Partner selection and production-distribution planning for optimal supply chain formation

This paper seeks to develop an effective methodology to solve the partner selection, and production-distribution planning problem that arises in the formation of an optimal supply chain. The methodology includes a Mathematical model which describes the characteristics of the integrated supply chain and an efficient genetic search algorithm. The objective is to minimize the total operating cost. Unlike canonical genetic algorithms, individuals of the proposed genetic algorithm have the ability to learn from their ancestors in order to enhance the convergence speed and the quality of the final solution. The performance of the proposed algorithm is evaluated by solving a set of randomly generated problems, Comparison of the results obtained with that of a conventional genetic algorithm clearly shows that the proposed algorithm is superior to the conventional genetic algorithm. The proposed methodology is described in some details in the hope of thus stimulating the use of similar methodology to the solution of other important problems in industrial engineering practice. Suggestions for future work are also included.