number of challenges deriving from the different features that characterize it, such as vehicles’ limited operating range and non-negligible charging time, time windows for the execution of the tasks, and above all, limited capacity of the road segments of the plant layout. As the CF-EVRP is an extension of the VRP, several studies exist on the topic, and each of the additional features (battery charge, capacity constraints on the routes, time windows, etc.) have been tackled previously. Whatever the objective function (total travelled distance, number of robots, etc.) finding optimal solutions fast is only tractable for small problem instances. Hence several approximate methods have been used to solve larger problem instances fast. In our previous work (Roselli et al. 2018) we claimed the novelty of the CF-EVRP as, to the best of our knowledge and at the time of publication, no other formulation included all the requirements the CF-EVRP did. We hence developed the compositional algorithm ComSat to quickly solve larger problem instances, and in this work, we present an improved version of it.

3 Preliminaries

In the CF-EVRP the plant layout is represented by a finite, strongly connected, weighted, directed graph, where edges represent road segments and nodes represent either tasks’ locations or intersections between road segments. A task is defined by a unique (numerical) identifier, a location, and a time window, i.e., a lower and upper bound that represent the earliest and latest arrival time allowed to execute the task. Edges have two attributes, the first representing the road segment’s length, and the second its capacity. The capacity is 2 if two vehicles can simultaneously travel in opposite directions, 1 otherwise.

The following logical operators are used as a shorthand to express cardinality constraints (Sinz 2005) in the SMT sub-problems:

  • \(\bigoplus _{i \in \mathcal {I}}(x_i,n):\) exactly n variables \(x_i\) are true \(\forall i \in \mathcal {I}\);

  • \(\text {If}(c,o_1,o_2):\) evaluates to \(o_1\) if c is true, else \(o_2\).

Moreover, in the MILP sub-problems, the big-M method (Trespalacios and Grossmann 2015) is used to model alternatives, hence the need for a big enough integer M.

The following definitions are provided:

  • Node: a location in the plant. A node can only accommodate one vehicle at a time unless it is a hub node that can accommodate an arbitrary number of vehicles.

    • \(\mathcal {N}\): a finite set of nodes.

    • \(\mathcal {N}_H \subseteq \mathcal {N}\): the set of hub nodes.

  • Depots: nodes at which one or more vehicles start and must return to after completing the assigned tasks. A depot can accommodate an arbitrary number of vehicles at the same time, thus all depots are hubs.

    • \(\emptyset \subset \mathcal {D}\subseteq \mathcal {N}_H \): the set of depots.

    • \( \mathcal {D_S}= \{\,s_k \mid k \in \mathcal {D}\} \): the set of dummy tasks representing the start from depot k.

    • \( \mathcal {D_F}= \{\,f_k \mid k \in \mathcal {D}\} \): the set of dummy tasks representing the arrival at depot k.

    The sets \(\mathcal {D_S}\) and \(\mathcal {D_F}\) are disjoint with each other and with the set of tasks \(\mathcal {K}\) (see below).

  • Edge: a road segment that connects two nodes.

    • \(\mathcal {E}\subseteq \mathcal {N}\times \mathcal {N}\): the finite set of direct edges.

    • \(\bar{e}\): the reverse edge of edge \(e \in \mathcal {E}\).

    • \( |e|\in \mathbb {R}^+\): the length of edge \(e \in \mathcal {E}\).

    • \( g_e \in \{1,2\}\): the capacity of edge \(e \in \mathcal {E}\).

  • Time horizon: a fixed, continuous point in time when all tasks have ended.

    • \( \textit{T} \): the time horizon.

  • Task: Entity representing a place to be visited exactly once by a vehicle in order to pickup or delivery material. A task is always associated with a node where the pickup/delivery takes place, and has a time window indicating the earliest and latest time at which the node can be visited. Unless explicitly given, the time window is the entire time span [0, T]. Moreover, each task has a precedence list that indicates other tasks that must be executed before the task itself. This so, since the tasks represent pickups and deliveries, hence a vehicle has to deliver the goods after it has picked them up.

    • \(\mathcal {K}\): the finite set of all tasks. Let \(\mathcal {K}^+ = \mathcal {K} \cup \mathcal {D_S}\cup \mathcal {D_F}\).

    • \( l_{k}, u_k \in \mathbb {R}^+, \ k \in \mathcal {K}\): the time window’s lower (\(l_k\)) and upper (\(u_k\)) bound for task k such that \(u_{k} > l_{k}\).

    • \( \tau _{k} \in \mathbb {R}^+\): the service time of task k.

    • \(L_{k} \in \mathcal {N}, \text {for} \ k \in \mathcal {K}\): location of task k.

    • \( \mathcal {Q}_{k} \subset \mathcal {K}, \ k \in \mathcal {K} \): the set of tasks to execute before task k.

    In order to execute a task, the vehicle assigned to it will (unless the task’s locations coincides with the vehicle’s initial location) visit nodes and edges in the graph other than the node where the task is located.

  • Vehicle: an AGV, that is able to move between locations in the plant and perform pickup and delivery operations. Battery-powered vehicles have a limited operating range, travel at constant speed or are stationary, and their energy consumption is linearly proportional to the distance travelled; they have the ability to recharge at the depot they are dispatched from, but not at other depotsFootnote 1. Charging, when executed, restores the battery to its full charge capacity; therefore, partial charging is not permitted. This restriction, inspired by Schneider et al. (2014), is necessary to formulate the routing problem of Section 4.2. Finally, we assume that a vehicle’s remaining charge is directly proportional to the vehicle’s remaining operating range and, in order to simplify the notation, we only talk about the latter.

    • \(\mathcal {V} \): the finite set of all vehicles.

    • \( \mathcal {V} _k \subseteq \mathcal {V}, \ \forall k \in \mathcal {K} \): set of vehicles eligible for task k.

    • \( \psi \in \mathbb {R^+}\): the vehicles’ maximum operating range when their battery is fully charged (distance).

    • \( C \in \mathbb {R^+}\): the charging coefficient (increase of remaining operating range per time unit).

    • \(\Omega \in \mathbb {R^+}\): vehicle speed (constant) while moving.

  • Route: a sequence, starting and ending at the same depot, with unique tasks in-between that may have the same depot embedded:

    • \(R_j = \langle d, k_1, k_2, \dots , d \dots , k_{n-1}, k_n, d\rangle \) where \(d\in \mathcal {D}\), \(k_i\in \mathcal {K}\)

    • \(\qquad k_i \ne k_j\) for \(i\ne j\), and \(n\le |\mathcal {K}|\), since a route can at most include all tasks.

  • Route set: a set of routes such that each task belongs to exactly one route, thus guaranteeing that all tasks are served.

    • \( \mathcal {R} = \{ R_1,\ldots ,R_m \},\ m \le |\mathcal {K}|\)

    A route contains at least one task, hence \(0 < m \le |\mathcal {K}|\).

  • Pair Set of route R: set containing the sequence of tasks of a route \(R=\langle k_{1},\ldots ,k_{m} \rangle \), grouped as pairs in sequence.

    • \(\mathcal {P} _R= \{ \langle k_1,k_2\rangle ,\langle k_2,k_3 \rangle ,\ldots ,\langle k_{m-1},k_m \rangle \} \)

  • Path: ordered set of unique nodes. It is used to keep track of how vehicles are travelling among tasks of routes, since each pair of tasks in a route is connected by a path.

    • \(\theta _p = \langle n_1,\ldots ,n_m \rangle , \ p \in \mathcal {P} _R, \ m \le |\mathcal {N}|,\)

    • \(\qquad n_i\in \mathcal {N},\, i = 1,\ldots ,m \)

  • Edge sequence: ordered set of unique edges for a given path \(\theta _p\).

    • \(\delta _p = \langle e_1,\ldots ,e_m \rangle , \ p \in \mathcal {P} _R, \ m = \vert \theta _p \vert - 1,\)

    • \( \qquad e_i\in \mathcal {E},\, i = 1,\ldots ,m \)

3.1 Recap of ComSat

In order to understand the changes to ComSat introduced in this work, let us recap how it works, and what sub-algorithms it is composed of. The initial step involves computing a set of the (possibly many) shortest paths between each pair of tasks, hereafter referred to as the current paths \( CP \). The first time, the computation of the \( CP \) is performed using Dijkstra’s algorithm, hence \( CP \) will be the shortest paths between the tasks, and are part of the input for the Router algorithm. When the Router is called, neither the vehicles’ availability nor the segment capacities are considered; the goal is simply to design routes to execute tasks within the time windows. At this point, the need for specific paths \( CP \) is that, through the constant speed \(\Omega \), it is possible to infer the arrival time at the tasks’ locations in order to guarantee that time windows are respected. If the Routing problem is infeasible, the whole problem is infeasible, since the tasks cannot be executed within n their time windows.

If the Routing Problem is feasible, Router returns a non-empty set of routes \(\mathcal {R} \) and for each route \(R\in \mathcal {R} \) the latest time \(\Upsilon _R\) at which R can start, while still meeting all the time windows of its tasks. \(\mathcal {R} \) is stored in the set \( PR \) so that each time Router is called, \( PR \) can be used to rule it out. In the next step, Assign will determine which vehicle is assigned to each route based on the routes’ requirements for specific types of vehicles, their latest start time, and the vehicles’ operating range and charging rate; also for the Assignment Problem there can be more feasible solutions, therefore it is important to store the current assignment \(\mathcal {A} \) in the set \( PA \) to be able to rule it out the next time Assign is called. If the Assignment Problem is infeasible the algorithm backtracks and solves the Routing Problem again, generating new routes that might be assignable; otherwise, ComSat proceeds to the Capacity Verification Problem.

