Multi-objective multi-item fixed-charge solid transportation problem under twofold uncertainty

• Published in: Neural computing & applications Vol. 31; no. 12; pp. 8593 - 8613
• Main Authors: Roy, Sankar Kumar, Midya, Sudipta, Weber, Gerhard-Wilhelm
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2019; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Fuzzy systems; Goal programming; Image Processing and Computer Vision; Multiple objective analysis; Original Article; Probability and Statistics in Computer Science; Transportation problem; Weighted goal programming; Fuzzy programming; Multi-objective programming; Fuzzy-rough variable; Twofold uncertainty; TOPSIS; Fixed-charge solid transportation problem
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-019-04431-2

In this paper, we investigate a multi-objective multi-item fixed-charge solid transportation problem (MOMIFCSTP) with fuzzy-rough variables as coefficients of the objective functions and of the constraints. The main focus of the paper is to analyze MOMIFCSTP under a fuzzy-rough environment for a transporting system. In practical situations, the parameters of a MOMIFCSTP are imprecise in nature, due to several uncontrollable factors. For these reasons, we introduce the fuzzy-rough variables in MOMIFCSTP to tackle vague data which are different from fuzziness and roughness. Fuzzy-rough expected-value operator is employed to convert fuzzy-rough MOMIFCSTP into deterministic MOMIFCSTP. Thereafter, we develop a methodology to solve the deterministic MOMIFCSTP by technique for order preference by similarity to ideal solution (TOPSIS). Three distinct approaches, namely extended TOPSIS, weighted goal programming (WGP) and fuzzy programming, are used to derive Pareto-optimal solution from the suggested model. A comparison is drawn among the optimal solutions which are derived from different approaches. It is observed from the extracted results that TOPSIS provides a better optimal solution than WGP and fuzzy programming. TOPSIS also overcomes some difficulties which arise in WGP. Finally, a real-world (industrial) problem is incorporated to show the applicability and feasibility of the proposed problem.