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A robust optimization model for multi-product two-stage capacitated production planning under uncertainty

机译：不确定条件下多产品两阶段产能计划的鲁棒优化模型

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摘要

Production planning (PP) is one of the most important issues carried out in manufacturing environments which seeks efficient planning, scheduling and coordination of all production activities that optimizes the company's objectives. In this paper, we studied a two-stage real world capacitated production system with lead time and setup decisions in which some parameters such as production costs and customer demand are uncertain. A robust optimization model is developed to formulate the problem in which minimization of the total costs including the setup costs, production costs, labor costs, inventory costs, and workforce changing costs is considered as performance measure. The robust approach is used to reduce the effects of fluctuations of the uncertain parameters with regards to all the possible future scenarios. A mixed-integer programming (MIP) model is developed to formulate the related robust production planning problem. In fact the robust proposed model is presented to generate an initial robust schedule. The performance of this schedule could be improved against of any possible occurrences of uncertain parameters. A case from an Iran refrigerator factory is studied and the characteristics of factory and its products are discussed. The computational results display the robustness and effectiveness of the model and highlight the importance of using robust optimization approach in generating more robust production plans in the uncertain environments. The tradeoff between solution robustness and model robustness is also analyzed.
机译：生产计划（PP）是在制造环境中执行的最重要的问题之一，该环境要求对所有生产活动进行有效的计划，计划和协调，以优化公司的目标。在本文中，我们研究了具有提前期和设置决策的两阶段现实世界容量化生产系统，其中某些参数（例如生产成本和客户需求）不确定。开发了一个健壮的优化模型来制定一个问题，其中将包括安装成本，生产成本，人工成本，库存成本和劳动力变更成本在内的总成本降至最低被视为绩效衡量指标。对于所有可能的未来方案，使用鲁棒的方法来减少不确定参数波动的影响。开发了混合整数规划（MIP）模型来制定相关的稳健生产计划问题。实际上，提出了鲁棒的提议模型以生成初始鲁棒时间表。对于任何可能出现的不确定参数，可以改进此计划的性能。研究了一个伊朗冰箱工厂的案例，并讨论了该工厂及其产品的特性。计算结果显示了模型的鲁棒性和有效性，并强调了在不确定环境中使用鲁棒优化方法生成更鲁棒的生产计划的重要性。还分析了解决方案鲁棒性与模型鲁棒性之间的权衡。

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