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Heuristics for a class of vehicle routing problem in green logistics

posted on 22.02.2017 by Teoh, Boon Ean
The increasing pressure to move towards sustainable development has encouraged many organizations to prioritize sustainable growth, especially the transportation sector since it is one of the highest contributors of greenhouse gas emissions. With the current economic growth, there is an increase demand in freight services. This leads to the need of thorough and proper planning of shipment routes. Optimization of the fleet structure is necessary in order to cope with the growing number of customers. A well planned journey is vital in reducing the transportation cost and risk to the environment. This thesis presents the development of the differential evolution algorithm with local search for solving the capacitated vehicle routing problem. The capacitated vehicle routing problem is a classical vehicle routing problem with additional constraint where the capacity of the vehicle travelling on a specific route cannot exceed the maximum vehicle capacity. Local search techniques help to refine the solutions found. The proposed differential evolution algorithm with local search is tested on capacitated vehicle routing problem instances described by Augerat et al. and Christofides & Eilon. The proposed differential evolution algorithm with local search approach generate quality solutions for the benchmark problems tested and are comparable to the algorithms reported in the literature. The second part of this thesis explores the field of multi-objective optimization problem with green logistics. The differential evolution algorithm with local search is expanded to consider multi-objective problems. Hence, a multi-objective differential evolution algorithm is presented to solve the capacitated vehicle routing problem by minimizing the hazardous risk and greenhouse gas emissions. The proposed algorithm incorporates differential evolution algorithm with Pareto ranking and crowding distance techniques. A series of Pareto Front are provided as the solution to the multi-objective capacitated vehicle routing problem on capacitated vehicle routing problem instances described by Augerat et al. The decision-maker can then consider the trade-off and choose from the set of optimal solutions in the Pareto Front. Computational results found proved the viability of the multi-objective differential evolution algorithm to solve the multi-objective problem with a certain trade-off to achieve an efficient and feasible route.


Campus location


Principal supervisor

S.G. Ponnambalam

Year of Award


Department, School or Centre

Engineering Science (Monash University Malaysia)


Faculty of Engineering