Imprecise aggregate production planning decisions using interactive possibilistic linear programming

2007 ◽  
Vol 10 (3) ◽  
pp. 451-472 ◽  
Author(s):  
Tien-Fu Liang
2020 ◽  
Vol 9 (2) ◽  
pp. 1-30
Author(s):  
Navee Chiadamrong ◽  
Noppasorn Sutthibutr

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.


Author(s):  
Halit Alper Tayali

The aggregate production planning model aims to match the supply with demand while minimizing the manufacturing or production activity costs. There are many methods in the mathematical programming theory to solve the aggregate production planning problem. This chapter develops a novel decision support system for the aggregate production planning model using the linear programming approach. The aggregate production problem modeled by the linear programming has been coded in R computer programming language, and a novel web application has been developed using Shiny to serve the needs of the production managers. The novel application is adjustable for any production setting and planning horizon for firms in global transitioning.


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