An ant colony optimization algorithm for solving the full truckload vehicle routing problem with profit

TitreAn ant colony optimization algorithm for solving the full truckload vehicle routing problem with profit
Publication TypeConference Paper
Year of Publication2017
AuthorsK. Bouyahyiouy, E, Bellabdaoui, A
Conference Name2017 International Colloquium on Logistics and Supply Chain Management: Competitiveness and Innovation in Automobile and Aeronautics Industries, LOGISTIQUA 2017
Abstract

This paper proposes an ant colony optimization (ACO) to solve the full-truckload selective multi-depot vehicle routing problem under time windows constraints (denoted by FT-SMDVRPTW). The objective is to construct a solution composed of a set of routes associated with the trucks, aiming at maximizing the total profit. Each order is a pickup and delivery order associated with an origin, a destination, two time windows, and a price for serving the order paid by its corresponding shipper. Each route is a sequence of selected orders to serve so that the operational constraints are respected. Our problem appears clearly when the vehicles return back. It is not obligatory to serve all orders. The motivation of this study is to solve this problem by using an ant colony optimization metaheuristic, called ant colony system, which was originally implemented for solving the basic vehicle routing problem (VRP). We modify the algorithm to incorporate a robust optimization methodology, so that the full truckload can be handled. Finally, we give a numerical example on a randomly generated instance to illustrate our approach. © 2017 IEEE.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85026628672&doi=10.1109%2fLOGISTIQUA.2017.7962888&partnerID=40&md5=88873263b0addef95537c20e4fe48482
DOI10.1109/LOGISTIQUA.2017.7962888
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