alexa Optimal Process/Reprocess Production Mix for Multiple Orders in Multiple Periods
ISSN: 2169-0316

Industrial Engineering & Management
Open Access

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Research Article

Optimal Process/Reprocess Production Mix for Multiple Orders in Multiple Periods

Hsiao-Fan Wang* and Hung-Shi Lin

Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan, Republic of China

*Corresponding Asuthor:
Hsiao-Fan Wang
Department of Industrial Engineering and Engineering Management
National Tsing Hua University, Hsinchu
Taiwan, Republic of China
Tel: +886-3-571-513
E-mail: [email protected]

Received November 12, 2014; Accepted November 26, 2014; Published December 03, 2014

Citation: Wang HF, Lin HS (2014) Optimal Process/Reprocess Production Mix for Multiple Orders in Multiple Periods. Ind Eng Manage 3:144. doi:10.4172/2169-0316.1000144

Copyright: ©2014 Wang HF, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Production planning, which allows the manufacturer to efficiently manage all production activities, is a major issue in manufacturing/processing industries. This study considers a medium-term production planning problem, in which a producer receives several periodic orders from multiple retailers at the beginning of the planning horizon. Given the limit in production capacity, the producer must reject some orders under the profit maximization principle, while considering multiple and inseparable orders, backlogging policy, and capacity limitation of the plant. Therefore, the producer faces two issues, namely, (a) which orders the producer should accept, and (b) how to allocate the products to the retailers. To solve these problems, we propose a multiple period production plan in the form of an integer program. Theoretical development with numerical examples is provided. The results have shown that this study can help producers deal with a more realistic environment and derive useful product delivery information.

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