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Integrating a Weighted Additive Multiple Objective Linear Model with Possibilistic Linear Programming for Fuzzy Aggregate Production Planning Problems

Integrating a Weighted Additive Multiple Objective Linear Model with Possibilistic Linear Programming for Fuzzy Aggregate Production Planning Problems

Navee Chiadamrong, Noppasorn Sutthibutr
Copyright: © 2020 |Volume: 9 |Issue: 2 |Pages: 30
ISSN: 2156-177X|EISSN: 2156-1761|EISBN13: 9781522598428|DOI: 10.4018/IJFSA.2020040101
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MLA

Chiadamrong, Navee, and Noppasorn Sutthibutr. "Integrating a Weighted Additive Multiple Objective Linear Model with Possibilistic Linear Programming for Fuzzy Aggregate Production Planning Problems." IJFSA vol.9, no.2 2020: pp.1-30. http://doi.org/10.4018/IJFSA.2020040101

APA

Chiadamrong, N. & Sutthibutr, N. (2020). Integrating a Weighted Additive Multiple Objective Linear Model with Possibilistic Linear Programming for Fuzzy Aggregate Production Planning Problems. International Journal of Fuzzy System Applications (IJFSA), 9(2), 1-30. http://doi.org/10.4018/IJFSA.2020040101

Chicago

Chiadamrong, Navee, and Noppasorn Sutthibutr. "Integrating a Weighted Additive Multiple Objective Linear Model with Possibilistic Linear Programming for Fuzzy Aggregate Production Planning Problems," International Journal of Fuzzy System Applications (IJFSA) 9, no.2: 1-30. http://doi.org/10.4018/IJFSA.2020040101

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Abstract

This study uses an integrated optimization method by applying a weighted additive multiple objective linear model with Possibilistic Linear Programming (PLP) to fuzzy Aggregate Production Planning (APP) problems under an uncertain environment. The uncertainty conditions include uncertainties of operating times and costs, customer demand, labor level, as well as machine capacity. The aim of this study is to minimize total costs of the plan that consist of the production cost and costs of changes in labor level. The proposed hybrid approach minimizes the most possible value of the imprecise total costs, maximizes the possibility of obtaining lower total costs, and minimizes the risk of obtaining higher total costs from PLP as multiple objectives for the fuzzy multiple objective linear model optimization. The outcome of the proposed approach shows that the solution is closer to the ideal solution obtained from Linear Programming than a typical solution obtained from PLP. There is also a higher overall satisfaction value.

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