Encyclopedia of Systems and Control

Living Edition
| Editors: John Baillieul, Tariq Samad

Scheduling of Batch Plants

  • John M. WassickEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4471-5102-9_254-1

Abstract

For manufacturers operating batch plants, production scheduling is a critical and challenging problem. A thorough understanding of the problem and the variety of solutions approaches is needed to achieve a successful application. This entry will present a brief overview of batch operations and the state of the art of batch plant scheduling for nonexperts in the field.

Keywords

Process networks Production sequencing Product wheel Dispatching rules Optimization 

Introduction

Batch plants, manufacturing operations composed of unit operations that operate in batch mode, are the primary manufacturing operations for the production of high margin products such as pharmaceuticals, specialty chemicals, and advanced materials. The scheduling of the sequence of operations over time has a significant impact on the overall performance of a batch plant (White 1989). The economic importance of batch plants, and the importance of scheduling for batch plants, has spawned a large body of research on the topic and a variety of commercial offerings.

The Nature of Batch Plants

In batch operations, the material transformation takes place in stages and the operation of each stage occurs over a specified time while the material remains in a particular unit operation performing that stage of production. (A familiar batch operation is baking a cake. Ingredients and their amounts, specified by a recipe, are combined and then subjected to a constant temperature over specified period of time to produce a cake.) A batch plant may have parallel units for some stages. Other stages may be operated in a continuous flow mode with a storage unit feeding the stage and another storage unit receiving the stage output. The path through the unit operations may be product dependent. Batch plants have highly diverse operational characteristics.

There are two broad categories of batch processes: (1) sequential where a batch moves from one stage to another without losing its identity and (2) networked where batches can be combined or split to feed downstream units (Mendez et al. 2006). Sequential processes can be further classified as single stage, multi-stage, or multi-purpose.

The nature of a batch process and the different process structures can be explored by referring to the process depicted in Fig. 1 (Chu et al. 2013). As drawn, this batch plant operates as a multi-stage sequential process where a batch starts in raw material preparation stage (selected raw materials are loaded and then blended for a specified time), moves to the reaction stage with two parallel units (prepared raw materials plus additives react at a constant temperature for a specified period of time), moves to the finishing stage (intermediate product is subjected to a vacuum for a specified period of time to remove volatile by-products), and finally is processed in the drumming stage (finished product is packaged in drums). If finished product storage tanks were placed between finishing and drumming to allow the drumming stage to be scheduled independently of the first three stages, then the drumming operation would represent a single stage sequential process. If we further assume that for some finished products Reactor 1 produces a batch of precursor for Reactor 2 and that some products produced in the reactors bypass the finishing stage and go directly to drumming, then the underlying plant would be a multi-purpose sequential process. Finally, if intermediate storage tanks exist for storing multiple batches of the precursors produced by Reactor 1 and the contents of the tanks are drawn off to produce multiple, subsequent batches in both reactors then the underlying plant is a networked process.
Fig. 1

Example batch plant (Solid lines represent material flows from limited inventory. Dashed lines represent material flow from unlimited inventory)

Besides the general structure of a batch plant, the specific processing requirements, resources needs, and process constraints have significant impact on the complexity of the scheduling problem. One important aspect is limited resources that are shared between different operations. The availability and capacity of shared resources place a severe constraint on the timing of competing operations. Another significant factor is intermediate storage between stages and the inventory policies that are enforced. Like shared resources, intermediate storage places hard constraints on the timing of upstream and downstream stages, especially when no storage is available. A third important constraint on scheduling is product transition policies that dictate what operations need to be performed to move from one product to another in a given stage. Such operations, sometimes called setups, might involve cleaning, or producing buffer batches to isolate the chemistry of one product from another. These operations involve costs and subtract from the productive use of the equipment so they have significant impact on the sequencing of products through the plant.

Production Scheduling of Batch Plants

Production scheduling in a batch plant involves three fundamental decisions: (1) determining the size of each batch in each stage, (2) assigning a batch to a processing unit in each stage, and (3) determining the sequence and timing of processing on each unit. These decisions are well illustrated by a graphical planning board or Gantt chart as shown in Fig. 2 (Chu et al. 2013). Personnel charged with creating and managing production schedules often rely on such a graphical tool to construct, analyze and report the schedule. Generally production schedules are determined using the information listed in Table 1.
Fig. 2

Gantt chart of a production schedule

Table 1

Information generally used to construct a production schedule

Scheduling information

Examples

 

Detailed production recipes

Batch times, processing rates, unit ratios, sequence dependencies

 

Equipment data

Capacities, availabilities, product suitability

 

