A fuzzy multi-objective optimization model for recoverable manufacturing systems in uncertain environments

References

• Koh SG, Hwang H, Shon KI, et al. An optimal ordering and recovery policy for reusable items. Comput Ind Eng. 2002;43:59–73.
   Web of Science ®Google Scholar
• Lee WC, Wu JW, Lei CL. Computational algorithmic procedure for optimal inventory policy involving ordering cost reduction and back-order discounts when lead time demand is controllable. Appl Math Comput. 2007;189:186–200.
   Web of Science ®Google Scholar
• Pentico DW. An evaluation and proposed modification of the Sepheri-Sliver-New heuristic for multip lot sizing under variable yield. IEE Trans. 1988;20:360–363.
   Web of Science ®Google Scholar
• Liang TF. Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains. Inf Sci. 2011;181:842–854.
   Web of Science ®Google Scholar
• Liang TF. Application of interactive possibilistic linear programming to aggregate production planning with multiple imprecise objectives. Prod Plann Control. 2007;18:548–560.
   Web of Science ®Google Scholar
• Liang TF. Application of possibilistic linear programming to multi-objective distribution planning decisions. J Chin Inst Ind Eng. 2007;24:97–109.
   Google Scholar
• Liang TF. Applying fuzzy goal programming to project management decisions with multiple goals in uncertain environments. Expert Syst Appl. 2010;37:8499–8507.
   Web of Science ®Google Scholar
• Liang TF. Imprecise aggregate production planning decisions using interactive possibilistic linear programming. J Stat Manage Syst. 2007;10:451–472.
   Google Scholar
• Yang MF, Lin Y. Applying fuzzy multi-objective linear programming to project management decisions with the interactive two-phase method. Comput Ind Eng. 2013;66:1061–1069.
   Web of Science ®Google Scholar
• Feng Y, Viswanathan S. A new lot-sizing heuristic for manufacturing systems with product recovery. Int J Prod Econ. 2011;133:432–438.
   Web of Science ®Google Scholar
• Li Y, Chen J, Cai X. Heuristic genetic algorithm for capacitated production planning problems with batch processing and remanufacturing. Int J Prod Econ. 2007;105:301–317.
   Web of Science ®Google Scholar
• Tsai WJ, Lin WR, Fan YW, et al. Applying a mathematical programming approach for a green product mix decision. Int J P Res. 2012;50:1171–1184.
   Web of Science ®Google Scholar
• Richter K, Weber J. The reverse Wagner/Whitin model with variable manufacturing and remanufacturing cost. Int J Prod Econ. 2001;71:447–456.
   Web of Science ®Google Scholar
• Van der Laan EA, Teunter RH. Simple heuristics for push and pull remanufacturing policies. Eur J Oper Res. 2006;175:1084–1102.
   Web of Science ®Google Scholar
• Konstantaras I, Skouri K. Lot sizing for a single product recovery system with variable setup numbers. Eur J Oper Res. 2010;203:326–335.
   Web of Science ®Google Scholar
• Kilic HS, Cebeci U, Ayhan MB. Reverse logistics system design for the waste of electrical and electronic equipment (WEEE) in Turkey. Resour Conserv Recycl. 2015;95:120–132.
   Web of Science ®Google Scholar
• Zadeh LA. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1978;1:3–28.
   Google Scholar
• Bellman RE, Zadeh LA. Decision-making in a fuzzy environment. Manage Sci. 1970;17:141–164.
   Web of Science ®Google Scholar
• Zimmermann HJ. Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst. 1978;1:45–56.
   Google Scholar
• Zadeh LA. Fuzzy set. Inf Control. 1965;8:38–53.
   Google Scholar
• Wang RC, Liang TF. Applying possibilistic linear programming to aggregate production planning. Int J Prod Econ. 2005;98:328–341.
   Web of Science ®Google Scholar
• Yaghoobi MA, Tamiz M. A short note on the relationship between goal programming and fuzzy programming for vectormaximum problem. Iran J Fuzzy Syst. 2005;2:31–36.
   Google Scholar
• Torabi SA, Hassini E. An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets Syst. 2008;159:193–214.
   Web of Science ®Google Scholar
• Sakallı ÜS, Baykoç ÖF, Birgören B. A possibilistic aggregate production planning model for brass casting industry. Prod Plann Control. 2010;21:319–338.
   Web of Science ®Google Scholar
• Vahdani B, Jolai F, Tavakkoli-Moghaddam R, et al. Two fuzzy possibilistic bi-objective zero-one programming models for outsourcing the equipment maintenance problem. Eng Optim. 2012;44:801–820.
   Web of Science ®Google Scholar
• Zangiabadi M, Maleki HR. Fuzzy goal programming technique to solve multiobjective transportation problems with some non-linear membership funtions. Iran J Fuzzy Syst. 2013;10:61–74.
   Web of Science ®Google Scholar
• Niakan F, Rahimi M. A multi-objective healthcare inventory routing problem; a fuzzy possibilistic approach. Transp Res Part E. 2015;80:74–94.
   Google Scholar
• Chakrabortty RK, Hasin MAA, Sarker RA, et al. A possibilistic environment based particle swarm optimization for aggregate production planning. Comput Ind Eng. 2015;88:366–377.
   Web of Science ®Google Scholar
• Kumral M, Sari YA. An application of possibilistic programming to production sequencing of mining parcels. Inte J Manage Sci Eng Manage. 2016. DOI:10.1080/17509653.2016.1159147
   Google Scholar
• Lai YJ, Hwang CL. A new approach to some possibilistic linear programming problems. Fuzzy Sets Syst. 1992;49:121–133.
   Web of Science ®Google Scholar
• Özgen D, Önüt S, Gülsün B, et al. A two-phase possibilistic linear programming methodology for multi-objective supplier evaluation and order allocation problems. Inf Sci. 2008;178:485–500.
   Web of Science ®Google Scholar