Fuzzy multi-objective procurement/production planning decision problems for recoverable manufacturing systems. (October 2015)

• Record Type: Journal Article
• Authors: Su, Tai-Sheng; Lin, Yu-Fan
• Is Part Of: Journal of manufacturing systems. Volume 37:Part 1(2015)
• Page Start: 396
• Page End: 408
• Publication Date: 2015-10
• Subjects: Lot-sizing -- Fuzzy multi-objective linear programming -- Recoverable remanufacturing systems -- Procurement/production planning; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2014.07.007
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Fuzzy multi-objective linear programming model. Procurement/production planning decision. Recoverable manufacturing systems. Abstract: In actual lot-sizing production-to-order problems for recoverable remanufacturing systems, input data or parameters are often imprecise or fuzzy. This study develops a novel fuzzy multi-objective linear programming (FMOLP) model with a piecewise linear membership function to solve integrated, procurement/production, planning decision problems with fuzzy environments and deal with multi-component, multi-vendor, multi-source and multi-machines under recoverable remanufacturing systems. The initial FMOLP model developed in this study attempts to simultaneously minimize total costs and total lead times in relation to supplier capacity, lead time, lot release, machine yield and customer demand. The proposed FMOLP model provides a systematic framework that facilitates a fuzzy decision-making process, enabling a decision maker to interactively adjust search direction during the solution procedure to obtain the preferred satisfactory solution. To test the model's adequacy, an actual implementation of several scenarios was conducted using remanufacturing production systems. The analytical results presented in this study can help decision managers better understand systematic analysis and the potential for improving cost-effectiveness and lead time in terms of recoverable remanufacturing planning.