Multi-trip vehicle routing with time windows; Heuristics; Adaptive large neighborhood search
Abstract :
[en] We consider a multi-trip vehicle routing problem with time windows (MTVRPTW), in which each vehicle can perform several trips during its working shift. This problem is especially relevant in the context of city logistics. Heuristic solution methods for multi-trip vehicle routing problems often separate routing and assignment phases in order to create trips and then assign them to the available vehicles. We show that this approach is outperformed by an integrated solution method in the presence of time windows. We use an automatic configuration tool to obtain efficient and contextualized implementations of our solution methods. We provide suitable instances for the MTVRPTW as well as an instance generator. Also, we discuss the relevance of two objective functions: the total duration and the total travel time. When minimizing the travel time, large increases of waiting time are incurred, which is not realistic in practice.
Research center :
QuantOM - Quantitative methods and Operations Management
Disciplines :
Production, distribution & supply chain management
Azi N, Gendreau M, Potvin JY (2014) An adaptive large neighborhood search for a vehicle routing problem with multiple routes. Comput. Oper. Res. 41(1):167-173.
Battarra M, Monaci M, VigoD (2009) An adaptive guidance approach for the heuristic solution of a minimum multiple trip vehicle routing problem. Comput. Oper. Res. 36(11):3041-3050.
Cattaruzza D, Absi N, Feillet D (2016a) The multi-trip vehicle routing problem with time windows and release dates. Transportation Sci. 50(2):676-693.
Cattaruzza D, Absi N, Feillet D (2016b) Vehicle routing problems with multiple trips. 4OR 14(3):223-259.
Cattaruzza D, Absi N, Feillet D, González-Feliu J (2017) Vehicle routing problems for city logistics. EURO J. Transportation Logist. 6(1):51-79.
Cattaruzza D, Absi N, Feillet D, Vidal T (2014) A memetic algorithm for the multi trip vehicle routing problem. Eur. J. Oper. Res. 236(3):833-848.
Comité National Routier (2017) Indices et statistiques: Logistique. Accessed December 19, 2017, http://www.cnr.fr/Indices -Statistiques.
Despaux F, Basterrech S (2016) Multi-trip vehicle routing problem with time windows and heterogeneous fleet. Internat. J. Comput. Inform. Systems Indust. Management Appl. 8:355-363.
Fleischmann B (1990) The vehicle routing problem with multiple use of vehicles. Working paper, Fachbereich Wirtschaftswissenschaften, Universität Hamburg, Hamburg, Germany.
François V, Arda Y, Crama Y, Laporte G (2016) Large neighborhood search for multi-trip vehicle routing. Eur. J. Oper. Res. 255(2): 422-441.
Hansen P, Mladenović N (2001) Variable neighborhood search: principles and applications. Eur. J. Oper. Res. 130(3):449-467.
Hernandez F, Feillet D, Giroudeau R, Naud O (2013) An exact algorithm to solve the multi-trip vehicle routing problem with time windows. Working paper, École Polytechnique de Montréal and Centre Interuniversitaire de Recherche sur les Réseaux d'Entreprise, la Logistique et le Transport, Montréal.
Hernandez F, Feillet D, Giroudeau R, Naud O (2014) A new exact algorithm to solve the multi-trip vehicle routing problem with time windows and limited duration. 4OR 12(3):235-259.
Hernandez F, Feillet D, Giroudeau R, Naud O (2016) Branch-and-price algorithms for the solution of the multi-trip vehicle routing problem with time windows. Eur. J. Oper. Res. 249(2):551-559.
Kergosien Y, Gendreau M, Billaut JC (2017) A Benders decompositionbased heuristic for a production and outbound distribution scheduling problem with strict delivery constraints. Eur. J. Oper. Res. 262(1):287-298.
Lee J, Kim BI, Johnson AL, Lee K (2014) The nuclear medicine production and delivery problem. Eur. J. Oper. Res. 236(2): 461-472.
López-Ibáñez M, Dubois-Lacoste J, Pérez Cáceres L, Birattari M, Stützle T (2016) The irace package: Iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3:43-58.
Nagata Y, Bräysy O, Dullaert W (2010) A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows. Comput. Oper. Res. 37(4):724-737.
Olivera A, Viera O (2007) Adaptive memory programming for the vehicle routing problem with multiple trips. Comput. Oper. Res. 34(1):28-47.
Pisinger D, Ropke S (2007) A general heuristic for vehicle routing problems. Comput. Oper. Res. 34(8):2403-2435.
Prins C (2004) A simple and effective evolutionary algorithm for the vehicle routing problem. Comput. Oper. Res. 31(12):1985-2002.
Ropke S, Pisinger D (2006a) An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Sci. 40(4):455-472.
Ropke S, Pisinger D (2006b) A unified heuristic for a large class of vehicle routing problems with backhauls. Eur. J. Oper. Res. 171(3):750-775.
Savelsbergh M (1992) The vehicle routing problem with time windows: minimizing route duration. ORSA J. Comput. 4(2): 146-154.
Shaw P (1998) Using constraint programming and local search methods to solve vehicle routing problems. Maher M, Puget JF, eds. Principles and Practice of Constraint Programming-CP98. CP 1998. Lecture Notes in Computer Science, vol. 1520 (Springer, Berlin, Heidelberg), 417-431.
Solomon M (1987) Algorithms for the vehicle routing and scheduling problem with time window constraints. Oper. Res. 35(2): 254-265.
Taillard E, Laporte G, Gendreau M (1996) Vehicle routing with multiple use of vehicles. J. Oper. Res. Soc. 47(8):1065-1070.
Union Professionnelle du Transport et de la Logistique (2012) Cahier de revendications. Accessed December 19, 2017, http://www .uptr.be/theme/medias/upload/f%C3%A9vrier_2012_cahier %20de_revendications_FR.pdf.
Vidal T, Crainic T, Gendreau M, Prins C (2015) Time-window relaxations in vehicle routing heuristics. J. Heuristics 21(3):329-358.
Wang Z, Liang W, Hu X (2014) A metaheuristic based on a pool of routes for the vehicle routing problem with multiple trips and time windows. J. Oper. Res. Soc. 65(1):37-48.
