Application of particle swarm optimisation for solving deteriorating inventory model with fluctuating demand and controllable deterioration rate

• Published in: International journal of systems science Vol. 44; no. 6; pp. 1026 - 1039
• Main Authors: Chen, Yu-Ren, Dye, Chung-Yuan
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis Group 01.06.2013; Taylor & Francis
• Subjects: Applied sciences; Demand; Deterioration; Exact sciences and technology; Inventories; inventory; Inventory control, production control. Distribution; Marketing; Operational research and scientific management; Operational research. Management science; Optimization; particle swarm optimisation; Preservation; preservation technology investment; Reliability theory. Replacement problems; Replenishment; Stockpiling; time-varying demand; Exogenous; Costs; Preservation; Refeeding; particle swarm optimisation; Retail marketing; time-varying demand; Replacement problem; Capital equipment; Markets; Non linear programming; Market survey; inventory; Modeling; Particle swarm optimization; Time varying system; Optimal strategy; deterioration; Replenishment; preservation technology investment; Inventory control; Planning; By product; Investment
• ISSN: 0020-7721; 1464-5319
• DOI: 10.1080/00207721.2011.652224

In most of the inventory models in the literature, the deterioration rate of goods is viewed as an exogenous variable, which is not subject to control. In the real market, the retailer can reduce the deterioration rate of product by making effective capital investment in storehouse equipments. In this study, we formulate a deteriorating inventory model with time-varying demand by allowing preservation technology cost as a decision variable in conjunction with replacement policy. The objective is to find the optimal replenishment and preservation technology investment strategies while minimising the total cost over the planning horizon. For any given feasible replenishment scheme, we first prove that the optimal preservation technology investment strategy not only exists but is also unique. Then, a particle swarm optimisation is coded and used to solve the nonlinear programming problem by employing the properties derived from this article. Some numerical examples are used to illustrate the features of the proposed model.