Models for solid transportation problems in logistics using particle swarm optimisation algorithm and genetic algorithm

Abhijit Baidya, Uttam Kumar Bera and Manoranjan Maiti

International Journal of Logistics Systems and Management, 2017, vol. 27, issue 4, 487-526

Abstract: Transportation policy seeks to improve agency freight and cargo management and enhance sustainable, efficient and effective transportation operations. In this paper, four new fuzzy fixed charge solid transportation problems (FFCSTP) are formulated to maximise the total profit and minimise the total cost. The interval objective function is approximated to an intervalvalued function, i.e., transformed to a single objective using weighted sum method and weighted multiplication method. The fuzzy constraints are converted to its equivalent deterministic form using different interval order relations. Genetic algorithm (GA) and particle swarm optimisation (PSO) algorithm are used to obtain the optimal transportation schedule for the proposed solid transportation problem. During the evaluation of the models, in one case, limitation on the transported amounts is imposed and in other case, no such limitation is used. The models are illustrated with numerical examples and the optimum results of the models are compared.

Keywords: fuzzy fixed charge solid transportation problem; weight and volume constraint; budget constraint; interval order relations; genetic algorithm; particle swarm optimisation algorithm. (search for similar items in EconPapers)
Date: 2017
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