Many firms try to optimize their supply and production levels separately, but this method could limit influence profitability, negatively. Thus, it is becoming more important to analyze these two levels, simultaneously. In this paper, an integrated supply-production planning is considered, simultaneously. We develop a mathematical model, which calculates the optimal inventory lot sizing for each supplier and minimizes the total cost associated with the process of procuring raw material, transferring and holding raw materials and manufacturing. The problem is formulated as a nonlinear programming and heuristic genetic algorithm (GA) method is developed to solve the resulted problem. We examine the performance of the proposed model for a case study conducted in Iran. Experimental results show that such a model can reduce the costs of the case study, substantially.

In today’s competitive business environment, companies strive to increase their market shares. All companies clearly understand that they have to reach this goal by implementing cost effective methods and increase profits as much as possible. The cost of purchasing raw materials and component parts are significant portion of products in most manufacturing firms. Supplier selection and evaluation have been widely recognized to be one of the most substantial issues on material purchasing. In order to choose reliable suppliers it is necessary to have a trade-off between some tangible and intangible factors where some of them are in serious conflict. In this paper, an integrated technique of analytical network process improved by VIKOR and fuzzy sets theory and multi-objective mixed integer nonlinear programming is proposed to determine the appropriate suppliers. The proposed model of this paper also determines the order quantity allocated to each supplier in the case of multiple sourcing, multiple products and multi-period time horizon for an Iranian cable company.