Supply and Production/Distribution Planning in Supply Chain with Genetic Algorithm

Supply and Production/Distribution Planning in Supply Chain with Genetic Algorithm

Babak Sohrabi (University of Tehran, Iran) and MohammadReza Sadeghi Moghadam (University of Tehran, Iran)
Copyright: © 2012 |Pages: 17
DOI: 10.4018/ijaie.2012010104
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The present study, using genetic algorithm, tries to improve material flow management in supply chain. Consequently, in this paper, an integrated supply-production and distribution planning (SPDP) is considered despite the fact that in most of the Iranian industrial firms, SPDP is done independently. The effective use of integrated SPDP not only enhances the performance rather decreases inventory cost, holding cost, shortage cost and overall supply chain costs. A quantitative mathematical model is used to the problem articulation, and then it is solved by applying heuristic genetic algorithm (GA) method. The proposed model with genetic algorithm could provide the best satisfactory result with the minimum cost. The reliability test was carried by comparing the model results with that of the amount of variables.
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2. Literature Review

Previous researchers came up with different models on supply chains, to which we came across during the course of literature review. These models can be categorized as:

  • Coordinated model of buyer-seller

  • Coordinated planning model of production-distribution

  • Coordinated planning model of production-invention

  • Model of location-allocation

  • Coordinated model of purchase-production-distribution

Chandra and Fisher (1994) proposed a coordinated planning model of production and distribution, in which products from a single production unit are transported to retailers directly from the plant. Significance of this model is the demand of retailers for each product in the specific period, however; this also minimizes the total costs consisting manufacturing, transportation and the inventories. Jayaraman and Pirkul (1998) presented yet another coordinated model of mixed zero-one programming. In this model, the input of the production plant are varied i.e., raw materials are supplied from different dealers and the output of finished goods are carried to different storages or distributed to markets based on customers’ demands. This model also aims to minimize the total costs including deployment, operation and storage, production and distribution. In a separate research, Jayaraman and Pirkul (2001) also proposed the PLANWAR model, showing the location of plants and storages with capacity constraints.

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