Abstract
Providers of logistic services in recent years are under a big pressure to lower their expenses. One way to accomplish this task is centralization of logistic activities. This creates a distribution centers with a large number of customers. The capacity or time of one delivery person is limited, but, at the same time, it usually serves many customers. This problem is often called a Street Routing Problem (SRP). This paper is a survey of aggregation heuristics that can be used for a solution of Very Large SRP (VLSRP). Performance of heuristics has been evaluated based on real data. This paper presents several approximations of length for a SRP with mixed transportation mode and compares them with published approximations used for Vehicle Routing Problem (VRP) or Traveling Salesman Problems (TSP). The method was tested in seven real world instances ranging from 11000 to 29000 customers. Several aggregation methods including two new are presented and compared for the creation of delivery districts. New measurements for the quality of aggregation are created and tested on real data with all discussed aggregation methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bodin L, Maniezzo V, Mingozzi A (1999) Street routing and scheduling problems. In: Hall RW (eds) Handbook of transportation science. Kluwer, Dordrecht
Bräysy O, Gendreau M (2005) Vehicle routing problem with time windows, Part I: route construction and local search algorithms. Transp Sci 39: 104–139
Figliozzi MA (2008) Planning approximations to the average length of vehicle routing problems with varying customer demands and routing constraints. Transp Res Rec J Transp Board (2089):1–8
Gendreau M, Crainic TG (2002) Cooperative parallel tabu search for capacitated network design. J Heuristics 8(6): 601–627
Janácek J (2003) Optimization in transport network. EDIS-ZU, Zilina
Kwon O, Golden B, Wasil E (1995) Estimating the length of the optimal TSP tour—an empirical-study using regression and neural networks. Comput Oper Res 22(10): 1039–1046
Lee H, Cha S, Yu Y, Jo G (2009) Large neighborhood search using constraint satisfaction techniques in vehicle routing problem. Lecture Notes in Computer Science. pp 229–232
Lemoine OW, Skjoett-Larsen T (2004) Reconfiguration of supply chains and implications for transport: a danish study. Int J Phys Distrib Logistics Manage 34(10): 793–810
Li F, Golden B, Wasil E (2005) Very large-scale vehicle routing: new test problems, algorithms, and results. Comput Oper Res 32: 1165–1179
Matis P (2006) Management of street routing problems using decisions support system. Communications 3: 5–8
Matis P (2008) Decision support system for solving the street routing problem. Transport 23(3): 230–236
Marinakis Y, Migdalas A (2007) Annotated bibliography in vehicle routing. Oper Res 7(1): 27–46
Oppen J, Løkketangen A (2006) Arc routing in a node routing environment. Comput Oper Res 33(4): 1033–1055
Poot A, Kant G, Wagelmans A (2002) A saving based method for real-life vehicle routing problems. J Oper Res Soc 57–68
Richards JA, Jia X (2005) Remote sensing digital image analysis, an introduction. Springer, Berlin
Rimiene K, Grundey D (2007) Logistics centre concept trough evolution and definition. Eng Econ 4(54): 87–95
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Matis, P., Koháni, M. Very large street routing problem with mixed transportation mode. Cent Eur J Oper Res 19, 359–369 (2011). https://doi.org/10.1007/s10100-010-0159-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10100-010-0159-2