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Multi-Objective Programming for Lot-Sizing with Quantity Discount
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Multi-Objective Programming for Lot-Sizing with Quantity Discount

He-Yau Kang, Amy H. I. Lee, Chun-Mei Lai 和 Mei-Sung Kang
ADVANCES IN MATHEMATICAL AND COMPUTATIONAL METHODS: ADDRESSING MODERN CHALLENGES OF SCIENCE, TECHNOLOGY, AND SOCIETY, 卷.1368, 頁碼.205-208
AIP Conference Proceedings
01/01/2011
Web of Science ID: WOS:000299569200050

摘要

Mathematics Mathematics, Interdisciplinary Applications Physical Sciences Physics Physics, Mathematical Science & Technology
Multi-objective programming (MOP) is one of the popular methods for decision making in a complex environment. In a MOP, decision makers try to optimize two or more objectives simultaneously under various constraints. A complete optimal solution seldom exists, and a Pareto-optimal solution is usually used. Some methods, such as the weighting method which assigns priorities to the objectives and sets aspiration levels for the objectives, are used to derive a compromise solution. The epsilon -constraint method is a modified weight method. One of the objective functions is optimized while the other objective functions are treated as constraints and are incorporated in the constraint part of the model. This research considers a stochastic lot-sizing problem with multi-suppliers and quantity discounts. The model is transformed into a mixed integer programming (MIP) model next based on the epsilon -constraint method. An illustrative example is used to illustrate the practicality of the proposed model. The results demonstrate that the model is an effective and accurate tool for determining the replenishment of a manufacturer from multiple suppliers for multiperiods.

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