Facility information

Shared resource availability and capacities, storage capacities

 

Production costs

Raw materials, utilities, setups, cleanings, manpower

 

Production targets

Inventory replenishments, customer orders with due dates

 

Current process status

Current inventories, operations in progress, schedule items fixed in future time

 

The scope of the scheduling decisions is defined by the level of process detail considered in the scheduling problem. This idea can be examined by referring to Figs. 1 and 2. Such a Gantt chart could apply to a batch plant with four stages of production: raw material preparation, reaction, finishing, and drumming, with two parallel reactors in the reaction stage. If dedicated finished product storage exists with large enough capacity to cover the process lead time then one schedule could be confined to the first three stages of production and a different schedule applied to the drumming stage. The scope of the scheduling problem could be further reduced if raw material preparation only takes place just in time to load a reactor rather than execute as soon as possible. In this situation, the time for raw material preparation could be added to the reactor batch time and the schedule would involve only the reactors and the finishing system with the raw material unit or units schedule implied by the reactor schedule. At a higher level still, the first three stages of production could be considered a production train and scheduling could then be reduced to planning campaigns of batches for each product over time with the detailed synchronization of the individual stages left to operations personnel. Obviously with each level of abstraction some efficiency in the schedule is lost and subsequently the opportunity to increase throughput of the plant.

In most batch plants a person with a title such as “production scheduler” is charged with the scheduling decisions. In general, the production scheduler is responsible for delivering a production schedule that meets customer orders on time and maintains finished product inventory while dealing with rush orders, late deliveries, equipment breakdowns and other contingencies. Generally schedulers develop and publish a schedule to manufacturing on a regular basis (e.g., every 2 days, once a week, etc.) and then monitor ongoing circumstances (e.g., actual production vs. plan, new demand, etc.) to determine if minor adjustments to the schedule are needed or if a complete new schedule needs to be published. The construction of a schedule can be an iterative process involving negotiations with manufacturing, supply chain, sales, maintenance and logistics. The tools available to the production scheduler can have a significant impact on the quality of schedules they produce.

It is evident from the description above that production scheduling of batch plants is really carried out as an exercise in rescheduling in response to disturbances identified through feedback from the process and market. Under these circumstances production scheduling serves as a form of high level feedback control of the process. In this regard the manipulated variables are the production amounts for each product and the controlled variables are the inventory levels and customer service levels for each product. A scheduling problem can be converted to a state-space formulation and compared to model predictive control (Subramanian et al. 2012).

Solution Approaches

The solution approaches applied to scheduling batch plants cover a wide spectrum of sophistication. A very simple form is nothing more than a sequence of batches maintained on a white board in the plant control room. A level above this would be the use of custom spreadsheets for arranging batches chronologically and computing finished product inventory. Another step up is the use of a manually manipulated Gantt chart as illustrated in Fig. 2, possibly pre-populated by an automated planning application that determines the volume to be produced across the units of production while leaving the detailed sequencing and timing decisions to the production scheduler. The highest level of sophistication involves an automatically generated schedule with the application retrieving all the necessary data from the appropriate business databases and plant control system.

Regardless of the level of sophistication, all solution approaches rely on two fundamental components for developing a schedule. One is the modeling paradigm used to represent the physical system in a more abstract way. The primary components are: material balances in terms of batches or units of measure (e.g., pounds), and timing information as either precedence-based describing the order of operations or time grid-based describing the instant at which any operation takes place. Time can either be described by a continuous representation or divided into discrete increments. Within these two aspects of the modeling framework, significant freedom exists to describe the scheduling problem. The second fundamental component is the solution method used to generate the schedule. Each method has its strengths; therefore solutions combining methods are also used. The essential problem is to produce the information needed to draw the Gantt chart in Fig. 2 given the information in Table 1.

Product Wheel

While the primary objective of production scheduling is to meet customer orders while managing finished product inventory, other operational issues need to be managed, such as minimizing product transition costs, minimizing variability in manufacturing operations, keeping the scheduling process simple, and balancing the tradeoff between production lead times, inventory, and transition losses. The product wheel is a practical approach widely used in industry to address these competing issues. A product wheel is a regular repeating sequence of products made on a specific unit operation or an entire production process. A product wheel is typically depicted as a pie chart as shown in Fig. 3. Segments of the pie, called spokes of the wheel, represent a production campaign of a particular product. The size of the spoke represents the length of the campaign relative to the overall duration, or cycle time, of the wheel.
Fig. 3

Product wheel

A product wheel has specific design parameters to address various operations objectives. The sequences is fixed and optimized for minimum transition costs. The overall cycle time is fixed and optimized to balance lead time and inventory costs. The campaign size or spokes for each product are sized to match average demand for each product. The fixed pattern of the product wheel provides manufacturing with a predictable operational rhythm and the production scheduler with a very structured decision framework. Refer to King and King (2013) for a complete treatment of product wheels.

