Multi-objective models for lot-sizing with supplier selection
Multi-objective models for lot-sizing with supplier selection
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DOI：
10.1016/j.ijpe.2010.11.017
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发表时间：
2011-03
影响因子：
12
通讯作者：
J. Rezaei;M. Davoodi
中科院分区：
文献类型：
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作者：
J. Rezaei;M. Davoodi
针对多产品、多供应商的多周期批量调度问题，建立了两个多目标混合整数非线性模型。每个模型都建立在三个目标函数(成本、质量和服务水平)和一组约束的基础上。总成本包括采购、订购、保留(和延交)和运输成本。订货成本被视为与订货频率有关的函数，而总质量和服务水平则被视为与时间有关的函数。第一个模型在不允许缺货的情况下代表了这个问题，而在第二个模型中，缺货期间的所有需求都是延交的。一方面，考虑到这些模型的复杂性，以及遗传算法获得一组Pareto最优解的能力，我们采用了一种创新的遗传算法来求解模型。比较结果表明，在缺货情况下，与没有缺货的情况相比，买家能够更好地优化他们的目标。如果我们把订货频率考虑在内，总成本就会大大降低。
In this paper, two multi-objective mixed integer non-linear models are developed for multi-period lot-sizing problems involving multiple products and multiple suppliers. Each model is constructed on the basis of three objective functions (cost, quality and service level) and a set of constraints. The total costs consist of purchasing, ordering, holding (and backordering) and transportation costs. Ordering cost is seen as an ‘ordering frequency’-dependent function, whereas total quality and service level are seen as time-dependent functions. The first model represents this problem in situations where shortage is not allowed while in the second model, all the demand during the stock-out period is backordered. Considering the complexity of these models on the one hand, and the ability of genetic algorithms to obtain a set of Pareto-optimal solutions, we apply a genetic algorithm in an innovative approach to solve the models. Comparison results indicate that, in a backordering situation, buyers are better able to optimize their objectives compared to situations where there is no shortage. If we take ordering frequency into account, the total costs are reduced significantly.