Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing problem

• Published in: Transportation research. Part D, Transport and environment Transport and environment, 2017-12, Vol.57, p.422-457
• Main Authors: Jabir, E., Panicker, Vinay V., Sridharan, R.
• Format: Article
• Language: English
• Subjects: Ant colony optimization; Environmental impact; Integer linear programming model; Variable neighbourhood search; Vehicle routing problem
• ISSN: 1361-9209; 1879-2340
• DOI: 10.1016/j.trd.2017.09.003

•Develop ILP models for the Capacitated Multi-Depot Green Vehicle Routing Problem.•Analyze the solution for an exact algorithm for a set of small scale instances.•Design ACO based and hybrid ACO-VNS based heuristics as solution methodologies.•Computational study shows the enhanced performance of hybrid algorithm.•The emission cost reduction model proves to be a direction sensitive model variant. The traditional distribution planning problem in a supply chain has often been studied mainly with a focus on economic benefits. The growing concern about the effects of anthropogenic pollutions has forced researchers and supply chain practitioners to address the socio-environmental concerns. This research study focuses on incorporating the environmental impact on route design problem. In this work, the aim is to integrate both the objectives, namely economic cost and emission cost reduction for a capacitated multi-depot green vehicle routing problem. The proposed models are a significant contribution to the field of research in green vehicle routing problem at the operational level. The formulated integer linear programming model is solved for a set of small scale instances using LINGO solver. A computationally efficient Ant Colony Optimization (ACO) based meta-heuristic is developed for solving both small scale and large scale problem instances in reasonable amount of time. For solving large scale instances, the performance of the proposed ACO based meta-heuristic is improved by integrating it with a variable neighbourhood search.