At this point, routes have been assigned vehicles to execute them, and start times \(\beta _R, \ \forall R\in \mathcal {R} \) have been computed to meet the vehicles’ need for charging. Hence it is possible to verify if the execution of the routes is possible without violating the capacity constraints. If that is the case, the overall problem is feasible and ComSat terminates and returns a feasible schedule. On the other hand, if this step is infeasible, ComSat will try to find alternative paths (currently the shortest paths are used as \( CP \)) for the vehicles to execute the routes by solving the Paths Changing Problem.

If the Paths Changing Problem is feasible, the new paths found become the current paths \( CP \) and ComSat backtracks to verify whether a conflict-free schedule exists by solving the Capacity Verification Problem again; if not, the \( PC \) algorithm is called again. Otherwise, if the Paths Changing Problem is infeasible, the algorithm backtracks and looks for a new assignment.

Whenever the Assignment Problem is infeasible, all possible assignments for the current set of routes \(\mathcal {R} \) have been explored. In the same way, whenever the Paths Changing Problem is infeasible, all possible paths for the current assignment \(\mathcal {A} \) have been explored. Therefore, the set of previous paths \( PP \) is emptied because these paths are only eligible for the current assignment, and the shortest paths are set as current paths to compute the next assignment.

Note that the complete version of ComSat also involves an additional sub-problem, namely the Routes Verification Problem, where it is verified whether the previously computed routes are still feasible when using the newly computed paths. However, discussing this sub-problem is out of the scope of this paper since the sub-problem is very quick to solve and does not constitute a bottleneck; instead, the reader is referred to Roselli et al. (2022) for further details.

3.2 The minimal unsat core

For infeasible problems, a subset of the constraints that conflict, i.e., they cannot all simultaneously be satisfied, can be identified. Such a subset is called an Unsat Core. An Unsat Core with the property that removing any one of the constraints makes the Unsat Core feasible, is said to be minimal.

Formally, given an SMT formula \(\varphi \) and a set of conflicting constraints \(\mathcal {C} \subseteq \varphi \), \(\mathcal {C} \) is a Minimal Unsat Core (MUC) of \(\varphi \) if removing any constraint \(\mathcal {C} _i\in \mathcal {C} \) makes \(\mathcal {C} \setminus \mathcal {C} _i\) no longer infeasible; removing \(\mathcal {C} _i\) removes the particular conflict represented by the MUC. Consequently, for an infeasible problem with a MUC \(\mathcal {C} \), adding to the problem a constraint that prevents all the constraints in \(\mathcal {C} \) to be simultaneously active will resolve this particular conflict.

The naïve approach to MUC extraction, Dershowitz et al. (2006), successively removes constraints and solves the problem again; if the problem is still infeasible after a constraint has been removed that constraint does not belong to a MUC. There exist more efficient approaches; the MUC  (Huang 2005) algorithm based on efficient manipulation of Binary Decision Trees guarantees the extraction of a minimal Unsat Core. Nadel (2010) presents an algorithm based on the resolution graph (Kroening and Strichman 2016) for MUC extraction. Nadel et al. (2013) improves the resolution based algorithm using model rotation and path strengthening.

In Section 5 we discuss how to use the MUC to extract information about conflicts on when the Capacity Verification Problem is infeasible, in order to guide the search for alternative, feasible paths by defining additional constraints for the Paths Changing Problem.

4 The feasible routes search

As mentioned in Section 1, experiments showed that for some problem instances Router can generate a large number of solutions to the routing problem \(\mathcal {R} \) that lead to the Assignment Problem being infeasible. In this section we present first a tighter formulation to break the symmetry of different solutions, and then an approach that involves merging the Routing and Assignment problems, to deal with this bottleneck.

4.1 A tighter formulation to break the symmetry among solutions

The Routing problem as formulated in Roselli et al. (2022) does not permit charging during the routes. Therefore, in this section, routes are defined as sequences that starts and end at the same depot, with unique tasks in between and with no depot embedded.

The problem is formulated using 0–1 variables \(\sigma _{rk_1k_2}\) (\(k_1,k_2 \in \mathcal {K}^+\!,\,r=1,\ldots ,\vert \mathcal {K} \vert \)) that represent that a route R includes direct travel from task \(k_1\) to \(k_2\). Let \(\varsigma = \{\sigma _{rk_1k_2}\mid k_1,k_2 \in \mathcal {K}^+\!,\,r=1,\ldots ,\vert \mathcal {K} \vert \}\) be the set of all variables \(\sigma _{rk_1k_2}\), then the optimal solution to the Routing Problem found at iteration h is \( \mathcal {R} ^*= \{ \sigma _{rk_1k_2} \in \varsigma \mid \sigma _{rk_1k_2} = 1 \}\); also, let \( PR ^*\) be the set containing the optimal solutions \(\mathcal {R} ^*\) found until the \((h -1)\)-th iteration. Finally, note that it is possible to compute \(\mathcal {R} \) from \(\mathcal {R} ^*\) and vice versa. Subsequently, it is possible to compute \( PR \) from \( PR ^*\) and vice versa.

Let us clarify this concept with an example. Assume that we have solved a Routing Problem involving four tasks such that \(\mathcal {K} = \{k_1,k_2,k_3,k_4\}\) and one depot d such that \(\mathcal {D_S}= \{s_d\}\) and \(\mathcal {D_F}= \{f_d\}\). One extreme case is that all tasks are executed in one route, while another extreme case is that the solution involves four routes, each including only one task. These solutions, and all the others in between, might or might not be feasible, depending on the task locations, their time windows, and other parameters (see Roselli et al. (2022) for further details). Let us assume that a possible solution in terms of the variables \(\sigma _{rk_1k_2}\) would be the following:

$$\begin{aligned} \mathcal {R} ^* = \{ \sigma _{1s_dk_1},\, \sigma _{1k_1k_2},\, \sigma _{1k_2f_d},\, \sigma _{2s_dk_3},\, \sigma _{2k_3k_4},\, \sigma _{2k_4f_d} \}. \end{aligned}$$

From \(\mathcal {R} ^*\) we can compute \(\mathcal {R} =\{\langle s_d,k_1,k_2,f_d\rangle ,\,\langle s_d,k_3,k_4,f_d\rangle \}\). Note that the first index of each \(\sigma \) variable represents the route number, and is used to keep track of the route the tasks are assigned to in order to compute \(\mathcal {R} \) from \(\mathcal {R} ^*\).

In general, a feasible Routing Problem results in a number of the \(\sigma \) variables being set to 1, thus defining a set of routes \(\mathcal {R} \). If the Assignment Problem fails for this set of routes, the Router is called again, with the additional requirement that at least one of the \(\sigma \) variables that were previously set to 1, should now be set to 0. In Roselli et al. (2022) we enforced this requirement using the following constraint:

$$\begin{aligned} \sum _{\sigma _{rk_1k_2} \in \mathcal {R} ^*}{\sigma _{rk_1k_2}} \le \ |\mathcal {R} ^* |- 1,\; \forall \mathcal {R} ^* \in PR^* . \end{aligned}$$

Assuming Eq. 1 is a solution to the Routing problem, in the next call of Router, constraint Eq. 2 expands to:

$$\begin{aligned} \sigma _{1s_dk_1} + \sigma _{1k_1k_2} + \sigma _{1k_2f_d} + \sigma _{2s_dk_3} + \sigma _{2k_3k_4} + \sigma _{2k_4f_d} \le 5. \end{aligned}$$

This call of Router may then return the solution:

$$\begin{aligned} \mathcal {R} ^* = \{ \sigma _{1s_dk_1}, \sigma _{1k_1k_2}, \sigma _{1k_2f_d}, \sigma _{3s_dk_3}, \sigma _{3k_3k_4}, \sigma _{3k_4f_d} \} \end{aligned}$$

Solutions Eqs. 1 and 3 are identical except for the route index. Therefore, if one is infeasible, the other will be as well. In order to avoid generating such identical solutions, a constraint is defined that forbids a particular sequence of tasks visited for every route index. For the example above, such a constraint would be \(\forall r=1,\ldots ,4\):

$$\begin{aligned} (\sigma _{rs_dk_1} + \sigma _{rk_1k_2 } + \sigma _{rk_3f_d} \le 3) \,\vee \, (\sigma _{rs_dk_3} + \sigma _{rk_3k_4 } + \sigma _{rk_4f_d} \le 3) \end{aligned}$$

In general, a Routing Problem solution may involve up to \(|\mathcal {K} |\) routes, and at the h-th call of Router, there are \(h -1\) solutions \(\mathcal {R} ^*\) stored in \( PR ^*\). As mentioned before, \(\mathcal {R} ^*\) is the set containing the \(\sigma \) variables that evaluated to 1 when solving the Routing Problem and it is possible to compute \(\mathcal {R} \) from \(\mathcal {R} ^*\). For \(h = 1,\ldots ,| PR ^* |\), let \(\mathcal {R} _h^*\) be the h-th solution to the Routing Problem, from which it is possible to compute \(\mathcal {R} _h\); let \(R\in \mathcal {R} _h\) be a route computed in the h-th call of Router, and \(k_n\) the task coming after task \(k_{n-1}\) in R; then, in order to avoid identical solutions, the following additional decision variables are required:

  • \(\mathcal {X}_{Rh}\) is a \(0-1\) variable that is assigned value 1 if route \(R\) of the solution h is forbidden, 0 otherwise.

  • \(\iota _{Rh r}\) is a \(0-1\) variable that is assigned value 1 if route index \(r\) is forbidden for route \(R\) of the solution h, 0 otherwise.

