Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty
Computers and Industrial Engineering
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We analyze the importance of practical aggregate production planning (APP) under uncertain environment and present a novel fuzzy programming resolution model based on the credibility measure of fuzzy event. The customers’ demands, the unit profits and the available manufacturing capacities are all regarded as fuzzy variables; three types of common constraints are re-formulated as fuzzy chance constraints. Considering that the generalized fuzzy optimizing model is difficult to solve, we deduce an equivalent transformation technique on the assumption that the fuzzy variables obey trapezoidal distributions. In this way we convert these models into a equivalent crisp linear programming model which can be rapidly solved by the Simplex algorithm. Finally, we employ this approach on a real-world fuzzy APP problem in an automobile pressing company. The results demonstrate the effectiveness and applicability of the proposed algorithm.