Implementation of Particle Swarm Intelligence Within Inventory Control for an Electrical Industry: A Case Study
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Abstract
Optimal inventory management is a major issue in many companies and may impact their productivity and profitability. Our research targets an electrical energy facility in Iraq to find optimal quantities and inventory costs that reduce total inventory costs, including ordering, holding, transportation, and inspection. The research compares classical particle swarm optimization (CPSO) with modified particle swarm optimization (MPSO). The artificial neural network analyzes the relationship between input and output to form a cost function. The MPSO technique produced better cost savings than CPSO or traditional methods (EOQ). The traditional economic order quantity management system produced total costs of $7,486,304.80, but CPSO cut them to $5,414,100 while MPSO lowered them to $2,418,000 when controlling 20 electrical items. The results show that MPSO achieves better than traditional methods and CPSO in lowering expenses and improving inventory handling. Sensitivity analyses are carried out on some important parameters, and the changes in the objective function are investigated. Finally, the experimental results verify MSPO’s good performance.
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