applying genetic algorithm for an integrated supply and production/distribution planning in assembly systems

abstract: an efficient supply chain system operates under a strategy to minimize costs by integrating the different functions inside the system and by meeting customer demands in time. in this paper, an integrated supply-production and distribution planning (spdp) is considered despite the fact that in most of the papers, part of the supply chain has been studied, not all parts. therefore, we develop a mathematical model that calculate the optimal inventory lot sizing for each supplier and minimize the total cost associated in the process of procuring raw material, transferring and holding raw materials, manufacturing and, finally, delivering the finished product. the problem is formulated as a pure integer programming and heuristic genetic algorithm (ga) method applied to solve it. then we test the proposed model in a case study conducted in iran. experimental results show that such a model can reduce the costs of the case study by 8/4694%.