Abstract
This paper presents a solution methodology for the heterogeneous fleet vehicle routing problem with time windows. The objective is to minimize the total distribution costs, or similarly to determine the optimal fleet size and mix that minimizes both the total distance travelled by vehicles and the fixed vehicle costs, such that all problem’s constraints are satisfied. The problem is solved using a two-phase solution framework based upon a hybridized Tabu Search, within a new Reactive Variable Neighborhood Search metaheuristic algorithm. Computational experiments on benchmark data sets yield high quality solutions, illustrating the effectiveness of the approach and its applicability to realistic routing problems.
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Bent, R., van Hentenryck, P.: A two-stage hybrid local search for the vehicle routing problem with time windows. Transp. Sci. 38(4), 515–530 (2004)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimisation: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)
Brandäo, J., Mercer, A.: A tabu search algorithm for the multi-trip vehicle routing and scheduling problem. Eur. J. Oper. Res. 100, 180–191 (1997)
Bräysy, O.: Local search and variable neighborhood search algorithms for the vehicle routing problem with time windows. Acta Wasaensia 87, University Wasaenis, Vaasa (2001)
Bräysy, O.: A reactive variable neighborhood search algorithm for the vehicle routing problem with time windows. INFORMS J. Comput. 15(4), 347–368 (2003)
Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows part I: route construction and local search algorithms. Transp. Sci. 39(1), 104–118 (2005)
Cordeau, J.-F., Desaulniers, G., Desrosiers, J., Solomon, M., Soumis, F.: The vehicle routing problem with time windows. In: Toth, P., Vigo, D. (eds.) The Vehicle Routing Problem. SIAM Monographs on Discrete Mathematics and Applications, pp. 157–193. SIAM, Philadelphia (2002)
Cordone, R., Calvo, R.W.: A heuristic for the vehicle routing with time windows. J. Heur. 7, 107–129 (2001)
Choi, E., Tcha, D.-W.: A column generation approach for the heterogeneous fleet vehicle routing problem. Comput. Oper. Res., doi:10.1016/j.cor.2005.08.002 (2005)
Clarke, G., Wright, J.: Scheduling of vehicles from a central depot to a number of delivery points. Oper. Res. 12, 568–581 (1964)
Desrochers, M., Verhoog, T.W.: A new heuristic for the fleet size and mix vehicle routing problem. Comput. Oper. Res. 18(3), 263–274 (1991)
Dell-Amico, M., Monaci, M., Pagani, C., Vigo, D.: Heuristic approaches for the fleet size and mix vehicle routing problem with time windows. Technical Report, Observatorio Logistico, Università di Modena e Reggio Emilia, Italy (2006)
Dullaert, W., Janssens, G.K., Sörensen, K., Vernimmen, B.: New heuristics for the fleet size and mix vehicle routing problem with time windows. J. Oper. Res. Soc. 53, 1232–1238 (2002)
García-López, F., Melián-Batista, B., Moreno-Pérez, J.A., Moreno-Vega, J.M.: The parallel variable neighborhood search for the p-median problem. J. Heur. 8, 375–388 (2002)
Gendreau, M., Potvin, J.Y.: Metaheuristics in combinatorial optimization. Ann. Oper. Res. 140, 189–213 (2005)
Gendreau, M., Laporte, G., Musaraganyi, C., Taillard, E.D.: A tabu search heuristic for the heterogeneous fleet vehicle routing problem. Comput. Oper. Res. 26, 1153–1173 (1999)
Geysens, F., Golden, B., Assad, A.: A comparison of techniques for solving the fleet size and mix vehicle routing problem. Oper. Res. Spektrum 6, 207–216 (1984)
Geysens, F., Golden, B., Assad, A.: A new heuristic for determining fleet size composition. Math. Program. Stud. 26, 233–236 (1986)
Glover, F.: New ejection chain and alternating path methods for travelling salesman problem. In: Balci, O., Sharda, R., Zenios, S. (eds.) Computer Science and Operation Research: New Development in Their Interfaces, pp. 449–509. Pergamon Press, Oxford (1992)
Golden, B., Assad, A., Levy, L., Gheysens, F.: The fleet size and mix vehicle routing problem. Comput. Oper. Res. 11, 49–66 (1984)
