Vehicle routing problems with alternative paths: An application to on-demand transportation

Abstract

To cite this version:Thierry Garaix, Christian Artigues, Dominique Feillet, Didier Josselin. Vehicle routing problems with alternative paths: an application to on-demand transportation. European Journal of Operational Research, Elsevier, 2010, 204 (1) AbstractThe class of vehicle routing problems involves the optimization of freight or passenger transportation activities. These problems are generally treated via the representation of the road network as a weighted complete graph. Each arc of the graph represents the shortest route for a possible origin-destination connection. Several attributes can be defined for one arc (travel time, travel cost . . . ), but the shortest route modeled by this arc is computed according to a single criterion, generally travel time. Consequently, some alternative routes proposing a different compromise between the attributes of the arcs are discarded from the solution space. We propose to consider these alternative routes and to evaluate their impact on solution algorithms and solution values through a multigraph representation of the road network. We point out the difficulties brought by this representation for general vehicle routing problems, which drives us to introduce the so-called fixed sequence arc selection problem (FSASP). We propose a dynamic programming solution method for this problem. In the context of an on-demand transportation (ODT) problem, we then propose a simple insertion algorithm based on iterative FSASP solving and a branch-and-price exact method. Computational experiments on modified instances from the literature and on realistic data issued from an ODT system in the French Doubs Central area underline the cost savings brought by the proposed methods using the multigraph model.

Minimizing the Carbon Footprint for the Time-Dependent Heterogeneous-Fleet Vehicle Routing Problem with Alternative Paths

“…In additional to truck transport, the VRP with alternative paths can transport cargos by trains, airplanes, and other additional transportation methods, and may select more than one path between customer nodes. Regardless of additional transportation method or alternative path selection, the costs for each selection vary [8]. The objective of the VRP with alternative paths is to find one (or multiple) optimal route combinations to obtain an optimal solution.…”

“…Although the previous works have addressed the basic VRP, real life VRPs often contain additional variables (e.g., road situations and vehicle types). Since it is unclear whether the basic VRP can be applied to the reality of multiple vehicle types and alternative path selection problems, and the factors in calculating carbon foot prints depend upon different vehicles, vehicle travel speeds, and road situations, most previous works on VRP often only considered a single characteristic, such as vehicle types [6], time-dependent [7], and multiple alternative path selection [8]. However, practical problems often include multiple variables, and applying the VRP that only consider one variable to practical problems may pose difficulties.…”

Efficient heuristic for non-linear transportation problem on the route with multiple ports

“…(15) (16) for m = 1, 2,.., M; i = 1, 2,..., N Generally, we try to find out the optimal loading/unloading sequence with maximal freight costs and minimal expenses; see [4] and [5]. It is a very demanding dual max/min transportation problem but it can be solved in a very simple way.…”

“…The problem can be solved using different techniques, see [5] and [16] In special circumstances the NTP can be seen as Minimum Cost Multiple Commodities Flow Problem (MCMCF); see [1] and [2]. In this paper we applied such network optimization approach.…”

A particle swarm approach to solve vehicle routing problem with uncertain demand: A drug distribution case study

“…Therefore, there have been tremendous efforts to develop realistic problems in supply chain management and logistics to reduce the associated cost as much as possible (Clark and Scarf, 1960;Graves et al, 1993;Garaix et al, 2010;Andersson et al, 2010). One of the primary concerns on most of the real world supply chain problems is the complexity of the resulted mathematical formulations.…”