Abstract
The transportation industry expanded rapidly in a highly competitive environment. Logistics companies with insufficient volume of transport capacities are forced to make a selection of customers that they can integrate efficiently into their tours. This is of particular relevance in the pickup and delivery market, where shipments from several different customers can be moved on the same vehicle. In the literature, however, the problem of customer selection has not been applied for the given class of pickup and delivery problems so far. We want to fill this gap by introducing the multi-vehicle profitable pickup and delivery problem (MVPPDP), where multiple carriers transport goods from a selection of pickup customers to the corresponding delivery customers within given travel time limits. For this problem, we propose a method based on general variable neighborhood search (GVNS). We conduct experiments with two different variants of this method, namely a sequential (GVNSseq) and a self-adaptive (GVNSsa) version. Additionally, we compare it to an algorithm based on Guided Local Search (GLS), which is known to find good solutions for related problems very fast. The performance of these methods is examined on the basis of data instances with up to 1000 customer requests. In an experimental study, we observe that both variants of GVNS with 11 neighborhoods outperform GLS with regard to solution quality for all sizes of test instances. However, for medium sized and large instances, GLS shows an advantage in average runtimes.
Highlights
In the past decades, intensification of competition on global markets together with heightened customer expectations has led to an increased pricing pressure which negatively affects logistic providers’ profit margins (Ruijgrok 2001)
In this study we propose and formally define the multivehicle profitable pickup and delivery problem (MVPPDP), where we assume carriers to serve less than truckload paired pickup and delivery requests, i.e., each request is associated with a prespecified origin and destination
For the general variable neighborhood search (GVNS), we present two Variable Neighborhood Descent (VND) search strategies, which are the GVNS sequential (GVNSseq) as well as the GVNS self-adaptive (GVNSsa) one
Summary
Intensification of competition on global markets together with heightened customer expectations has led to an increased pricing pressure which negatively affects logistic providers’ profit margins (Ruijgrok 2001). For the carriers participating in such networks, it is of particular importance to (i) make a good selection of customer requests that should be served with their own fleet, (ii) assign selected requests to vehicles, and (iii) visit them in most efficient tours This real world issue has been addressed in the literature of combinatorial optimization by the huge subclass of vehicle routing problems (VRPs) that consider the possibility of visiting only a subset of customers, associate a revenue with each of them and aim at maximizing the total profit. This real world problem is, for instance, addressed by the well-known team orienteering problem (TOP), proposed by Chao et al (1996b), or multiple tour maximum collection problem (Butt and Cavalier 1994).
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