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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 475606, 10 pages
http://dx.doi.org/10.1155/2014/475606
Research Article

An Integrated Model for Production and Distribution Planning of Perishable Products with Inventory and Routing Considerations

Department of Industrial Engineering, Iran University of Science & Technology, P.O. Box 1684613114, Narmak, Tehran, Iran

Received 28 January 2014; Revised 22 April 2014; Accepted 25 April 2014; Published 12 May 2014

Academic Editor: Michael Freitag

Copyright © 2014 S. M. Seyedhosseini and S. M. Ghoreyshi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In many conventional supply chains, production planning and distribution planning are treated separately. However, it is now demonstrated that they are mutually related problems that must be tackled in an integrated way. Hence, in this paper a new integrated production and distribution planning model for perishable products is formulated. The proposed model considers a supply chain network consisting of a production facility and multiple distribution centers. The facility produces a single perishable product that is storable only for predetermined periods. A homogenous fleet of vehicles is responsible for delivering the product from facility to distribution centers. The decisions to be made are the production quantities, the distribution centers that must be visited, and the quantities to be delivered to them. The objective is to minimize the total cost, where the trip minimization is considered simultaneously. As the proposed formulation is computationally complex, a heuristic method is developed to tackle the problem. In the developed method, the problem is divided into production submodel and distribution submodel. The production submodel is solved using LINGO, and a particle swarm heuristic is developed to tackle distribution submodel. Efficiency of the algorithm is proved through a number of randomly generated test problems.