Optimization of the multiple retailer supply chain management problem

With stock surpluses and shortages representing one of the greatest elements of risk to wholesalers, a solution to the multiretailer supply chain management problem would result in tremendous economic benefits. In this problem, a single wholesaler with multiple retailer customers must find an optimal balance of quantities ordered from suppliers and acceptable lead time costs, while taking into account limiting factors such as the time each retailer will wait for a backorder. The following four evolutionary computations (EC) are utilized to find a solution: evolutionary programming (EP), genetic algorithms (GA), particle swarm optimizers (PSO), and estimation of distribution algorithms (EDA). In addition, problem-specific modifications to each are created. Of the 32 attempted algorithms, the following proved to be best with respect to the client-mandated test-suite: Probabilistic Dual-Topology Full-Model PSO, Star-Topology Full-Model PSO using dynamically-adjusting learning rates, Outo...

• Type: Conference
• Year: 2008
• Where: ACMSE
• Authors: Caio Soares, Gerry V. Dozier, Emmett Lodree, Jared Phillips, Katie Nobles, Yong won Park
• Keywords: ACMSE 2008, Algorithms, Dual-Topology Full-Model PSO, Star-Topology Full-Model PSO, Theoretical Computer Science