An artificial intelligence approach for fuzzy possibilistic-stochastic multi-objective logistics network design
In this paper, a logistics network is investigated which includes multi-suppliers, collection centers, transfer stations, treatment stations, and products. For this purpose, a multi-objective mathematical programming model is proposed that minimizes the total costs including the fixed costs for opening facilities and transportation costs between facilities, minimizes the distance between each waste-generating facilities and transfer stations, maximizing the distance between treatment and disposal stations and customer zones, and maximizes the sum of the reliability of coverage for the potential facilities which will be open. In order to make the results of this paper more realistic, a case study in the iron and steel industry has been investigated. Besides, a new solution approach is proposed by combining fuzzy possibilistic programming, stochastic programming, and fuzzy multi-objective programming. Moreover, an imperialist competitive algorithm is proposed to obtain near optimal solution in comparison with other evolutionary algorithms. Finally, computational experiments are provided to demonstrate the applicability and suitability of the proposed model and solution approaches.
fuzzy possibilistic programming stochastic programming facility location undesirable facility imperialist competitive algorithm (ica)