| Summary: | 碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 100 === A Production-Distribution Integer Model with Item Substitution Strategy（PDISS）has been proposed in the supply chain literature. The model aims to find optimization combinations of item substitutions in order to maximize the total profit on supply chain. Existing heuristic algorithms for the PDISS model have lots rooms to improve in terms of the solution quality and the stability of the solution quality. The main reason that leads to poor solution quality for existing heuristic algorithm is that the algorithms often decide not to produce products even though the supply over the demand, which leads to excessive stock out penalty.
This study proposes a Particle Swarm Optimization（PSO）algorithm to solve PDISS model. PSO algorithms feature with memorization and fast convergence to iteratively search optimal solution in problem space. The first step of the proposed PSO is to initializes the velocity and position of each particle. The second step is to evaluate each particle’s fitness according to its position in the problem space. The third step updates velocity and position of each particle considering the global and personal best positions. The fourth step converts each particle’s position to a solution in the problem space that represents decisions for substituting products and components. In this step, this study proposes two mechanisms for the conversion. After the step four, the PSO algorithm goes back to step two and iterate until a stop condition is met.
Three sizes of supply chain are established for evaluating the proposed PSO algorithm. Two performance indexes are considered: the solution quality, and the computation time. The optimal solutions of the PDISS problem instances will be used as benchmarks for obtaining the performance indexes. Lingo optimization software will be used to solve the PDISS problem instances.
Experiment results showed that the best GAP value was 10.58% and the worst value was 14.26% for the large supply chain; the best GAP value was 4.40% and worst value was 4.98% for the middle supply chain; the best value was 9.30% and worst value was 9.56% for the small supply chain. As for the computation time, the best IMP value was 82.743 and worst value is 39.086 in
III
the large supply chain for the large supply chain; the best value was 1.64 and worst value was 0.693 for the middle supply chain; the best value was 0.006 and worst value was 0.003 for small supply chain.
|
|---|