Hansen, P., Mladenović, N.: Variable neighborhood search: principles and applications. Eur. J. Oper. Res. 130, 449–467 (2001)
Ioannou, G., Kritikos, M., Prastacos, G.: A greedy look-ahead heuristic for the vehicle routing problem with time windows. J. Oper. Res. Soc. 52, 523–537 (2001)
Kindervater, G.A.P., Savelsbergh, M.W.P.: Vehicle routing: handling edge exchanges. In: Aarts, E., Lenstra, J.K. (eds.) Local Search in Combinatorial Optimization. pp. 337–360. Wiley, UK (1998)
Li, F., Golden, B., Wasil, E.: A record-to-record travel algorithm for solving the heterogeneous fleet vehicle routing problem. Comput. Oper. Res., doi:10.1016/j.cor.2005.10.015 (2006)
Liu, F.H., Shen, S.Y.: A method for vehicle routing problem with multiple vehicle types and times windows. Proc. Nat. Sci. Counc. Part A Phys. Sci. Eng. 23(4), 526–536 (1999a)
Liu, F.H., Shen, S.Y.: The fleet size and mix routing problem with time windows. J. Oper. Res. Soc. 50(7), 721–732 (1999b)
Osman, I., Salhi, S.: Local search strategies for the vehicle fleet mix problem. In: Rayward-Smith, V.J., Osman, I.H., Reeves, C.R., Smith, G.D. (eds.) Modern Heuristic Search Methods. pp. 131–153. Wiley, New York (1996)
Polacek, M., Hartl, R.F., Doerner, K.: A variable neighborhood search for the multi depot vehicle routing problem with time windows. J. Heur. 10, 613–627 (2004)
Renaud, J., Boctor, F.F.: A sweep-based algorithm for the fleet size and mix vehicle routing problem. Eur. J. Oper. Res. 140, 618–628 (2002)
Rochat, Y., Semet, F.: A tabu search approach for delivering pet food and flour in Switzerland. J. Oper. Res. Soc. 45, 1233–1246 (1994)
Rouseau, L.M., Gendreau, M., Pesant, G.: Using constraint based operators to solve the vehicle routing problem with time windows. J. Heur. 8, 43–58 (2002)
Salhi, S., Rand, G.: Incorporating vehicle routing into the vehicle fleet composition problem. Eur. J. Oper. Res. 66, 313–330 (1993)
Salhi, S., Sari, M., Touati, N.: Adaptation of some vehicle fleet mix heuristics. OMEGA 20, 653–660 (1992)
Semet, F.: A two-phase algorithm for the partial accessibility constrained vehicle routing. Ann. Oper. Res. 61, 45–65 (1995)
Semet, F., Taillard, E.D.: Solving real-life vehicle routing problems efficiently using tabu search. Ann. Oper. Res. 41, 469–488 (1993)
Solomon, M.M.: Algorithms for the vehicle routing and scheduling with time windows constraints. Oper. Res. 35, 254–265 (1987)
Taillard, E.D.: A heuristic column generation method for the heterogeneous fleet VRP. RAIRO 33, 1–14 (1999)
Tarantilis, C.D.: Solving the vehicle routing problem with adaptive memory programming methodology. Comput. Oper. Res. 32, 2309–2327 (2005)
Tarantilis, C.D., Kiranoudis, C.T.: BoneRoute: an adaptive memory-based method for effective fleet management. Ann. Oper. Res. 115, 227–241 (2002)
Tarantilis, C.D., Kiranoudis, C.T.: A flexible adaptive memory-based algorithm for real-life transportation operations: two case studies from dairy and construction sector. Eur. J. Oper. Res., doi:10.1016/j.ejor.2005.03.059 (2005)
Tarantilis, C.D., Kiranoudis, C.T., Vassiliadis, V.S.: A list based threshold accepting metaheuristic for the heterogeneous fixed fleet vehicle routing problem. J. Oper. Res. Soc. 54, 65–71 (2003)
Tarantilis, C.D., Kiranoudis, C.T., Vassiliadis, V.S.: A threshold accepting metaheuristic for the heterogeneous fixed fleet vehicle routing problem. Eur. J. Oper. Res. 152, 148–158 (2004)
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This work is supported by the General Secretariat for Research and Technology of the Hellenic Ministry of Development under contract GSRT NM-67.
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Paraskevopoulos, D.C., Repoussis, P.P., Tarantilis, C.D. et al. A reactive variable neighborhood tabu search for the heterogeneous fleet vehicle routing problem with time windows. J Heuristics 14, 425–455 (2008). https://doi.org/10.1007/s10732-007-9045-z
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DOI: https://doi.org/10.1007/s10732-007-9045-z