Name
Abstract | ||
|---|---|---|
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, Out-of-the-Box Star-Topology Full-Model PSO, and a Gaussian-based Star-Topology Full-Model PSO with the Constriction Coefficient. A secondary test-suite was also developed to test the effectiveness of the best algorithms on the problem. With respect to the client-mandated and the developed test suite's fitness threshold and maximum number of function evaluations, the best algorithm had an 87% and 90% success rate, respectively. Considering the flexibility and high performance of the solution and the generality of the problem, these results represent a significant contribution to commercial wholesaling. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1593105.1593234 | ACM Southeast Regional Conference 2005 |
Keywords | Field | DocType |
acceptable lead time cost,star-topology full-model pso,developed test suite,management problem,evolutionary computation,client-mandated test-suite,gaussian-based star-topology full-model pso,best algorithm,multiple retailer supply chain,probabilistic dual-topology full-model pso,evolutionary programming,out-of-the-box star-topology full-model pso,supply chain management,genetic algorithm,data mining,java,xhtml,xsl,xml,limiting factor,evolutionary computing,hacker,screen scraping,estimation of distribution algorithm,html | Test suite,Particle swarm optimization,Data mining,Estimation of distribution algorithm,Computer science,Lead time,Supply chain management,Probabilistic logic,Evolutionary programming,Genetic algorithm | Conference |
Citations | PageRank | References |
1 | 0.35 | 2 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Caio Soares | 1 | 11 | 2.61 |
| Gerry V. Dozier | 2 | 326 | 44.63 |
| Emmett Lodree | 3 | 12 | 1.29 |
| Jared Phillips | 4 | 10 | 1.65 |
| Katie Nobles | 5 | 1 | 0.35 |
| Yong won Park | 6 | 1 | 0.35 |