Stochastic dynamic lot-sizing problem using bi-level programming base on artificial intelligence techniques |
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Authors: | Jui-Tsung Wong Chwen-Tzeng SuChun-Hsien Wang |
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Affiliation: | a Department of International Business Management, Shih Chien University, Neimen Shiang, Kaohsiung, Taiwan, Republic of China b Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Touliu, Yunlin, Taiwan, Republic of China c Department of Bio-industry and Agribusiness Administration, National Chiayi University, Chiayi, Taiwan, Republic of China |
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Abstract: | Simulation is generally used to study non-deterministic problems in industry. When a simulation process finds the solution to an NP-hard problem, its efficiency is lowered, and computational costs increase. This paper proposes a stochastic dynamic lot-sizing problem with asymmetric deteriorating commodity, in which the optimal unit cost of material and unit holding cost would be determined. This problem covers a sub-problem of replenishment planning, which is NP-hard in the computational complexity theory. Therefore, this paper applies a decision system, based on an artificial neural network (ANN) and modified ant colony optimization (ACO) to solve this stochastic dynamic lot-sizing problem. In the methodology, ANN is used to learn the simulation results, followed by the application of a real-valued modified ACO algorithm to find the optimal decision variables. The test results show that the intelligent system is applicable to the proposed problem, and its performance is better than response surface methodology. |
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Keywords: | Lot-sizing problem Deteriorating commodity Artificial neural network Ant colony optimization Simulation |
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