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A multi-objective joint replenishment inventory model of deteriorated items in a fuzzy environment

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  • Wee, Hui-Ming
  • Lo, Chien-Chung
  • Hsu, Ping-Hui

Abstract

In this study, a fuzzy multi-objective joint replenishment inventory model of deteriorating items is developed. The model maximizes the profit and return on inventory investment (ROII) under fuzzy demand and shortage cost constraint. We propose a novel inverse weight fuzzy non-linear programming (IWFNLP) to formulate the fuzzy model. A soft computing, differential evolution (DE) with/without migration operation, is proposed to solve the problem. The performances of the proposed fuzzy method and the conventional fuzzy additive goal programming (FAGP) are compared. We show that the solution derived from the IWFNLP method satisfies the decision maker's desirable achievement level of the profit objective, ROII objective and shortage cost constraint goal under the desirable possible level of fuzzy demand. It is an effective decision tool since it can really reflect the relative importance of each fuzzy component.

Suggested Citation

  • Wee, Hui-Ming & Lo, Chien-Chung & Hsu, Ping-Hui, 2009. "A multi-objective joint replenishment inventory model of deteriorated items in a fuzzy environment," European Journal of Operational Research, Elsevier, vol. 197(2), pages 620-631, September.
    • Handle: RePEc:eee:ejores:v:197:y:2009:i:2:p:620-631
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    References listed on IDEAS

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    Cited by:

    1. Prasenjit Pramanik & Manas Kumar Maiti & Manoranjan Maiti, 2018. "An appropriate business strategy for a sale item," OPSEARCH, Springer;Operational Research Society of India, vol. 55(1), pages 85-106, March.
    2. Roya Tat & Ata Allah Taleizadeh & Maryam Esmaeili, 2015. "Developing economic order quantity model for non-instantaneous deteriorating items in vendor-managed inventory (VMI) system," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(7), pages 1257-1268, May.
    3. Bakker, Monique & Riezebos, Jan & Teunter, Ruud H., 2012. "Review of inventory systems with deterioration since 2001," European Journal of Operational Research, Elsevier, vol. 221(2), pages 275-284.
    4. Deshpande, Paras & Shukla, Deepak & Tiwari, M.K., 2011. "Fuzzy goal programming for inventory management: A bacterial foraging approach," European Journal of Operational Research, Elsevier, vol. 212(2), pages 325-336, July.
    5. Majumder, Pinki & Mondal, Sankar Prasad & Bera, Uttam Kumar & Maiti, Manoranjan, 2016. "Application of Generalized Hukuhara derivative approach in an economic production quantity model with partial trade credit policy under fuzzy environment," Operations Research Perspectives, Elsevier, vol. 3(C), pages 77-91.
    6. Cui, Ligang & Deng, Jie & Liu, Rui & Xu, Dongyang & Zhang, Yajun & Xu, Maozeng, 2020. "A stochastic multi-item replenishment and delivery problem with lead-time reduction initiatives and the solving methodologies," Applied Mathematics and Computation, Elsevier, vol. 374(C).
    7. Irfan Ali & Srikant Gupta & Aquil Ahmed, 2019. "Multi-objective linear fractional inventory problem under intuitionistic fuzzy environment," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(2), pages 173-189, April.
    8. Kumar Maiti, Manas, 2011. "A fuzzy genetic algorithm with varying population size to solve an inventory model with credit-linked promotional demand in an imprecise planning horizon," European Journal of Operational Research, Elsevier, vol. 213(1), pages 96-106, August.
    9. Wu, Meng & Zhu, Stuart X. & Teunter, Ruud H., 2013. "Newsvendor problem with random shortage cost under a risk criterion," International Journal of Production Economics, Elsevier, vol. 145(2), pages 790-798.

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