Therefore the constraints to rule out previous solutions can be defined as:

$$\begin{aligned} \begin{array}{lr} \mathcal {X}_{Rh} \le \iota _{Rh r}&\forall r = 1, \ldots , |\mathcal {K} |, h = 1,\ldots , | PR ^* |, \ R\in \mathcal {R} _h \end{array} \end{aligned}$$
$$\begin{aligned}&\sum _{k \in R}{\sigma _{rk_{n-1}k_{n}}} \le |R|- 1 + (1- \iota _{Rh r})\,M \nonumber \\&\quad \qquad \qquad \qquad \qquad \qquad \qquad \qquad \quad \ \ h = 1,\ldots , | PR ^* |,\ R\in \mathcal {R} _h, \ r= 1,\ldots , |\mathcal {K}| \end{aligned}$$
$$\begin{aligned}&\sum _{R \in \mathcal {R}}{\mathcal {X}_{Rh}} \ge 1&\forall h = 1,\ldots , | PR ^* | \end{aligned}$$

Constraints Eqs. 4 and 5 enforce the definitions of variables \(\mathcal {X}\) and \(\iota \), respectively; Eq. 6 states that at least one route in each of the previous solutions is forbidden. For the reminder of the paper, we will call the version of ComSat presented in Roselli et al. (2022) \(ComSat _1\), and the version using constraints Eqs. 4-6\(ComSat _2\).

4.2 Merging the routing and assignment problems

While the constraints presented in Section 4.1 can help break the symmetry of the solutions, sets of routes might still be infeasible for other reasons, and having to enumerate them, unnecessarily prolongs ComSat ’s running time. For instance, in a solution \(\mathcal {R} \) there might be two routes \(R_1\) and \(R_2\) whose lengths are 20 and 30 distance units respectively, and whose latest start times \(\Upsilon \) are 0 and 15, respectively. Also, \(R_1\) and \(R_2\) can only be executed by vehicle \(v_1\), whose speed \(\Omega \) is one distance unit per time unit. Clearly, \(R_1\) and \(R_2\) overlap in time, since \(R_1\) will start at time 0 and end at least at time 20 (depending on the service times at the tasks included in \(R_1\)), while \(R_2\) cannot start later than time 15. Therefore the Assignment Problem will be infeasible for \(\mathcal {R} \)

Moreover, even if they do not overlap in time, they might still be too close in time for one vehicle to execute them. For instance, in the example above, assume that \(\Upsilon _{R_2} = 30\) and that the operating range \( \psi \) = 40. Even though \(v_1\) can finish \(R_1\) before it needs to start \(R_2\), it does not have enough charge to execute both routes and hence needs to recharge in between them. With the charging coefficient \(C=1\) it will not be able to fully charge before time 30, hence the Assignment Problem will be infeasible for \(\mathcal {R} \).

The reason for infeasibility discussed in the previous paragraph may be generalized to an arbitrary number of routes and vehicles, e.g., three routes need the same type of vehicles but only two such vehicles are available. It is possible to define constraints that rule out the possibility of generating such routes. However, this would imply the enumeration of a large number of possible solutions, since the possible scenarios that lead to infeasibility are numerous. This might as well turn out to be as time-consuming (or more) as iterating between Router and Assign until a feasible set of routes is found.

When considering possible causes of infeasibility of the Routing Problem, it becomes clear that the lack of knowledge about the vehicles’ operating range and the non-negligible time to recharge significantly affect the generated solutions. Therefore, it may be beneficial to extend the Routing Problem to include the vehicles’ limited operating range, and the possibility to recharge batteries at the charging stations. Such a new problem, henceforth called the E-Routing Problem, would at once compute the routes to execute the tasks and assign them to the vehicles. The model we present is an extension of the problem formulation for the E-VRP presented in Schneider et al. (2014).

Because actual vehicles are considered in the E-Routing Problem, and recharging is allowed, the decision variables used to formulate a model for the Routing Problem in Roselli et al. (2022) have to be adjusted or replaced:

  • \(\sigma _{vk_1k_2}\): 0–1 variable that is 1 if vehicle \(v\) is travelling from the location of task \(k_1\) to the location of task \(k_2\), 0 otherwise.

  • \(\gamma _{k}\): non-negative real variable that represents the time when task k is served.

  • \(\epsilon _{vk}\): non-negative real variable that represents the remaining operating range of vehicle v when it is at the location of task k.

Moreover, let \( \mathcal {D_C}= \{\,c_k \mid k \in \mathcal {D}\} \) be the set of dummy tasks representing the arrival at a charging station; for each vehicle, its charging station is the station it is dispatched from (and has to go back to). For each depot, a trivial upper bound on the number of dummy tasks (each representing the possibility of recharging once) is the number of tasks \(|\mathcal {K} |\). Also, let \(\mathcal {K}^\star = \mathcal {K}^+ \cup \mathcal {D_C}\), and let \(\overline{\mathcal {V}}_k=\mathcal {V} \setminus \mathcal {V} _k\). With some abuse of notation, we can define the distance between two arbitrary tasks’ locations as \(|k_1k_2|\) instead of \(|L_{k_1}L_{k_2}|\). Let \(\Delta _k\) be the set of mutually exclusive tasks for task k (i.e. vehicles eligible for task k are not eligible for any of the task in \(\Delta _k\) due to requirements on the vehicle type); let \(\Lambda _k\) be the set of permutations of tasks that belong to \(\mathcal {Q}_{k}\), thus, each element \(\lambda \in \Lambda _k\) is the list of tasks in \(\mathcal {Q}_{k}\) sorted in a unique way. \(\Lambda _k\) is used to model that pickup tasks can be performed in different orders. Note that not all elements \(\lambda \) result in correct orderings of tasks. However, these invalid orderings will be eliminated out by the other constraints in place. Let \(\varrho _{\lambda k}\) be a 0–1 variable that is 1 if the permutation \(\lambda \) of \(\mathcal {Q}_{k}\) is selected, 0 otherwise. Finally, let \(k_{i}\) be the task coming after task \(k_{i-1}\) in \(\lambda \).

Then the formulation of the E-Routing Problem is as follows:

$$\begin{aligned}&\min \sum _{k_2 \in \mathcal {K}}\sum _{k_1 \in \mathcal {D_S}}\sum _{v\in \mathcal {V}}{\sigma _{vk_1 k_2}} + \sum _{k_1 \in \mathcal {K}}\sum _{k_2 \in \mathcal {D_C}}\sum _{v\in \mathcal {V}}{\sigma _{vk_1 k_2}}&\end{aligned}$$
$$\begin{aligned}&\sigma _{vkk} =0,&\forall k \in \mathcal {K}^\star , \ v\in \mathcal {V} \end{aligned}$$
$$\begin{aligned}&\sigma _{vk_1 k_2} = 0,&\forall k_1 \in \mathcal {K}, \ k_2 \in \mathcal {D_S}, \ v\in \mathcal {V} \end{aligned}$$
$$\begin{aligned}&\sigma _{vk_1 k_2} = 0,&\forall k_1 \in \mathcal {D_F}, \ k_2 \in \mathcal {K}, \ v\in \mathcal {V} \end{aligned}$$
$$\begin{aligned}&\sigma _{vk_1 k_2} = 0,&\forall k_1,k_2 \in \mathcal {K}^+\!, \ v\in \overline{\mathcal {V}}_{k_2} \end{aligned}$$
$$\begin{aligned}&\epsilon _{vk} \le \psi ,&\forall k \in \mathcal {K}^\star , v\in \mathcal {V} \end{aligned}$$
$$\begin{aligned}&\gamma _{k} \ge \gamma _{k'},&\forall k \in \mathcal {K}, \ k' \in \mathcal {Q}_{k} \end{aligned}$$
$$\begin{aligned}&l_{k} \le \gamma _{k} \wedge \gamma _{k} \le u_{k},&\forall k \in \mathcal {K}^+ \end{aligned}$$
$$\begin{aligned}&\gamma _{k_2} \ge \gamma _{k_1} + \tau _{k_1} + {|k_1k_2|}/{\Omega } - M(1-\sigma _{vk_1k_2}),&\forall k_1,k_2 \in \mathcal {K}^+, v\in \mathcal {V} \end{aligned}$$
$$\begin{aligned}&\gamma _{k_2} \ge \gamma _{k_1} + ( \psi - \epsilon _{vk}) / C + {|k_1k_2|}/{\Omega } - M (1- \sigma _{vk_1k_2}), \nonumber \\&\forall k_1 \in \mathcal {D_C}, k_2 \in \mathcal {K}^+, v\in \mathcal {V} \end{aligned}$$
$$\begin{aligned}&\epsilon _{vk_2} \le \epsilon _{vk_1} - |k_1k_2|/{\Omega } + M(1-\sigma _{vk_1k_2}),&\forall k_1 \in \mathcal {K}^+, k_2 \in \mathcal {K}^\star v\in \mathcal {V} \end{aligned}$$
$$\begin{aligned}&\epsilon _{vk_2} \le \psi - |k_1k_2|/{\Omega } + M(1-\sigma _{vk_1k_2}),&\forall k_1 \in \mathcal {D_C},k_2 \in \mathcal {K}^\star , v\in \mathcal {V} \end{aligned}$$
$$\begin{aligned}&\sum _{v\in \mathcal {V}}\sum _{k_2 \in \mathcal {K}}{\sigma _{vk_1k_2}} = 1,&\forall k_1 \in \mathcal {K}^\star , k_1 \ne k_2 \end{aligned}$$
$$\begin{aligned}&\sum _{v\in \mathcal {V}}\sum _{k_1 \in \mathcal {K}^\star }{\sigma _{vk_1k_2}} \le 1,&\forall k_2 \in \mathcal {D_C}, k_1 \ne k_2 \end{aligned}$$
$$\begin{aligned}&\sum _{k_2 \in \mathcal {K}}{\sigma _{vk_1k_2}} \le 1,&\forall k_1 \in \mathcal {D_S}, \ v\in \mathcal {V} \end{aligned}$$
$$\begin{aligned}&\sum _{k_1 \in \mathcal {K}}{\sigma _{vkk_1}} = \sum _{k_2 \in \mathcal {K}}{\sigma _{vk_2k}},&\forall k \in \mathcal {K}^\star , v\in \mathcal {V} \end{aligned}$$
$$\begin{aligned}&\sum _{k \in \mathcal {K}^\star }{\sigma _{vk_1k}} = \sum _{k \in \mathcal {K}^\star }{\sigma _{vkk_2}},&\forall k_1 \in \mathcal {D_S}, \ k_2 \in \mathcal {D_F}, \ v\in \mathcal {V} \end{aligned}$$
$$\begin{aligned}&\sum _{v\in \mathcal {V}}{\sigma _{vk_{n-1}^{'}k^{'}_n}} \ge \varrho _{\lambda k},&\forall k \in \mathcal {K}, \lambda \in \Lambda _k, k' \in \lambda \end{aligned}$$
$$\begin{aligned}&\sum _{\lambda \in \Lambda _k}{\varrho _{\lambda k}} = 1,&\forall k \in \mathcal {K} \end{aligned}$$
$$\begin{aligned}&\sum _{ \sigma _{vk_1k_2} \in \mathcal {R} ^*}{\sigma _{vk_1k_2}} \le \ |\mathcal {R} ^* |- 1,&\mathcal {R} ^* \in PR ^{*} \textsf { var} \end{aligned}$$

