Distribution of waiting time for dynamic pickup and delivery problems
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Pickup and delivery problems have numerous applications in practice such as parcel delivery and passenger transportation. In the dynamic variant of the problem, not all information is available in advance but is revealed during the planning process. Thus, it is crucial to anticipate future events in order to generate high-quality solutions. Previous work has shown that the use of waiting strategies has the potential to save costs and maximize service quality. We adapt various waiting heuristics to the pickup and delivery problem with time windows. Previous research has shown, that specialized waiting heuristics utilizing anticipatory knowledge potentially outperform general heuristics. Direct policy search based on evolutionary computation and a simulation model is proposed as a methodology to automatically specialize waiting strategies to different problem characteristics. Based on the strengths of the previously introduced waiting strategies, we propose a novel waiting heuristic that can utilize historical request information based on an intensity measure which does not require an additional data preprocessing step. The performance of the waiting heuristics is evaluated on a single set of benchmark instances containing various instance classes that differ in terms of spatial and temporal properties. The diverse set of benchmark instances is used to analyze the influence of spatial and temporal instance properties as well as the degree of dynamism to the potential savings that can be achieved by anticipatory waiting and the incorporation of knowledge about future requests.
KeywordsDynamic pickup and delivery problem Waiting strategies Direct policy search Simulation-based optimization
- Beham, A., Kofler, M., Wagner, S., Affenzeller, M. (2009). Coupling simulation with heuristiclab to solve facility layout problems. In: Simulation Conference (WSC), Proceedings of the 2009 Winter, pp. 2205–2217. doi: 10.1109/WSC.2009.5429238.
- Bent, R., Van Hentenryck, P. (2007). Waiting and relocation strategies in online stochastic vehicle routing. In: IJCAI, pp. 1816–1821.Google Scholar
- Branke, J., Middendorf, M., Noeth, G., Dessouky, M. (2005). Waiting strategies for dynamic vehicle routing. Transportation Science 39:298–312. doi: 10.1287/trsc.1040.0095.
- Can, B., Beham, A., Heavey, C. (2008). A comparative study of genetic algorithm components in simulation-based optimisation. In: Proceedings of the 40th Conference on Winter Simulation, Winter Simulation Conference, WSC ’08, pp 1829–1837, URL http://dl.acm.org/citation.cfm?id=1516744.1517063
- Cordeau, J., Gendreau, M., Laporte, G., Potvin, J., & Semet, F. (2002). A guide to vehicle routing heuristics. Journal of the Operational Research Society, 53(5), 512–522.Google Scholar
- Ferrucci, F., Bock, S., & Gendreau, M. (2012). A pro-active real-time control approach for dynamic vehicle routing problems dealing with the delivery of urgent goods. European Journal of Operational Research, 225(1), 130–141.Google Scholar
- Hentenryck, P. V., & Bent, R. (2009). Online stochastic combinatorial optimization. Cambridge: The MIT Press.Google Scholar
- Ichoua, S., Gendreau, M., Potvin, JY. (2007). Planned route optimization for real-time vehicle routing. In: Dynamic fleet management, Springer, pp 1–18.Google Scholar
- Li, H., Lim, A. (2001). A metaheuristic for the pickup and delivery problem with time windows. In: Tools with artificial intelligence, Proceedings of the 13th International Conference on, pp 160–167, Doi: 10.1109/ICTAI.2001.974461.
- Mitrovic-Minic, S., Adviser-Krishnamurti, R., & Adviser-Laporte, G. (2001). The dynamic pickup and delivery problem with time windows. Burnaby: Simon Fraser University.Google Scholar
- Moriarty, D. E., Schultz, A. C., & Grefenstette, J. J. (1999). Evolutionary algorithms for reinforcement learning. Journal of Artificial Intelligence Research, 11, 241–276.Google Scholar
- Pappa, GL., Ochoa, G., Hyde, MR., Freitas, AA., Woodward, J., Swan, J. (2013). Contrasting meta-learning and hyper-heuristic research: The role of evolutionary algorithms. Genetic Programming and Evolvable Machines, 1–33.Google Scholar
- Pitzer, E., Beham, A., Affenzeller, M., Heiss, H., Vorderwinkler, M. (2011). Production fine planning using a solution archive of priority rules. In: Logistics and industrial informatics (LINDI), 2011 3rd IEEE International Symposium on, pp 111–116, Doi: 10.1109/LINDI.2011.6031130.
- Psaraftis, H. (1988). Dynamic vehicle routing problems. In: Vehicle routing: Methods and studies, Elsevier Science Publishers, pp 223–249.Google Scholar
- R Core Team. (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, URL http://www.R-project.org
- Scheibenpflug, A., Wagner, S., Kronberger, G., Affenzeller, M. (2012). Heuristiclab hive-an open source environment for parallel and distributed execution of heuristic optimization algorithms. In: 1st Australian Conference on the Applications of Systems Engineering ACASE’12, p 63.Google Scholar
- Silverthorn, BC. (2012). A probabilistic architecture for algorithm portfolios. PhD thesis, The University of Texas at Austin, may.Google Scholar
- Van Hemert, JI., La Poutré, JA. (2004). Dynamic routing problems with fruitful regions: Models and evolutionary computation. In: Parallel problem solving from nature-PPSN VIII, Springer, pp 692–701.Google Scholar
- Vonolfen, S., Beham, A., Kommenda, M., Affenzeller, M. (2013b). Structural synthesis of dispatching rules for dynamic dial-a-ride problems. In: Proceedings of the 14th international conference on Computer Aided Systems Theory, Springer.Google Scholar
- Wagner, S. (2009). Heuristic optimization software systems - Modeling of heuristic optimization algorithms in the HeuristicLab software environment. PhD thesis, Johannes Kepler University, Linz, Austria.Google Scholar
- Whiteson, S. (2012). Evolutionary computation for reinforcement learning. In: Reinforcement learning, Springer, pp 325–355.Google Scholar
- Zeimpekis, V., Tarantilis, C., Giaglis, G., & Minis, I. (2007). Dynamic fleet management. Operations research/computer science interfaces. New York: Springer.Google Scholar