A branch-and-cut approach for the vehicle routing problem with loading constraints. (1st April 2016)

• Record Type: Journal Article
• Authors: Hokama, Pedro; Miyazawa, Flávio K.; Xavier, Eduardo C.
• Is Part Of: Expert systems with applications. Volume 47(2016)
• Page Start: 1
• Page End: 13
• Publication Date: 2016-04-01
• Subjects: Vehicle routing -- Two-dimensional packing -- Three-dimensional packing -- Branch-and-cut -- Combinatorial optimization; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.10.013
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A Branch-and-Cut Approach is proposed for the VRP with Loading Constraints. Several techniques are used such as metaheuristics, constraint programming and ILP. This problem arises in real-life problems in the area of transportation of goods. We improved previous results from literature. Abstract: In this paper we describe a branch-and-cut algorithm for the vehicle routing problem with unloading constraints. The problem is to determine a set of routes with minimum total cost, each route leaving a depot, such that all clients are visited exactly once. Each client has a demand, given by a set of items, that are initially stored in a depot. We consider the versions of the problem with two and tri dimensional parallelepiped items. For each route in a solution, we also need to construct a feasible packing for all the items of the clients in this route. As it would be too expensive to rearrange the vehicle cargo when removing the items of a client, it is important to perform this task without moving the other client items. Such packings are said to satisfy unloading constraints. In this paper we describe a branch-and-cut algorithm that uses several techniques to prune the branch-and-cut enumeration tree. The presented algorithm uses several packing routines with different algorithmic approaches, such as branch-and-bound, constraint programming and metaheuristics. The careful combination of these routines showed that the presented algorithm is competitive, and could obtain optimum solutions within significantly smaller computational times for most of the instances presented in the literature.