The cost function to minimize Eq. 7 is the total number of routes (this is done by minimizing the number of direct travels from the depots) plus the number of visits to the charging stations; Eq. 8 forbids to travel from and to the same location; Eqs. 9 and 10 express that a vehicle can never travel to the start, nor travel from the end: start and end referring to the same depot are physically located at the same node, but they play different roles in the Routing Problem, hence two different tasks; Eq. 11 expresses that there cannot be direct travel among mutual exclusive tasks. Equation 12 restricts the remaining operating range to be lower than or equal to the maximum operating range; Eq. 13 guarantees that precedence constraints among tasks are enforced. Equation 14 constrains the earliest and latest arrival time to the tasks locations; Eq. 15 model the case when a vehicle is travelling directly between two tasks’ locations and constrain the arrival time at the location of the second task based on its distance from the previous task’s location, and the speed \(\Omega \). Equation 16 models the case when a vehicle is travelling directly between a charging station and a task \(k \in \mathcal {K}^+\). This constraint accounts for the charging time spent at the charging station to restore full battery (the term \(( \psi - \epsilon _{vk}) / C \)), and the time required to travel to the next customer’s location; Eqs. 17 and 18 model the decrease of remaining operating range based on the distance travelled. Equation 19 expresses that each task’s location must be visited exactly once; Eq. 20 constrains the visit to charging stations, i.e. a vehicle can visit the location of each dummy task at most once; Eq. 21 restricts each vehicle to travel to at most one location from a depot. Since vehicles are only allowed to travel to/from their depot, and task locations can only be visited once, this constraint implies that a vehicle can execute at most one route before returning to the depot it was dispatched from; Eq. 22 guarantees the flow conservation between start and end; Eq. 23 ensures that the number of vehicles leaving the depots equals the number of vehicles returning to them. Since each vehicle can only visits its own depot, each vehicle will return to the depot it started from; Eqs. 24 and 25 state that some tasks have to be executed sequentially, when they represent pick-up and delivery of goods; Eq. 26 allows to rule out the previously computed sets of routes as a solution. This is necessary as this optimization sub-problem may be called multiple times during the execution of ComSat.

Fig. 2
figure 2

Flowchart of S-ComSat

Based on the model described above, the algorithm E-Router takes the set of current paths \( CP \), the set \( PR \) (from which \( PR ^*\) is computed), the set of tasks \(\mathcal {K}^\star \) and the set of vehicles \(\mathcal {V} \) and returns the current assignment \(\mathcal {A} \) containing the routes \(\mathcal {R} \) (computed from \(\mathcal {R} ^*\)), the vehicle executing each route \(R\in \mathcal {R} \), and the starting time \(\beta _R\) time of the route; if and only if the E-Routing Problem is infeasible, \(\mathcal {A} = \emptyset \).

If the Routing and the Assignment problems are merged, ComSat has one less step to execute to find a feasible solution to the CF-EVRP. We call this version of the compositional algorithm S-ComSat. Figure 2 shows how the E-Router is now the algorithm that takes care of solving the FRS. If it returns a non-empty \(\mathcal {A} \), this solution is checked against the capacity constraints by the \( CV \). The CFPS takes place as before and, if there is no set of paths that makes the Capacity Verification Problem feasible, then the E-Router is called again. S-ComSat terminates either when \( CV \) returns a feasible schedule or when E-Router is infeasible.

5 The conflict-free paths search

In this section the two sub-problems that form the CFPS are presented. The Capacity Verification Problem is modelled as a Job Shop Problem (JSP) (Manne 1960), in order to exploit the good performance of the SMT solver Z3 (Bjørner et al. 2015) in dealing with JSPs, as demonstrated in Roselli et al. (2018). The Paths Changing Problem formulation is inspired by Aloul et al. (2006).

5.1 The capacity verification problem

The Capacity Verification Problem aims to find a feasible schedule for the vehicles, where the routes that the vehicles are assigned to satisfy the capacity constraints on nodes and edges.

In this work the Capacity Verification Problem, as defined in Roselli et al. (2022), has been extended to account for pairs as well, since the information about conflicts must be related to a specific pair to define additional constraints in the \( PC \). The following example clarifies how the concept of pairs, introduced in Section 3 is used to formulate the Capacity Verification Problem.

5.1.1 Example of routes, pairs, nodes, and edges

Let \(n_e\) be the node visited before edge e, and let \(e_n\) be the edge visited before node n. Similarly, let \(n^e\) be the node visited after edge e, and let \(e^n\) be the edge visited after node n . Let \(p_R^0\) be the first pair of route R  and \(n^*_R\) be its starting node.

Let \(\mathcal {K} = \{k_1,\ldots ,k_7\}\), \(\mathcal {N}=\{n_1,\ldots ,n_{20}\}\), and a depot d. Let \(L_{k_1} = n_1 \) and \( L_{k_2} = n_7 \), and assume two routes are computed to execute all tasks: \(R_1 = \langle s_d, k_1, k_2, k_5, k_7, f_d \rangle , \ R_2 = \langle s_d,k_3, k_4, k_6,f_d \rangle \).

In order to clarify the notation introduced above, let us analyze \(R_1\). First, the set of pairs for \(R_1\) is defined as

\(\mathcal {P} _{R_1} = \{ \langle s_d,k_1 \rangle ,\langle k_1,k_2 \rangle ,\langle k_2,k_5 \rangle ,\langle k_5,k_7 \rangle ,\langle k_7,f_d \rangle \} \).

Then, let us assume that the path and edge sequence for pair \(\langle k_1,k_2 \rangle \) are the following:

\(\theta _{\langle k_1,k_2 \rangle } = \langle n_1,n_2,n_4,n_5,n_7 \rangle \),

\(\delta _{\langle k_1,k_2 \rangle } = \langle \langle n_1,n_2 \rangle ,\langle n_2,n_4 \rangle ,\langle n_4,n_5 \rangle ,\langle n_5,n_7 \rangle \rangle \).

Then \(p_{R_1}^0 = \langle k_1,k_2 \rangle \) and \(n^*_{R_1} = n_1\). Also, let \(e = \langle n_1,n_2 \rangle \), then \(n_e = n_1\), and \(n^e = n_2\); for \(n = n_1\), \(e^n = \langle n_1,n_2 \rangle \), and for \(n = n_2\), \(e_n = \langle n_1,n_2 \rangle \).

For a specific pair \(p = \langle k_1,k_2 \rangle \) of a specific route R, the first and last nodes \(n_i\) and \(n_j\) are the locations of a task \(k_1\) and \(k_2\) respectively. Then \(l_{k_1}\), the earliest arrival time of task \(k_1\), is equal to \(l_{n_i}\) and \(u_{k_1}\), the latest arrival time of task \(k_2\), is equal to\( u_{n_i}\). Similarly, \(l_{k_2} = l_{n_j}\), and \(u_{k_2} = u_{n_j}\). The same applies also to the service times of tasks \(k_1\) and \(k_2\), i.e., \( \tau _{k_1} = \tau _{n_i}\) and \(\tau _{k_2} = \tau _{n_j}\). For all the other nodes \(n \in p\), \(l_{n} = 0\), \(u_{n} = \textit{T} \) and \(\tau _{n} = 0 \). The same applies to every pair of every route. Finally, let \(\mu \) \(> 0\) be a small real constant used to prevent swapping of vehicles’ positions between a node and the previous or following edge.

The decision variables for the SMT model of the Capacity Verification Problem are:

  • \(x _{Rpn}\): non-negative real variable that models the time when a vehicle executing route \(R\) starts using node n in pair p;

  • \(y _{Rpe}\): non-negative real variable that models the time when a vehicle executing route \(R\) starts using edge e in pair p.