In practice, the duration of a campaign for a given product will vary from cycle to cycle as it will be sized to replenish any inventory consumed in the previous cycle. Low volume products may not be made on every cycle, although they will have a fixed location in the sequence. This same approach applies to make-to-order products that are not inventoried but produced to fill specific orders. Thus, in some cases a product wheel may be composed of several different but repeating cycles.

Dispatching Rules Used in Discrete Manufacturing

Batch processes are closely related to discrete manufacturing. Batches processed on a unit are analogous to jobs processed on a machine. Much of the literature on machine scheduling has focused on the analysis of the specifics encountered in general classes of problems such as single machines, parallel machines, flow shops and job shops, and developing constructive scheduling rules where a schedule is built up by adding one job at a time (Blackstone et al. 1982). Under certain circumstances these rules used for machine scheduling can be applied to scheduling batch plants. This allows one to take advantage of a great body of literature, and at times, very simple scheduling rules that have proven optimality or worst case performance limits.

Consider again the batch process referred to in Fig. 1 which has two parallel reactors. The two reactors can be modeled as a single stage process and scheduled like parallel machines using the simple Shortest Processing Time first (SPT) rule if the following circumstances hold: (1) raw material preparation can be included in the batch time of reactors, (2) significant storage exists between the reactors and finishing to essentially isolate the two stages, (3) product specific batch times are identical for both reactors, (4) the number of batches of each product is given (perhaps the result of an inventory policy for make to stock products), and (5) the objective is to minimize the total completion time for all batches. The SPT rule is simply to select, whenever a reactor is free, the batch with the shortest processing time from those yet to be processed. This can be proven to produce an optimal schedule for the given conditions.

Another simple dispatching rule to mention is the Earliest Due Date first (EDD) rule. This rule is designed for single stage processes without parallel units where each batch has an associated due date. The rule simply orders the batches in increasing order of their due dates to minimize the maximum lateness of all orders.

The conditions needed for the SPT rule or the EDD rule to produce an optimal schedule can be quite restrictive when considering batch processes, however these rules and others found in the machine scheduling literature (Baker and Trietsch 2009) can still produce a good initial schedule even in cases where optimality conditions are not satisfied. Once generated, the schedule can be improved by manual manipulation of the Gantt chart or the application of improvement heuristics.

Improvement Heuristics

Improvement heuristics try to improve the current schedule by searching for alternative solutions either in the neighborhood of the current schedule or by broadly exploring the solution space. The behavior of these algorithms is determined by tuning parameters that balance the use of the two search techniques and the underlying algorithm that performs the search. Improvement heuristics generally have the following basic procedure:
Step 1:

Initialize – determine a starting schedule

Step 2:

Generate alternatives – build modifications to the current schedule

Step 3:

Check for improvements in modified schedule – if no improvement is found return to Step 2 otherwise proceed to Step 4

Step 4:

Check for termination – terminate the algorithm if the number of iterations is exceeded or minimal improvement is obtained.

Many improvement heuristics are inspired by processes found in nature. Two of the more popular heuristics are simulated annealing which mimics the crystal formation during the cooling process of dense matter (Ryu et al. 2001) and genetic algorithms that mimic the evolution of a species over time (Löhl et al. 1988). A key aspect of improvement heuristics is the representation of the schedule in context of the algorithm used. For problems with complicated constraints this becomes a challenge. Nevertheless, when tuned properly and used where they fit the problem, improvement heuristics can produce very good schedules quickly.

Tree Search Methods

The scheduling solutions considered so far have taken a relatively simple view of a batch process as a single stage process or a flow shop. In situations where a batch plant involves shared resources, complicated transition rules or is a process network, tree search methods are better suited because they can deal with a large number of degrees of freedom and many types of constraints. Tree search methods rely on representing alternative schedules as the final nodes in a tree where intermediate nodes represent partial solutions of the schedule. To be practical, these methods must be able to effectively search through the tree while pruning non promising branches (see Fig. 4). Three of the most popular techniques are mathematical programming, constraint programming, and beam search.
Fig. 4

