The dynamic vehicle rescheduling problem
- 255 Downloads
The pre-planned schedules of a transportation company are often disrupted by unforeseen events. As a result of a disruption, a new schedule has to be produced as soon as possible. This process is called the vehicle rescheduling problem, which aims to solve a single disruption and restore the order of transportation. However, there are multiple disruptions happening over a “planning unit” (usually a day), and all of them have to be addressed to achieve a final feasible schedule. From an operations management point of view the quality of the final solution has to be measured by the combined quality of every change over the horizon of the “planning unit”, not by evaluating the solution of each disruption as a separate problem. The problem of finding an optimal solution where all disruptions of a “planning unit” are addressed will be introduced as the dynamic vehicle rescheduling problem (DVRSP). The disruptions of the DVRSP arrive in an online manner, but giving an optimal final schedule for the “planning unit” would mean knowing all information in advance. This is not possible in a real-life scenario, which means that heuristic solution methods have to be considered. In this paper, we present a recursive and a local search algorithm to solve the DVRSP. In order to measure the quality of the solutions given by the heuristics, we introduce the so-called quasi-static DVRSP, a theoretical problem where all the disruptions are known in advance. We give two mathematical models for this quasi-static problem, and use their optimal solutions to evaluate the quality of our heuristic results. The heuristic methods for the dynamic problem are tested on different random instances.
KeywordsDisruption management Vehicle rescheduling Heuristic method
This research was partionally supported by National Research, Development and Innovation Office - NKFIH Fund No. SNN-117879.
- Balogh J, Dávid B (2014) An algorithmic framework for real-time rescheduling in public bus transportation. In: Proceedings of the 2013 mini-conference on applied theoretical computer science (MATCOS), pp 29–33Google Scholar
- Bodin L, Golden B, Assad A, Ball M (1983) Routing and scheduling of vehicles and crews: the state of the art. Comput Oper Res 10(1):63–212Google Scholar
- Clausen J, Larsen A, Larsen J (2005) Disruption management in the airline industry—concepts, models and methods. Tech. rep., informatics and mathematical modelling, Technical University of Denmark, DTUGoogle Scholar
- Dávid B, Krész M (2014) A model and fast heuristics for the multiple depot bus rescheduling problem. In: 10th international conference on the practice and theory of automated timetabling (PATAT), pp 128–141Google Scholar
- Desrochers M, Lenstra J, Savelsbergh M, Soumis F (1988) Vehicle routing with time windows: optimization and approximation. Veh Routing Methods Stud 16:65–84Google Scholar
- Jespersen-Groth J, Potthoff D, Clausen J, Huisman D, Kroon LG, Maróti G, Nielsen MN (2007) Disruption management in passenger railway transportation. Tech. rep., Erasmus University RotterdamGoogle Scholar
- Lettovsky L (1997) Airline operations recovery: an optimization approach. Ph.D. thesisGoogle Scholar
- Löbel A (1997) Optimal vehicle scheduling in public transit. Ph.D. thesisGoogle Scholar
- Psaraftis H (1988) Dynamic vehicle routing problems. Veh Routing Methods Stud 16:223–248Google Scholar