Abstract
In maritime transportation, decisions are made in a dynamic setting where many aspects of the future are uncertain. However, most academic literature on maritime transportation considers static and deterministic routing and scheduling problems. This work addresses a gap in the literature on dynamic and stochastic maritime routing and scheduling problems, by focusing on the scheduling of departure times. Five simple strategies for setting departure times are considered, as well as a more advanced strategy which involves solving a mixed integer mathematical programming problem. The latter strategy is significantly better than the other methods, while adding only a small computational effort.
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References
Bektas T, Repoussis PP, Tarantilis CD (2014) Dynamic vehicle routing problems. In: Toth P, Vigo D (eds) Vehicle routing: problems, methods, and applications, MOS-SIAM series on optimization, society for industrial and applied mathematics, 2nd edn, pp 299–348
Bent RW, Van Hentenryck P (2007) Waiting and relocation strategies in online stochastic vehicle routing. In: IJCAI, pp 1816–1821
Bent RW, Van Hentenryck P (2004) Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper Res 52:977–987
Berhan E, Beshah B, Kitaw D, Abraham A (2014) Stochastic vehicle routing problem: a literature survey. J Inf Knowl Manag 13:1450022
Branke J, Middendorf M, Noeth G, Dessouky M (2005) Waiting strategies for dynamic vehicle routing. Transp Sci 39:298–312
Christiansen M, Fagerholt K, Nygreen B, Ronen D (2013) Ship routing and scheduling in the new millennium. Eur J Oper Res 228:467–483
Ferrucci F, Bock S, Gendreau M (2013) A pro-active real-time control approach for dynamic vehicle routing problems dealing with the delivery of urgent goods. Eur J Oper Res 225:130–141
Ghiani G, Laporte G, Manni E, Musmanno R (2008) Waiting strategies for the dynamic and stochastic traveling salesman problem. Int J Oper Res 5:233–241
Ghiani G, Manni E, Quaranta A, Triki C (2009) Anticipatory algorithms for same-day courier dispatching. Transp Res E Logist Transp Rev 45:96–106
Hemmati A, Hvattum LM, Norstad I, Fagerholt K (2014) Benchmark suite for a class of industrial and tramp ship routing and scheduling problems. INFOR 52:28–38
Hvattum LM, Løkketangen A, Laporte G (2006) Solving a dynamic and stochastic vehicle routing problem with a sample scenario hedging heuristic. Transp Sci 40:421–438
Hvattum M, Løkketangen A, Laporte G (2007) A branch-and-regret heuristic for stochastic and dynamic vehicle routing problems. Networks 49:330–340
Ichoua S, Gendreau M, Potvin J-Y (2006) Exploiting knowledge about future demands for real-time vehicle dispatching. Transp Sci 40:211–225
Korsvik JE, Fagerholt K, Laporte G (2010) A tabu search heuristic for ship routing and scheduling. J Oper Res Soc 61:594–603
Mitrović-Minić S, Laporte G (2004) Waiting strategies for the dynamic pickup and delivery problem with time windows. Transp Res B Methodol 38:635–655
Moretti Branchini R, Amaral Armentano V, Løkketangen A (2009) Adaptive granular local search heuristic for a dynamic vehicle routing problem. Comput Oper Res 36:2955–2968
Pillac V, Gendreau M, Guéret C, Medaglia AL (2013) A review of dynamic vehicle routing problems. Eur J Oper Res 225:1–11
Psaraftis HN (1995) Dynamic vehicle routing: status and prospects. Ann Oper Res 61:143–164
Psaraftis HN, Wen M, Kontovas CA (2016) Dynamic vehicle routing problems: three decades and counting. Networks 67:3–31
Pureza V, Laporte G (2008) Waiting and buffering strategies for the dynamic pickup and delivery problem with time windows. Inf Syst Oper Res 46:165–176
Ritzinger U, Puchinger J, Hartl RF (2016) A survey on dynamic and stochastic vehicle routing problems. Int J Prod Res 54:215–231
Saint-Guillain M, Deville Y, Solnon C (2015) A multistage stochastic programming approach to the dynamic and stochastic VRPTW. In: Integration of AI and OR techniques in constraint programming, pp 357–374. Springer
Silberholz J, Golden B (2010) Comparison of metaheuristics. In: Gendreau M, Potvin J-Y (eds) Handbook of metaheuristics, volume 146 of international series in operations research and management science, 2nd edn. Springer, New York, pp 625–640
Thomas BW (2007) Waiting strategies for anticipating service requests from known customer locations. Transp Sci 41:319–331
Tirado G, Hvattum LM, Fagerholt K, Cordeau J-F (2013) Heuristics for dynamic and stochastic routing in industrial shipping. Comput Oper Res 40:253–263
Vonolfen S, Affenzeller M (2016) Distribution of waiting time for dynamic pickup and delivery problems. Ann Oper Res 236:359–382
Acknowledgments
This research was carried out with financial support from the DOMinant II project, partly funded by the Research Council of Norway, from a grant from Iceland, Liechtenstein and Norway through the EEA Financial Mechanism (operated by Universidad Complutense de Madrid) with reference 026-ABELIM-2013, and from the Government of Spain, grant TIN2012-32482.
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Tirado, G., Hvattum, L.M. Determining departure times in dynamic and stochastic maritime routing and scheduling problems. Flex Serv Manuf J 29, 553–571 (2017). https://doi.org/10.1007/s10696-016-9242-x
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DOI: https://doi.org/10.1007/s10696-016-9242-x