Trimming the solution tree

Mathematical programming solution techniques for scheduling generally convert the problem to a mixed integer linear programming (MILP) formulation where branching at nodes of the tree represent alternative values of the integer or binary variables. The tree is searched by a branch-and-bound algorithm which eliminates a node and the branch that emanates from it if the lower bound of the objective function represented by the terminal nodes of the branch is larger than the current best schedule. The MILP formulation can be stated generically as
$$\begin{array}{l} \min \quad z = cx + fy \\ \mathrm{s.t.}\mbox{ }Ax + By \geq b\mbox{ } \\ x \in \mathfrak{R}_{+}^{n},y \in \left \{0,1\right \}^{p}\\ \end{array}$$
where c, f, b are vector of constants, A and B are matrices of constants, and the solution is defined by the vector variables x and y. A key feature of using mathematical programming is to represent the relationships implied in Table 1 and Fig. 1 in terms of algebraic descriptions. The advantage of this approach is that a proven optimal solution exists for a problem stated this way. This provides the means to assess the quality of the solution and the impact of implementing the solution. The drawback of this approach is that since binary variables are used to represent the assignment of a batch to a processing unit, and the sequence and timing of processing on each unit, their number grows rapidly with the number of units and the length of the scheduling horizon. However, the performance of modern computing hardware and commercial solvers for MILP problems has allowed industrial size problems to be tackled.

A large variety of modeling paradigms have been developed to produce a MILP solution (Floudas and Lin 2004; Mendez et al. 2006). They address both sequential and networked processes using continuous time or discrete time representations. For sequential processes, time slot approaches have been developed. For networked processes, the Resource Task Network and the State Task Network have been investigated by many researchers and have been used in industrial applications.

Constraint programming (CP) formulates a problem by writing constraints; but unlike the MILP method, the CP method stresses the feasibility of solutions rather than optimality. Another important difference is that constraints in the CP method do not have to be formulated as algebraic relationships but can be a more general form, thus making it easier in CP to represent complicated constraints. CP processes the constraints sequentially to reduce the space of possible solutions. At each node in the tree, CP processes one constraint after another, reducing the search space at each constraint. Being much newer than mathematical programming, constraint programming has a smaller body of literature to review but excellent performance has been reported in the literature (Baptiste et al. 2001).

In the beam search method, the branch-and-bound algorithm is modified to only evaluate the most promising nodes at any given level of the search tree (Ow and Morton 1988). The number of nodes evaluated is called the beam width and it is a key tuning parameter of the method. Another important element of the method is the technique used to retain nodes for complete evaluation. The technique must balance speed versus thorough evaluation to keep the method practical without discarding promising nodes. The beam search method applied to scheduling has been investigated by many authors (Sabuncuoglu and Bayiz 1999).

Simulation

The simulation approach to scheduling batch plants relies on representing the plant and the relationships inferred by Table 1 in a computer program whose algorithms recreate the behavior of the plant when executed. Generally, the simulators used for batch operations apply Discrete Event Simulation (DES) where entities that have attributes like size, due date, priority, etc. are operated on by activities for a specified duration. Fundamental to DES are the use of queues to hold entities until conditions in the simulation allow them to proceed to their next activity. Time in a DES does not proceed in a continuous manner but rather advances when activities occur. Simulation has the advantage of being able to describe processes and operating policies of arbitrary complexity and model variability in the process operation. Simulators can be used to evaluate manually created schedules or can be combined with optimization and heuristics to produce schedules by simulation-based optimization (Pegden 2011).

An alternative to DES for batch scheduling is the use of multi-agent simulators which are composed of semiautonomous agents assigned to represent the operation of the process and the associated decision making. Each agent has a local goal and communicates with other agents to accomplish it. Like DES, multi-agent simulators are capable of describing very complicated processes. A production schedule can be built through negotiations between agents (Chu et al. 2013).

Selecting a Solution Approach

The selection of the approach for a given batch plant should be value-based, balancing improved revenue with long term cost of ownership by considering such factors as the technical competency of the production scheduler, the expected capacity utilization of the plant, the operational complexity of the plant, and the cost to maintain the scheduling application. The key is to obtain the least complicated solution by reducing the scheduling problem to the highest level of abstraction and by using the simplest solution method that provides an effective schedule. See Harjunkoski et al. (2013) and Pinedo (2008) for a survey of methods and recommendations for their practical application.

Summary and Future Directions

While there are a great variety of solution methods for scheduling, there are still promising research areas to be investigated. The recent introduction of sophisticated, object oriented process control systems with ties to enterprise management systems sets the stage for the development of automatic, real time scheduling. It is here that the principles of feedback control can be applied to batch plant scheduling. Pursuit of this goal will require continued development of fast, adaptive scheduling methods, real time assessment techniques of schedule performance, and tight integration of scheduling with the process control.

Cross-References

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.The Dow Chemical CompanyMidland, MIUSA