Contemporary Statistics: A Computer Approach
Contemporary Statistics: A Computer Approach
A Supply Network Model with Base-Stock Control and Service Requirements
Operations Research
Development of a Rapid-Response Supply Chain at Caterpillar
Operations Research
Proceedings of the 35th conference on Winter simulation: driving innovation
Multicriterion genetic optimization for due date assigned distribution network problems
Decision Support Systems - Special issue: Collaborative work and knowledge management
Strategic level three-stage production distribution planning with capacity expansion
Computers and Industrial Engineering
A reactive GRASP and path relinking for a combined production-distribution problem
Computers and Operations Research
Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management
Information Sciences: an International Journal
A particle swarm optimization algorithm for the multiple-level warehouse layout design problem
Computers and Industrial Engineering
A Pareto archive particle swarm optimization for multi-objective job shop scheduling
Computers and Industrial Engineering
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
Hybrid genetic algorithm for multi-time period production/distribution planning
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
Two-layer particle swarm optimization for unconstrained optimization problems
Applied Soft Computing
Application of particle swarm optimization to association rule mining
Applied Soft Computing
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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.