Replenishment policy based on modified ant colony optimisation and statistical analysis under the pre-order penetration point

Authors:
Jui-Tsung Wong;Chwen-Tzeng Su;Kuei-Hsien Chen
Affiliations:
Department of International Business, Shih Chien University Kaohsiung Campus, Kaohsiung, Taiwan, Republic of China;Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Taiwan, Republic of China;Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Taiwan, Republic of China
Venue:
International Journal of Computer Mathematics
Year:
2009

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Abstract

In the supply chain, most businesses in the pre-order penetration point (pre-OPP) operate under the forecast-driven mode, so that the decisions regarding inventory are made in accordance with the forecast and replenishment planning. This paper considers the stochastic dynamic lot-sizing problem of the two-phased transportation cost, service level constraint, and cash flow under a non-deterministic demand. This problem includes a nonlinear integer programming sub-problem. Therefore, this paper proposes an optimisation replenishment policy method based on modified ant colony optimisation (ACO) and response surface methodology. The main differences between the modified ACO and the traditional ACO lie in the modified update of pheromone intensity and the dynamic mutation operator. The experimental result shows that when the demand is normal distribution, the proposed approach, successfully finds the stationary point of minimum response. Besides, in the test of the algorithm solution quality, the modified ACO is better than the traditional ACO in all scenarios.