Multi-Objective Fixed-Charge Transportation Problem with Random Rough Variables

作     者：Roy, Sankar Kumar Midya, Sudipta Yu, Vincent F. 

作者机构：Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Midnapore 721102 W Bengal India Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Keelung Rd Taipei 10607 Taiwan 

出 版 物：《INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS》 (国际不确定性、模糊性及基于知识的系统杂志)

年 卷 期：2018年第26卷第6期

页      面：971-996页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Fixed-charge transportation problem rough and random rough variables rough programming fuzzy programming global criterion method epsilon-constrained method 

摘      要：This paper considers a multi-objective fixed-charge transportation problem (MOFCTP) in which the parameters of the objective functions are random rough variables, while the supply and the demand parameters are rough variables. In real-life situations, the parameters of a multi-objective fixed-charge transportation problem may not be defined precisely, because of globalization of the market, uncontrollable factors, etc. As such, the multi-objective fixed-charge transportation problem is proposed under rough and random rough environments. To tackle uncertain (rough and random rough) parameters, the proposed model employs an expected value operator. Furthermore, a procedure is developed for converting the uncertain multi-objective fixed-charge transportation problem into a deterministic form and then solving the deterministic model. Three different methods, namely, the fuzzy programming, global criterion, and epsilon-constrained methods, are used to derive the optimal compromise solutions of the suggested model. To provide the preferable optimal solution of the formulated problem, a comparison is drawn among the optimal solutions that are extracted from different methods. Herein, the epsilon-constrained method derives a set of optimal solutions and generates an exact Pareto-front. Finally, in order to show the applicability and feasibility of the proposed model, the paper includes a real-life example of a multi-objective fixed-charge transportation problem. The main contribution of the paper is that it. deals with MOFCTP using two types of uncertainties, thus making the decision making process more flexible.