The model for the Capacity Verification Problem is:

$$\begin{aligned}&\ \ x _{Rp_R^0n^*_R} \ge \beta _R,&\forall R\in \mathcal {R} \end{aligned}$$
$$\begin{aligned}&\ \ y _{Rpe} \ge x _{Rpn_e} + \tau _{n_e},&\forall R\in \mathcal {R}, \ p \in \mathcal {P} _R, \ e \in \delta _p \end{aligned}$$
$$\begin{aligned}&\ \ x _{Rpn} = y _{Rpe_n} + \nicefrac {|e_n|}{\Omega },&\forall R\in \mathcal {R}, \ p \in \mathcal {P} _R, \ n \in \theta _p \end{aligned}$$
$$\begin{aligned}&\ \ l_n \le x _{Rpn} \wedge x _{Rpn} \le u_n,&\forall R\in \mathcal {R}, \ p \in \mathcal {P} _R, \ n \in \theta _p \end{aligned}$$
$$\begin{aligned}&x _{Rp n} \ge y _{R'p'e^n} + \mu \ \vee \ x _{R'p'n} \ge y _{Rp e^n} + \mu ,\nonumber \\&\qquad \qquad \qquad \forall R,R' \in \mathcal {R}, \ R\ne R', \ p \in \mathcal {P} _{R}, \ p' \in \mathcal {P} _{R'}, \ n \in \theta _{p} \cap \theta _{p'}, \ n \notin \mathcal {N}_H \end{aligned}$$
$$\begin{aligned}&y _{Rpe} \ge y _{R'p' e} + \mu \vee y _{R'p'e} \ge y _{Rp e} + \mu , \nonumber \\&\quad \qquad \qquad \qquad \qquad \qquad \forall R,R' \in \mathcal {R}, \, R\ne R', \ p \in \mathcal {P} _{R}, \ p' \in \mathcal {P} _{R'}, \ e \in \delta _{p_1} \cap \delta _{p_2} \end{aligned}$$
$$\begin{aligned}&y _{Rp e} \ge y _{R'p'e'} + \nicefrac {|e'|}{\Omega } \ \vee y _{R'p'e'} \ge y _{Rp e} + \nicefrac {|e|}{\Omega },\nonumber \\&\quad \qquad \qquad \qquad \qquad \qquad \qquad \qquad \qquad \quad \ \forall R,R' \in \mathcal {R}, \ R\ne R', \ p \in \mathcal {P} _{R}, \ p' \in \mathcal {P} _{R'},\nonumber \\&\quad \qquad \qquad \qquad \qquad \qquad \qquad \qquad \qquad \qquad \quad \ e \in \delta _{p}, \ e' \in \delta _{p'}, \ e = \bar{e}', \ g_{e} = g_{e'} = 1 \end{aligned}$$

Equation 27 constrains the start time of a route; Eqs. 28 and 29 define the precedence among nodes and edges to visit in a route; Eq. 30 enforces time windows on the nodes that correspond to the tasks; Eq. 31 prevents vehicles from using the same node at the same time, \(\mu \) is a small positive number to prevent swapping; Eqs. 32 and 33 constrain the transit of vehicles over the same edge. If two vehicles are using the same edge from the same node, one has to start at least \(\mu \) after the other, and if two vehicles are using the same edge from opposite nodes one has to fully transit before the other one can start.

Based on the model described above, the algorithm \( CV \) is defined, that takes a set of routes \(\mathcal {R} \), the start times in \(\beta _R, \forall R\in \mathcal {R} \), and the current set of paths \( CP \) as input and returns:

  • \( CFS \) is a list of triplets where each triplet contains the information of a vehicle (first element), a node (second element) and a time-step (third) element. Vehicles that are unscheduled vehicle have only one triplet, as they do not change location. In contrast, scheduled vehicles generate as many triplets as there are nodes in the paths forming the routes the vehicles travel. In each triplet, the third elements indicates when the vehicle reaches the node. The list is empty if, and only if, the problem is infeasible.

  • \(\,\bar{\!\mathcal {C}}\), the Unsat Core relative to constraints Eqs. 31-33 (see Section 5.3); this is empty if the problem is feasible.

5.2 Paths changing problem

In the Paths Changing Problem, alternative paths are computed to connect the consecutive tasks of each route. Finding alternative paths may be necessary when, for a given set of routes \(\mathcal {R} \) and starting times \(\beta \), no feasible schedule exists. The Capacity Verification Problem may be infeasible due to the current set of paths that connect the tasks’ locations, therefore a different set may lead to a feasible solution. A route is defined as a sequence of tasks, and for any two consecutive tasks there is a path (a sequence of edges) connecting them. Therefore, for a route containing \(i + 1\) tasks we will have i paths and for each path we can define a start and an end node, \(\xi _i\) and \(\pi _i\), respectively. The sets of outgoing and incoming edges for a certain node n are denoted \(\mathcal {O}_n\) and \(\mathcal {I}_n\), respectively.

The decision variables used to build the SMT model of the Paths Changing Problem are:

  • \(w _{Rpn}\): Boolean variable that represents whether the pair p of route \(R\) is using node n;

  • \(z _{Rpe}\): Boolean variable that represents whether the pair p of route \(R\) is using edge e;

This problem can be split into \( R\cdot i\) sub-problems (assuming all routes have \(i + 1\) tasks) that find paths for each route separately; simpler and smaller models are faster to solve. Unfortunately it may be necessary to explore different combinations of paths, so to retain the information we have only one model. Therefore, let the set of all variables \(z \) be \( \mathcal {Z} = \{ z _{Rpe} \mid R\in \mathcal {R}, p \in \mathcal {P} _R, e \in \mathcal {E}\} \) and let the optimal solution to the Path Changing Problem found at iteration h be \( CP ^* = \{z \in \mathcal {Z} \mid z \ \text {is True } \}\). Finally, let \( PP ^*\) be the set containing the optimal solutions found until the \((h -1)\)-th iteration. Note that it is possible to compute \( CP \) from \( CP ^*\) and vice versa. Subsequently, it is possible to compute \( PP \) from \( PP ^*\) and vice versa. The model is then:

$$\begin{aligned}&\min _{ R\in \mathcal {R}, \ p \in \mathcal {P} _R, \ e \in \mathcal {E}} \sum {\text {If}(z _{Rpe},|e|,0)} \end{aligned}$$
$$\begin{aligned}&w _{Rp\xi _p} \wedge w _{Rp\pi _p},&\forall p \in \mathcal {P} _R, \ R\in \mathcal {R} \end{aligned}$$
$$\begin{aligned}&\bigoplus _{e \in \mathcal {O}_{\xi _p} }{(z _{Rpe},1)},&\forall p \in \mathcal {P} _R, \ R\in \mathcal {R} \end{aligned}$$
$$\begin{aligned}&\bigoplus _{e \in \mathcal {I}_{\xi _p} }{(z _{Rpe},1)},&\forall p \in \mathcal {P} _R, \ R\in \mathcal {R} \end{aligned}$$
$$\begin{aligned}&z _{Rpe} \implies \lnot {z _{Rp\bar{e}}},&\forall p \in \mathcal {P} _R, \ R\in \mathcal {R}, \ e \in \mathcal {E}\end{aligned}$$
$$\begin{aligned}&\bigvee _{z _{Rpe} \in CP }{\lnot {z _{Rpe}}},&\forall CP \in PP ^* \end{aligned}$$
$$\begin{aligned}&\bigwedge _{\underset{n \ne \pi _p}{\underset{n \ne \xi _p,}{n \in \mathcal {N},}}}\text {If} \Biggl ( w _{Rpn},\bigoplus _{e \in \mathcal {O}_n}{(z _{Rpe}},1) \wedge \bigoplus _{e \in \mathcal {I}_n}{(z _{Rpe},1)},\bigoplus _{e \in \mathcal {O}_n}{(z _{Rpe},0)} \wedge \bigoplus _{e \in \mathcal {I}_n}{(z _{Rpe},0)} \Biggl ) , \nonumber \\&\forall p \in \mathcal {P} _R, \ R\in \mathcal {R} \end{aligned}$$

The cost function Eq. 34 to minimize is the cumulative length of the used edges; Eq. 35 guarantees that, for each path of each route, the start and end nodes are used; Eqs. 36 and 37 make sure that exactly one outgoing (incoming) edge is incident with the start (end) node of a route; Eq. 38 makes sure that a path is not allowed to use both an edge and its reverse; Eq. 39 rules out all the previously found solutions; finally, Eq. 40 guarantees that if a node (different from the start or end) is selected, exactly one of its outgoing and one of its incoming edges will be used. On the other hand, if a node is not used, none of its incident edges will be used. Based on the model described above the algorithm \( PC \) is defined, that takes the previous paths \( PP \) (from which \( PP ^*\) is computed) as input and returns a new set of paths \( NP \). If and only if the Paths Changing Problem is infeasible then \( NP =\emptyset \).

5.3 Exploiting the MUC

Experiments reported in Roselli et al. (2022) show that ComSat performs well for many problem instances, however, for some specific instances ComSat failed to find feasible solutions in reasonable time. Investigations revealed the \( PC \) to be the culprit. The reason is that the \( PC \) searches blindly through the possible paths that connect any two tasks, while minimizing the paths’ cumulative length. A conflict-free solution may involve paths that are longer than the current ones though, and the \( PC \) may have to explore many shorter solutions before finding the longer one. Improving the performance of the \( PC \) would be beneficial for the overall performance of ComSat, and letting the MUC guide the paths changing does indeed improve the search in many cases, even though not all.

