A five-phase combinatorial approach for solving a fuzzy linear programming supply chain production planning problem

Abstract

AbstractSupply Chain Production Planning (SCPP) is a core value of operation management that affects organization performance and market competitiveness. In the presence of increasing competitive market pressure, firms need to look for a surviving way to improve themselves by attacking several goals simultaneously to gain competitive advantages. Therefore, a practical approach that can handle two main obstacles, i.e. conflicting objectives and an uncertain environment, is needed to assist Decision Makers (DMs) in planning an efficient SCPP. To tackle SCPP problems, a five-phase combinatorial approach is proposed to overcome not only these two main obstacles but also several weak points of traditional Fuzzy Linear Programming (FLP). The five-phase combinatorial approach is developed by integrating the application of Intuitionistic Fuzzy Linear Programming (IFLP), Realistic Robust Programming (RRP), Chance-Constrained Programming (CCP), and Augmented Epsilon Constraint (AUGMECON). Then, a case study of SCPP is performed using this approach by aiming to minimize total supply chain costs, minimize shortages of products, and maximize total values of purchasing where operating costs, customer demand, defective rate, and service level are imprecise. The performance of the proposed approach shows to outperform the traditional FLP approach in terms of hesitation allowance, robust modeling, satisfaction and non-satisfaction levels consideration, and providing a set of strong Pareto optimal solutions. These benefits help DMs to obtain the best compromise solution that is more robust and concrete as well as reflects more intention of DMs.

Keywords

• Supply chain production planning
• triangular intuitionistic fuzzy set
• intuitionistic fuzzy linear programming
• robust possibilistic chance-constrained programming
• augmented epsilon constraint
• Zhou Zude, Wuhan University of Technology, China