Abstract
Currently, most of the policies for the dynamic demand vehicle routing problem are based on the traditional method for static problems as there is no general method for constructing a real-time optimization policy for the case of dynamic demand. Here, a new approach based on a combination of the rules from the static sub-problem to building real-time optimization policy is proposed. Real-time optimization policy is dividing the dynamic problem into a series of static sub-problems along the time axis and then solving the static ones. The static sub-problems’ transformation and solution rules include: Division rule, batch rule, objective rule, action rule and algorithm rule, and so on. Different combinations of these rules may constitute a variety of real-time optimization policy. According to this general method, two new policies called flexible G/G/m and flexible D/G/m were developed. The competitive analysis and the simulation results of these two policies proved that both are improvements upon the best existing policy.
Supported by the National Natural Science Foundation of China (71461006, 71461007, 71761009), Hainan Province Planning Program of Philosophy and Social Science (HNSK(YB)19-06, HNSK(YB)19-11), and a Key Program of Hainan Educational Committee (hnky2019ZD-10)
References
[1] Bianchi L. Notes on dynamic vehicle routing — The state of the art// Notes on dynamic vehicle routing — The state of the art. 2000.Search in Google Scholar
[2] Bertsimas D J, Van Ryzin G. A stochastic and dynamic vehicle routing problem in the Euclidean plane. Operations Research, 1991, 39(4): 601–615.10.1287/opre.39.4.601Search in Google Scholar
[3] Bertsimas D J, Van Ryzin G. Stochastic and dynamic vehicle routing with general demand and interarrival time distributions. Advances in Applied Probability, 1993, 25(4): 947–978.10.2307/1427801Search in Google Scholar
[4] Bertsimas D J, Van Ryzin G. Stochastic and dynamic vehicle routing in the Euclidean plane with multiple capacitated vehicles. Operations Research, 1993, 41(1): 60–76.10.1287/opre.41.1.60Search in Google Scholar
[5] Papastavrou J D. A stochastic and dynamic routing policy using branching processes with state dependent immigration. European Journal of Operational Research, 1996, 95(1): 167–177.10.1016/0377-2217(95)00189-1Search in Google Scholar
[6] Pavone M, Frazzoli E, Bullo F. Decentralized algorithms for stochastic and dynamic vehicle routing with general demand distribution//2007 46th IEEE Conference on Decision and Control. IEEE, 2007: 4869–4874.10.1109/CDC.2007.4434989Search in Google Scholar
[7] Pavone M, Frazzoli E, Bullo F. Adaptive and distributed algorithms for vehicle routing in a stochastic and dynamic environment. IEEE Transactions on Automatic Control, 2011, 56(6): 1259–1274.10.1109/TAC.2010.2092850Search in Google Scholar
[8] Gendreau M, Guertin F, Potvin J Y, et al. Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries. Transportation Research Part C: Emerging Technologies, 2006, 14(3): 157–174.10.1016/j.trc.2006.03.002Search in Google Scholar
[9] Gendreau M, Guertin F, Potvin J Y, et al. Parallel tabu search for real-time vehicle routing and dispatching. Transportation Science, 1999, 33(4): 381–390.10.1287/trsc.33.4.381Search in Google Scholar
[10] Montemanni R, Gambardella L M, Rizzoli A E, et al. Ant colony system for a dynamic vehicle routing problem. Journal of Combinatorial Optimization, 2005, 10(4): 327–343.10.1007/s10878-005-4922-6Search in Google Scholar
[11] Chen Z L, Xu H. Dynamic column generation for dynamic vehicle routing with time windows. Transportation Science, 2006, 40(1): 74–88.10.1287/trsc.1050.0133Search in Google Scholar
[12] Bent R W, Van Hentenryck P. Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Operations Research, 2004, 52(6): 977–987.10.1287/opre.1040.0124Search in Google Scholar
[13] Larsen A, Madsen O B G, Solomon M M. The a priori dynamic traveling salesman problem with time windows. Transportation Science, 2004, 38(4): 459–472.10.1287/trsc.1030.0070Search in Google Scholar
[14] Bent R, Van Hentenryck P. Waiting and relocation strategies in online stochastic vehicle routing//IJCAI. 2007: 1816–1821.Search in Google Scholar
[15] Ichoua S, Gendreau M, Potvin J Y. Diversion issues in real-time vehicle dispatching. Transportation Science, 2000, 34(4): 426–438.10.1287/trsc.34.4.426.12325Search in Google Scholar
[16] Gendreau M, Potvin J Y. Dynamic vehicle routing and dispatching//Fleet management and logistics. Springer, Boston, MA, 1998: 115–126.10.1007/978-1-4615-5755-5_5Search in Google Scholar
[17] Ichoua S, Gendreau M, Potvin J Y. Exploiting knowledge about future demands for real-time vehicle dispatching. Transportation Science, 2006, 40(2): 211–225.10.1287/trsc.1050.0114Search in Google Scholar
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Articles in the same Issue
- Energy Consumption and Economic Growth Nexus in Bangladesh
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- Multiobjective Imperialist Competitive Algorithm for Solving Nonlinear Constrained Optimization Problems
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