When extracting the MUC, it is possible to track specific constraints. This feature can be exploited to focus only on the capacity constraints violations. In fact, since time windows and service time are not flexible, it is of little to no use to track constraints represented by Eqs. 27-30. Also, an infeasible formula \(\varphi \) may have multiple MUCs; in the CF-EVRP this means that conflicts may arise at different locations in the plant. In order to catch all of them, it is possible to iteratively relax the conflicting constraints from the initial formula and solve it again, until it becomes feasible. The formula will indeed become feasible eventually, since it is based on a feasible solution \(\mathcal {R} \) and only the capacity constraints can make it infeasible; in the worst case all such constraints will be removed during the iterations. Note that, since not all constraints are tracked, the set of constraints \(\,\bar{\!\mathcal {C}}\) returned is not an actual Unsat Core, since \(\,\bar{\!\mathcal {C}}\) would only make the problem infeasible in conjunction with the untracked constraints. Nonetheless, it provides the information about the conflicts needed to guide the search of paths.

Let \(\varphi _0\) be the conjunction of constraints Eqs. 27-33. Assume that \(\varphi _0\) is infeasible, and let \(\,\bar{\!\mathcal {C}}_0\) be the subset of a MUC retrieved by tracking constraints Eqs. 31-33. Then let \(\varphi _1 = \varphi _0 \setminus \,\bar{\!\mathcal {C}}_0\), also infeasible, and let \(\,\bar{\!\mathcal {C}}_1\) be the subset of a MUC retrieved by tracking constraints defined by Eqs. 31-33, not including the ones in \(\,\bar{\!\mathcal {C}}_0\). In general, the constraints in \(\,\bar{\!\mathcal {C}}_{i-1}\) can be iteratively relaxed to obtain a new formula \(\varphi _i\), until a feasible \(\varphi _n = \varphi _0 \setminus ( \,\bar{\!\mathcal {C}}_0 \cup \ldots \cup \,\bar{\!\mathcal {C}}_{n-1} )\) is found. Then \(\,\bar{\!\mathcal {C}} = \,\bar{\!\mathcal {C}}_0 \cup \ldots \cup \,\bar{\!\mathcal {C}}_{n-1} \) contains all the conflicts due to the capacity constraints.

Each constraint represented by Eqs. 31-33 is defined over two routes \(r_1\) and \(r_2\) and their pairs \(p_1\) and \(p_2\) for a specific node/edge; therefore, based on the constraints in \(\,\bar{\!\mathcal {C}}\), it is possible to identify a sequence of nodes \(\theta _{p_1}^c \subseteq \theta _{p_1} \) and/or edges \(\delta _{p_1}^c \subseteq \delta _{p_1} \) where the conflict took place. If the conflict was generated by a set of constraints from Eq. 31, then the following constraint is added to Eqs. 34-39:

$$\begin{aligned}&\lnot \left( \bigwedge _{n \in \theta _{p_1^c}}{ w _{r_1p_1n}}\right) \vee \lnot \left( \bigwedge _{n \in \theta _{p_2^c}}{ w _{r_2p_2n}}\right) . \end{aligned}$$

On the other hand, if the conflict was caused by a set of constraints from Eq. 32 or Eq. 33, the following constraint is added to Eqs. 34-39:

$$\begin{aligned}&\lnot \left( \bigwedge _{e \in \delta _{p_1^c}}{ z _{r_1p_1e}}\right) \vee \lnot \left( \bigwedge _{e \in y _{p_2^c}}{ z _{r_2p_2e}}\right) . \end{aligned}$$

Constraints Eqs. 41 and 42 force at least one of the routes involved in the conflict to avoid at least one of the nodes (edges, respectively) that was involved in such conflict when computing new paths. The constraint is formulated so that the choice of the route to change is left to the solver, including the possibility of changing both routes; since the problem is an optimization, the solver will choose the change that leads to the shortest cumulative paths length.

Note that in our previous work (Roselli et al. 2022) constraints Eqs. 41 and 42 were slightly different, forbidding all conflicting nodes and edges to be used by both routes. While this would be a stronger restriction and could be more effective to find feasible paths, it can only work in some situations, e.g., when it is possible to completely change the path from one task to another. For this reason we generalized the constraints so that they work for any instance of the CF-EVRP.

Based on the model described by Eqs. 34-42, the function MUC-Guided-Paths-Changer (\( GPC \)) is defined, that takes the previous paths \( PP \) and \(\,\bar{\!\mathcal {C}}\) as input and returns a new set of paths \( NP \). If and only if the Paths Changing Problem is infeasible \( NP =\emptyset \).

Since the constraints added to the \( GPC \) are based on the constraints in \(\,\bar{\!\mathcal {C}}\), it is imperative that the Unsat Core returned when the \( CV \) is infeasible is minimal. This is so because if the Unsat Core is not minimal, it could contain constraints that are not actually causing capacity conflicts. These constraints would in turn lead to defining constraints Eqs. 41 and 42 in the \( GPC \) that may remove feasible solutions.

Figure 3 summarizes the steps required to find a conflict-free schedule \( CFS \), if such exists, using the improved paths searching algorithm \( GPC \). As mentioned, it is assumed that routes \(\mathcal {R} \) and their start times \(\beta _r, \ \forall r \in \mathcal {R} \) have already been computed. The shortest paths between any two tasks are computed using Dijkstra’s algorithm and then set as the current paths \( CP \). Also, \( CP \) are added to the list of previous paths \( PP \).

Fig. 3
figure 3

Flowchart of the MUC-Guided-CFPS

Then the \( CV \) will check such routes against the capacity constraints; if this sub-problem has a feasible solution the algorithm terminates and a conflict-free schedule is returned. Otherwise \(\,\bar{\!\mathcal {C}}\) is extracted as described in the previous paragraph and the \( GPC \) algorithm is invoked. \( GPC \) will use the information about previously computed paths \( PP \) and the information about conflicts from \(\,\bar{\!\mathcal {C}}\) to compute new paths \( NP \), which will be set as the current paths and stored in \( PP \). At this point the \( CV \) is run again using the new paths. The iterations between the two algorithms continue until either the \( CV \) is feasible, or the \( GPC \) is infeasible, i.e., there are no feasible, conflict-free paths to execute the routes \(\mathcal {R} \).

6 Proof of soundness and completeness

In this section, we provide proof of soundness and completeness of \(ComSat _2\) and S-ComSat. Since soundness and completeness of the previous version of ComSat has already been proven in Roselli et al. (2022), soundness and completeness of the new versions can be proven by showing that:

  • in the FRS, E-Router is able to enumerate all possible assignments that would originate from calling Router and Assign;

  • in the CFPS, \( GPC \) can enumerate at least as many feasible solutions of the Paths Changing Problem as \( PC \).

Observation 1

All the problem classes presented in this work are decidable. This is true because they are all combinations of decidable first-order theories and therefore the Nelson-Oppen theory combination method (Tinelli and Harandi 1996) applies. In fact the Routing and E-Routing problems are a combination of linear arithmetic and propositional logic, the Assignment Problem and the Capacity Verification Problem all fall into the category of difference logic (a fragment of linear arithmetic), and the Paths Changing Problem is a propositional logic problem.

Observation 2

The optimization problems solved in ComSat, i.e., the Routing and E-Routing problems, and the Paths Changing Problem, are bounded. The Routing and E-Routing problems involve a finite number of decision variables that are either integers with a finite domain (\(\{0,1\}\)), or non-negative reals and the objective is minimization, with an objective function involving only non-negative coefficients. The Paths Changing Problem involves only a finite number of Boolean variables, so the problem domain is finite.

6.1 Soundness and completeness of the FRS

In order to prove soundness and completeness of S-ComSat we are going to show that E-Router can compute the same solutions to the FRS as the combination of Routing and Assignment problems.

Lemma 1

The FRS has a finite number of feasible solutions.


By the definition of route given in Section 3, a route contains an ordered set of unique tasks from \(\mathcal {K}\). Also, a route must start and end at the same depot, and may include up to \(|\mathcal {K} |\) dummy tasks representing charging events at the depots. If we do not consider \(\mathcal {D}\), a set of routes \(\mathcal {R} \) to execute all tasks in \(\mathcal {K}\) is a partition of \(\mathcal {K}\). The number of partitions of the set \(\mathcal {K}\) is the Bell Number (Comtet 1974), \(B_{|\mathcal {K} |} < \infty \). Given \(|\mathcal {D}|\) depots, in the worst case, there may be \(2^{|\mathcal {D}|}\) combinations of depots and partitions of \(\mathcal {K}\). Also, for each of these combinations, there can be a any number of extra visits, between 0 and \(|\mathcal {K} |\), to the depots for charging. Finally, for each solution to the Routing Problem, there can be as much as \(2^{|\mathcal {V} |}\) assignments to vehicles. Therefore, there may be \(B_{|\mathcal {K} |} \cdot 2^{|\mathcal {D}|} \cdot |\mathcal {D}|\cdot 2^{|\mathcal {V} |} < \infty \) solutions to a FRS with \(|\mathcal {K} |\) tasks, \(|\mathcal {V} |\) vehicles, and \(|\mathcal {D}|\) depots, assuming that for each depot there can be \(|\mathcal {K} |\) charging stations.\(\square \)

Lemma 2

Repeated calls to E-Router will enumerate all feasible solutions before returning infeasible.


Let \(\varphi _0\) be a conjunction of the constraints Eqs. 7-26, and let \(\mathcal {A} _0\) be a solution to \(\varphi _0\). Then, if another solution \(\mathcal {A} _1\) for \(\varphi _0\) exists, it can be found by solving \(\varphi _0 \wedge \lnot {\mathcal {A} _0} = \varphi _1\). In general, the n-th solution can be found by solving \(\varphi _0 \wedge \lnot {\mathcal {A} _0}\wedge \ldots \wedge \lnot {\mathcal {A} _{n-1}} = \varphi _{n}\). Because of Lemma 1, we know that the number of solutions to \(\varphi \), i.e., \(\mathbb {S}(\varphi ) < \infty \) and we enumerate all by solving \(\varphi _0,\,\ldots ,\,\varphi _{\mathbb {S}(\varphi )-1}\).\(\square \)

Observation 3

Following the same reasoning used in Lemma 2, Routing and Assign together can enumerate all feasible solutions to the FRS before returning infeasible, since the number of route sets \(\mathcal {R} \) to serve a set of tasks \(\mathcal {K}\) is finite (Lemma 1) and so is the number of vehicles that can be assigned to them.

