An evolutionary algorithm for the multi‐objective pick‐up and delivery pollution‐routing problem. (7th February 2017)

• Record Type: Journal Article
• Authors: Bravo, Mauricio; Rojas, Lorena Pradenas; Parada, Victor
• Is Part Of: International transactions in operational research. Volume 26:Number 1(2019)
• Page Start: 302
• Page End: 317
• Publication Date: 2017-02-07
• Subjects: multi‐objective pick‐up and delivery problem -- pollution‐routing problem -- green logistics -- routing carbon footprint; Operations research -- Periodicals; 003
• DOI: 10.1111/itor.12376
• Languages: English
• ISSNs: 0969-6016

Abstract

Abstract: The design of sustainable logistics solutions poses new challenges for the study of vehicle‐routing problems. The design of efficient systems for transporting products via a heterogeneous fleet of vehicles must consider the minimization of cost, emissions of greenhouse gases, and the ability to serve every customer within an available time slot. This phenomenon gives rise to a multi‐objective problem that considers the emission of greenhouse gases, the total traveling time, and the number of customers served. The proposed model is approached with an ε ‐constraint technique that allows small instances to be solved and an evolutionary algorithm is proposed to deal with complex instances. Results for small instances show that all the points that approach the Pareto frontier found by the evolutionary algorithm are nondominated by any solution found by the multi‐objective model. For complex instances, nondominated solutions that serve most of the requests are found with low computational requirements.