A generic framework for multi-criteria decision support in eco-friendly urban logistics systems |
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| Affiliation: | 1. Computational Intelligence Lab, School of Computer Engineering, Nanyang Technological University, Singapore 639798;1. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran;2. Department of Electrical and Computer Engineering, Thompson Engineering Building, Western University, London, Ontario N6A 5B9, Canada;3. Department of Medical Biophysics, Medical Sciences Building, Western University, London, Ontario N6A 5C1, Canada;4. Imaging Research Laboratories, Robarts Research Institute (RRI), Western University, 1151 Richmond St. N., London, Ontario N6A 5B7, Canada;1. FICA, Universidad de las Américas, Quito, Ecuador;2. FACI, Universidad Estatal de Milagro, Ecuador;3. Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain;4. ETSII, Universidad Rey Juan Carlos, 28933 Madrid, Spain;1. Cognitum, Warsaw, Poland;2. Gdansk University of Technology, Gdansk, Poland;3. Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology in Warsaw, Warsaw, Poland |
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| Abstract: | In this paper, we present a multi-objective shortest path evolutionary algorithm for comprehensive solutions to real-world manifestations of the classical vehicle routing problem. The shift from being a purely academic pursuit is highlighted by the introduction of a generic optimization framework which accommodates a variety of attributes that commonly occur in industrial applications. Specifically, the paper's main contribution are as follows: (1) consideration for the following real-world constraints: (a) time windows at customer locations, (b) simultaneous pickup and delivery demands, (c) a heterogeneous fleet of vehicles, and (d) the heterogeneity of traffic congestion levels in urban transportation networks; (2) assimilation of all the above attributes into a multi-objective program which aims to minimize environmental impact, while simultaneously addressing the overall operational costs of the routing solution and service quality concerns; a feat that has not been fully realized by known intelligent systems according to the authors’ best knowledge. In order to showcase the efficacy of the proposed algorithm, it is first tested on existing benchmark instances and then applied on a pair of real-world industrial examples from Singapore. These industrial examples serve as a source of new benchmarks which facilitate the study of different routing constraints and their effects on the economic and environmental viability of urban logistics systems. |
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