One important difference between the Routing and E-Routing problem is constraint Eq. 21 that allows at most one route to be assigned to a vehicle. This so since in the E-Routing Problem vehicles are allowed to go back to the depot to recharge, while in the Routing Problem vehicles can be assigned multiple routes and the battery recharge is performed in between routes and handled by Assign. On the other hand, in the Routing Problem a route’s length cannot exceed the operating range of the vehicle, since charging is not included. For instance, let us assume ComSat is solving an instance of the CF-EVRP; it calls Router which would compute the routes \( R_1 = \langle s_d, k_1,\ldots ,k_i, f_d \rangle , \ \text {and} \ R_2 = \langle s_d, k_{i+1},\ldots ,k_j,f_d \rangle \) both only executable by vehicle v, available in the fleet. Now let us assume that the time windows of tasks \(k_1,\ldots ,k_j\) are all \( [ 0, \textit{T} ] \) and T is large enough so that Assign is able to assign vehicle v to both routes. If the same instance of the CF-EVRP was to be solved by S-ComSat, a call to E-Router may compute the route \( R_1 = \langle s_d, k_1,\ldots ,k_i, z_d, k_{i+1},\ldots ,k_j,f_d \rangle \) and directly assign \(R_1\) to vehicle v. Therefore Router + Assign can compute the same solution that E-Router would compute and vice versa. Let us now generalize this concept to any solution.

Lemma 3

Router + Assign can compute any solution that E-Router would compute.


Trivially, if tasks can be served without having to recharge, Router computes the same routes as E-Router. Otherwise, an assignment computed by E-Router may look like \((v, R_1)\) with \(R_1 = \langle s_d, k_1,\ldots ,k_i, z_d, k_{i+1},\ldots ,k_j,f_d \rangle \) with a length exceeding the operating range \( \psi \), since charging can be performed at the depot d. The route can be divided into as many routes as charging tasks included in the route, i.e., \( R_2 = \langle s_d, k_1,\ldots ,k_i, f_d \rangle , \ \text {and} \ R_3 = \langle s_d, k_{i+1},\ldots ,k_j,f_d \rangle \). The resulting routes are disjoint subsets of \(\mathcal {K}\) of length shorter than the vehicle’s operating range. Hence they can form a solution computed by Router. Moreover, since \(R_1\) is computed by E-Router, \(R_2\) and \(R_3\) can be assigned to one vehicle, namely v; hence \((v,R_2) \wedge (v,R_3)\) can form a solution computed by Assign.\(\square \)

Lemma 4

E-Router can compute any solution that Router + Assign would compute.


Trivially, if tasks can be served without having to recharge, E-Router computes the same assignment as Assign to the routes computed by Router. Otherwise, if two routes starting at the same depot d are assigned to the same vehicle v, they look like \( R_2 = \langle s_d, k_1,\ldots ,k_i, f_d \rangle , \ \text {and} \ R_3 = \langle s_d, k_{i+1},\ldots ,k_j,f_d \rangle \). Since the length of each of these is at most as long as the operating range of vehicle v, they could be merged into a single route \(R_1 = \langle s_d, k_1,\ldots ,k_i, z_d, k_{i+1},\ldots ,k_j,f_d \rangle \). This can be true for an arbitrary number of routes that are assigned the same vehicle. Moreover, since they were both assigned the same vehicle, it means that v has enough time to recharge its battery in between the execution of the routes. Therefore \((v, R_1) = \langle s_d, k_1,\ldots ,k_i, z_d, k_{i+1},\ldots ,k_j,f_d \rangle ) \) can be computed by E-Router.\(\square \)

Theorem 1

S-ComSat in Fig. 2 is sound and complete.


Because of Lemmas 3 and 4, any solution to the FRS computed by one method, can be computed by the other. Thus, since ComSat is sound and complete (Roselli et al. 2022), so is S-ComSat.\(\square \)

6.2 Soundness and completeness of \(ComSat _2\)

The proof for soundness and completeness of the CFPS builds on the following. There exists a finite number of solutions to the Paths Changing Problem; the \( GPC \) can enumerate at least all feasible solutions to the Paths Changing Problem; if a solution that satisfies the Capacity Constraints does exist, the \( GPC \) will eventually find it, otherwise it will declare the problem infeasible.

Let \(\mathcal {S} \) be the set of possible solutions to a Paths Changing Problem; divide \(\mathcal {S} \) into the set of conflict-free solutions \(\mathcal {F} \) and the set of conflicting solutions \(\mathcal {U} \). In other words, a solution to the Paths Changing Problem from \(\mathcal {F} \) will make the Capacity Verification Problem feasible, while a solution from \(\mathcal {U} \) will not. If the CFPS is infeasible, then \(\mathcal {S} = \mathcal {U} \) and \(\mathcal {F} = \emptyset \).

In case the CFPS is feasible, though, in order to prove completeness it is necessary to guarantee that at least all feasible solutions \(\mathcal {F} \) can be found by \( GPC \). This is proven for the \( PC \), since each call of the \( PC \) function will find the next optimal solution to the Paths Changing Problem, whether it belongs to \(\mathcal {F} \) or not, until all solutions have been enumerated. However, in the \( GPC \) there are additional constraints that may remove feasible solutions. In the proof below it is shown that such additional constraints only remove infeasible solutions.

Observation 4

Given a finite, directed, weighted graph, the number of paths that connect two arbitrary nodes is finite.


By definition, a path is an ordered set of nodes such that no node appears more than once. If the number of nodes in the graph is finite, there cannot be an infinite number of paths.\(\square \)

Lemma 5

For a given set of routes \(\mathcal {R} \) and start times in \(\beta \), repeated calls to the \( PC \) function will enumerate all feasible solutions to the Paths Changing Problem, either belonging to \(\mathcal {F} \) or \(\mathcal {U} \), before returning infeasible.


Let \(\varphi _0\) be the conjunction of constraints Eqs. 35-40, a relaxation of the Paths Changing Problem, and let \( CP _0\) be a solution to \(\varphi _0\). Then, if another solution \( CP _1\) for \(\varphi _0\) exists, it can be found by solving \(\varphi _0 \wedge \lnot { CP _0} = \varphi _1\). In general, the n-th solution can be found by solving \(\varphi _0 \wedge \lnot { CP _0}\wedge \ldots \wedge \lnot { CP _{n-1}} = \varphi _{n}\). Because of Lemma 4, we know that the number of solutions to the Paths Changing Problem, \(\mid \!\mathcal {S} \!\mid \), is finite and we can enumerate them all by solving \(\varphi _0,\,\ldots ,\,\varphi _{\mid \mathcal {S} \mid -1}\).\(\square \)

Lemma 6

Using the \( PC \) and \( CV \) is a sound and complete procedure to solve the CFPS.


Because of Observation 4 we know there is a finite number of solutions to the Paths Changing Problem, and because of Lemma 5 we know that the \( PC \) function can enumerate them all. If a solution that belongs to \(\mathcal {F} \) exists, the \( PC \) will find it, otherwise it will return all solutions belonging to \(\mathcal {U} \); the \( CV \) will then check whether they are conflict-free. Therefore, using the \( PC \) and \( CV \) in combination will correctly solve the CFPS.\(\square \)

Lemma 7

For a given set of routes \(\mathcal {R} \), the \( GPC \) is able to find at least all solutions in \(\mathcal {F} \).


For each set of current paths \( CP \), \(\,\bar{\!\mathcal {C}}\) only contains constraints defined by Eqs. 31, 32, and 33. The constraints in \(\,\bar{\!\mathcal {C}}\) are iteratively retrieved from Unsat Cores that are minimal and thus represent combinations of nodes and edges that are actually involved in conflicts. Since the constraints defined by Eqs. 41 and 42 address the constraints from \(\,\bar{\!\mathcal {C}}\), Eqs. 41 and 42 only define constraints over nodes or edges that cause conflicts. Hence these constraints only remove solutions of the Paths Changing Problem that belong to \(\mathcal {U} \).\(\square \)

Theorem 2

Using the \( GPC \) and \( CV \) is a sound and complete procedure to solve the CFPS.


The \( PC \) and the \( GPC \) are identical, except for constraints Eqs. 41-42, and because of Lemma 7, we know that the addition of these constraints only removes solutions from \(\mathcal {U} \). Thus, since the CFPS using the \( PC \) is sound and complete (Lemma 6), so is the CFPS using the \( GPC \).\(\square \)

7 Experiments

To evaluate the efficacy of \(ComSat _2\) and S-ComSat, as well as to compare their performance with \(ComSat _1\), problem instances are designed and used for testing. The algorithms called by ComSat used the SMT solver Z3 4.11.2 and the MILP solver (Gurobi 2023) 9.1.2 to compute solutions to the sub-problems the CF-EVRP is decomposed into. All the experimentsFootnote 2 were performed on an Intel Core i7 6700K, 4.0 GHZ, 32GB RAM running Ubuntu-18.04 LTS.

