A simulation based heuristic discrete particle swarm algorithm for generating integrated production–distribution plan

• Published in: Applied soft computing Vol. 12; no. 9; pp. 3034 - 3050
• Main Authors: Varthanan, P. Ashoka, Murugan, N., Kumar, G. Mohan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2012
• Subjects: Aggregate production–distribution plan; Demand; Discrete particle swarm optimization; Simulation-optimization; Demand; Discrete particle swarm optimization; Simulation-optimization; Aggregate production–distribution plan
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2012.05.001

. [Display omitted] ► An innovative simulation based heuristic DPSO algorithm is proposed in this research for solving stochastic demand problems. ► The proposed simulation based heuristic DPSO algorithm handles the constraints effectively by means of a regenerative technique. ► The regenerative approach of handling constraints is compared with the Deb's efficient constraint handling methodology and found to be superior. ► Experimental design methodology is used for determining the best parameter combination of DPSO algorithm. Deciding the strategy for production and distribution in a stochastic demand scenario is important for the manufacturing industries. An integrated production–distribution plan considering regular, overtime and outsourced production costs along with inventory holding, backorder, hiring/laying-off and trip-wise distribution costs is developed for a renowned bearing manufacturing industry producing three types of products at three locations. Demand is assumed to vary uniformly and a novel simulation based heuristic discrete particle swarm optimization (DPSO) algorithm is used for obtaining the best production–distribution plan that serves as a trade-off between holding inventory and backordering products. The algorithm also uses an innovative regeneration type constraint handling method which does not require a penalty operator. In addition to the bearing manufacturing industry data set, two other test data sets are also solved. The simulation based optimization approach gives good approximate solutions for the stochastic demand problems.