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Mathematical Problems in Engineering
Volume 2015, Article ID 241536, 12 pages
http://dx.doi.org/10.1155/2015/241536
Research Article

An Optimal Decision Model of Production-Inventory with MTS and ATO Hybrid Model Considering Uncertain Demand

1School of Traffic & Transportation Engineering, Central South University, Changsha, Hunan 410075, China
2Forestry Engineering Doctoral Research Center, Central South University of Forestry and Technology, Changsha, Hunan 410004, China

Received 28 January 2015; Accepted 4 May 2015

Academic Editor: Anders Eriksson

Copyright © 2015 Dezhi Zhang et al. 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

This paper presents an optimization decision model for a production system that comprises the hybrid make-to-stock/assemble-to-order (MTS/ATO) organization mode with demand uncertainty, which can be described as a two-stage decision model. In the first decision stage (i.e., before acquiring the actual demand information of the customer), we have studied the optimal quantities of the finished products and components, while in the second stage (i.e., after acquiring the actual demand information of the customer), we have made the optimal decision on the assignment of components to satisfy the remaining demand. The optimal conditions on production and inventory decision are deduced, as well as the bounds of the total procurement quantity of the components in the ATO phase and final products generated in the MTS phase. Finally, an example is given to illustrate the above optimal model. The findings are shown as follows: the hybrid MTS and ATO production system reduces uncertain demand risk by arranging MTS phase and ATO phase reasonably and improves the expected profit of manufacturer; applying the strategy of component commonality can reduce the total inventory level, as well as the risk induced by the lower accurate demand forecasting.