Table 1 Comparison of \(ComSat _1\), \(ComSat _2\), and S-ComSat over a set of benchmark instances of the CF-EVRP

The first set of experiments is run over one of the benchmark sets of the CF-EVRP presented in Roselli et al. (2022). Instances are characterized by the following parameters:

  • NVK indicates the number of nodes N in the graph that represents the plant layout, the number of vehicles V, and the number of tasks K;

  • Nodes Connection refers to the connectivity of the graph. The graph in the instances is grid-like (e.g. for \(N=15\) the graph is a \(3\times 5\) grid) and the value of Node Connection 100 means that all edges needed to form a grid (in both directions, e.g. from Node 1 to Node 2 and from Node 2 to Node 1) are present. For a Node Connection value of 90, ten percent of the edges are removed from the original graph, and for a Node Connection value of 80, twenty percent of edges are removed. In both cases edges are removed so that the graph remains connected;

  • the time horizon T;

  • a Seed connected to the random generation of parameters such as tasks location, time windows, charging coefficients and operating range.

In this test, the overall running time for termination is compared for \(ComSat _1\), \(ComSat _2\), and S-ComSat. In the evaluation, the number of calls of Router and E-Router is also considered. Repeated calls to Router might be necessary for two reasons; either the Assignment Problem is infeasible, or the Capacity Verification Problem is. Conversly, when repeated calls of E-Router are made, it is solely because the previous ones rendered the Capacity Verification Problem infeasible. The maximum number of routes is capped at 200; if a solution is not found or infeasibility is not confirmed by the 200th call of Router (or E-Router), the instance is declared unknown. A total of 180 instances is generated combining NVK values of 15-3-10 and 25-4-14, Nodes Connection values of 100, 90, and 80, and T values of 20, 25, 30, 40, 50, and 60. For each combination of these values, five instances are generated using different seeds. Table 1 shows, for each combination of parameters, calculated over the five instances with different seeds, the number of them that where feasible (or infeasible, respectively), the average solving time in seconds for both the feasible and the infeasible instances, and the average number of Router (or E-Router) calls for both the feasible and the infeasible instances. It is important to note that the average values for solving time and the number of calls to Router (or E-Router) are calculated only over the instances that were actually solved, not the unknown ones.

The results show that, for each category of instances, \(ComSat _2\) is able to solve more instances than \(ComSat _1\). For the infeasible instances, this often leads to a higher number of R.Calls and a longer average solving time for \(ComSat _2\), since the harder instances are simply not solved by \(ComSat _1\) and therefore not counted in. On the other hand, \(ComSat _1\) and \(ComSat _2\) can solve roughly the same number of feasible instances, but \(ComSat _1\) requires a larger number of R.Calls and, therefore, a longer solving time. It is also possible to see a correlation between the solving time and the number of R.Calls for both \(ComSat _1\) and \(ComSat _2\). The time required for a single iteration of \(ComSat _2\) is generally longer compared to \(ComSat _1\), but since the number iterations required is smaller, its overall performance is better.

On the other hand, S-ComSat could solve all instances from all categories except five in less than one second. The categories in which the overall execution took longer than one second are the ones that required more than one call of E-Router. Hence, merging the Routing and Assignment problems into one and solving this problem using E-Router is the preferred choice in terms of solving time. Moreover, it is interesting to note that the computation time saved is not only due to a limited number of calls of E-Router compared to Router. In fact, even when comparing the time required for one call, E-Router’s solving time is in the same order of magnitude as Router.

Table 2 Comparison of \(ComSat _1\), \(ComSat _2\), and S-ComSat average solving time (in seconds) and number of instances solved, after sorting the instances according to their feasiblity

Overall, \(ComSat _1\) is able to solve 109 instances, with an average time of about 1 second for the feasible instances, 0.1 second for the infeasible ones, and 46 seconds for the unknown ones; on the other hand, \(ComSat _2\) is able to solve 168 instances, taking on average about half a second for the feasible instances, 1.78 seconds for the infeasible ones, and about 350 seconds for the unknown ones; finally S-ComSat is able to solve all 180 instances, taking about 0.3 seconds for the feasible ones and 0.1 seconds for the infeasible ones. Table 2 shows the results discussed in this paragraph. Note that the geometric mean is used instead of the arithmetic mean in order to get rid of biases due to outliers.

Aside for the previously described benchmark sets, we have also tested S-ComSat on randomly generated, increasingly larger instances, in order to evaluate its scalability. The largest generated instance had NVK equal to 200-30-50. Of course, different instances of the same size may require different solving time, but the solving time of the sub-problems can still provide insights on scalability. Of the three sub-problems, E-Router is the least sensitive to the increase in the instance size, while the CapacityVerifier and especially the Paths-Changer become significantly slower as the problem sizes grow. Already for problems with NVK equal to 100-15-25 a Paths-Changer call can take over a minute. Above NVK 150-20-30 also a single call of CapacityVerifier takes on average one minute, and for the largest instances even the solving time of E-Router is measured in minutes. Overall, if S-ComSat does not require several iterations through the sub-problems to find a solution, it can solve instances with hundreds of nodes and tens of vehicles and tasks within a few minutes, at most. When iterations are required, the Paths-Changer remains the bottleneck of the algorithm, especially when the problem instance involves a large graph.

Table 3 Comparison of the \( PC \) and \( GPC \) over a set of instances of the CF-EVRP

In the second set of experiments, the goal is to compare the search for alternative paths, therefore additional instances are designed in such a way that there is only one feasible set of routes \(\mathcal {R} \) to execute the tasks and the shortest paths to execute the routes cause infeasibility of the Capacity Verification Problem.

Table 3 shows the results of the evaluation of five problem instances of the CF-EVRP solved using \(ComSat _1\). Each instance was solved three times, once using the \( PC \), once using the \( GPC \) as defined in Roselli et al. (2022), henceforth called \( GPC ^*\), and once using the \( GPC \) as described in this work; in each case, the number of iterations and the time (in seconds) required to find a feasible solution is reported. The problem instances presented are increasingly hard to solve, in terms of plant size (represented by the number of nodes), number of routes, and number of tasks in each route. The tasks’ locations and time windows are set so that conflicts will arise due to the capacity constraints when the shortest paths are used and a search for alternative paths will be necessary in order to find a conflict-free schedule.

Results from Roselli et al. (2022) differ from the results of Table 3 in terms of running time for the \( GPC ^*\) and both running time and the number of iterations for the \( PC \). We believe this to be related to using a different version of Z3 (4.8.9 in Roselli et al. (2022)) since the implementation and computer used in both cases are the same. For this reason, in order to make a fair comparison, we solved all instances again.

When solving instances 1 through 4 \( GPC \) takes less time and fewer iterations than \( PC \). However, for instance 5, \( PC \) can find a solution in only five iterations and 69 seconds, while \( GPC \) takes 23 iterations and 353 seconds. \( GPC ^*\) is able to solve all instances in only one iteration. Moreover, it seems that the additional constraints in \( GPC ^*\) slow down the search for a solution to the Paths Changing Problem, whereas for \( GPC \) there is no noticeable difference in the time required for one iteration, compared to \( PC \).

These results do not come unexpectedly. \( GPC ^*\) has strong restrictions on nodes and edges that routes can use since each node/edge involved in a conflict must be avoided by one of the two routes that use it. Hence it may be hard to find a feasible solution and it takes a longer time to do so. On the other hand, \( GPC \) does not have such strong restrictions, and the solving time for one iteration is barely affected by the additional constraints when compared to \( PC \). In turn, because of the less restrictive constraints, it takes more iterations to find feasible solutions. Instance 5 shows that \( GPC \) may fail to reduce the number of iterations compared to \( PC \).

On the other hand, \( GPC \) is guaranteed to find a feasible solution, if such exists, for any instance of the CF-EVRP, while \( GPC ^*\) only works if it is possible to avoid all edges/nodes involved in a conflict. In general, \( GPC ^*\) might be useful for graphs with high connectivity, where a different path can likely be found; unfortunately, determining beforehand whether a graph is suitable for \( GPC ^*\) might be a hard problem itself.

8 Conclusion

In this paper, we presented improvements on the compositional algorithm ComSat, first presented in Roselli et al. (2022). The algorithm is designed to solve instances of the CF-EVRP (Roselli et al. 2021), a vehicle routing problem involving capacity constraints on the road segments and vehicles’ limited operating range. Our previous work on the topic highlighted bottlenecks in ComSat that, for some problem instances, led to relatively long solving time. These bottlenecks are the Feasible Routes Search (FRS), performed through iterations between the algorithms Router and Assign within ComSat, and the Conflict-Free Paths Search (CFPS), performed through iterations between the \( PC \) and \( CV \) algorithms.

For the FRS, we introduced a new version of Router including a tighter constraint to rule out previous solutions, as well as a new algorithm that deals with routing and assignment, i.e., FRS, simultaneously. For the CFPS, we presented new versions of \( PC \) and \( CV \) that make use of the Unsat Cores to guide the search for feasible paths. We proved soundness and completeness of the different versions of ComSat originating from replacing the existing algorithms with the ones presented in this work.

We also compared the performance of the proposed algorithms that form part of ComSat, to the previous ones presented in Roselli et al. (2022). For the FRS, both \(ComSat _2\) and S-ComSat outperformed \(ComSat _1\), with S-ComSat being the fastest, sometimes by more than one order of magnitude. For the CFPS, the \( GPC \) needed fewer iterations and less time than the \( PC \) to find conflict-free paths in four cases out of five. In comparison, the previous version of the \( GPC \) presented in Roselli et al. (2022) shows a stronger impact on reducing the number of iterations, although it is not always guaranteed to return feasible solutions, if such exist. In conclusion, this method for the CFPS shows potential, but further investigation is needed to assess its actual effectiveness.