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Chance-constrained programming with robustness for lot-sizing and scheduling problems under complex uncertainty
Robotic Intelligence and Automation ( IF 2.1 ) Pub Date : 2022-06-28 , DOI: 10.1108/aa-01-2022-0004
Jizhuang Hui , Shuai Wang , Zhu Bin , Guangwei Xiong , Jingxiang Lv

Purpose

The purpose of this paper deals with a capacitated multi-item dynamic lot-sizing problem with the simultaneous sequence-dependent setup scheduling of the parallel resource under complex uncertainty.

Design/methodology/approach

An improved chance-constrained method is developed, in which confidence level of uncertain parameters is used to process uncertainty, and based on this, the reliability of the constraints is measured. Then, this study proposes a robust reconstruction method to transform the chance-constrained model into a deterministic model that is easy to solve, in which the robust transformation methods are used to deal with constraints with uncertainty on the right/left. Then, experimental studies using a real-world production data set provided by a gearbox synchronizer factory of an automobile supplier is carried out.

Findings

This study has demonstrated the merits of the proposed approach where the inventory of products tends to increase with the increase of confidence level. Due to a larger confidence level may result in a more strict constraint, which means that the decision-maker becomes more conservative, and thus tends to satisfy more external demands at the cost of an increase of production and stocks.

Research limitations/implications

Joint decisions of production lot-sizing and scheduling widely applied in industries can effectively avert the infeasibility of lot-size decisions, caused by capacity of lot-sing alone decision and complex uncertainty such as product demand and production cost. is also challenging.

Originality/value

This study provides more choices for the decision-makers and can also help production planners find bottleneck resources in the production system, thus developing a more feasible and reasonable production plan in a complex uncertain environment.



中文翻译:

具有鲁棒性的机会约束规划,用于复杂不确定下的批量和调度问题

目的

本文的目的是解决复杂不确定性下并行资源的同时依赖于序列的设置调度的容量多项目动态批量问题。

设计/方法/方法

提出了一种改进的机会约束方法,该方法利用不确定参数的置信度来处理不确定性,并在此基础上测量约束的可靠性。然后,本研究提出了一种鲁棒重建方法,将机会约束模型转化为易于求解的确定性模型,其中鲁棒变换方法用于处理左右不确定性约束。然后,使用汽车供应商的变速箱同步器工厂提供的真实生产数据集进行实验研究。

发现

这项研究证明了所提出方法的优点,即产品库存随着置信水平的增加而增加。由于更大的置信水平可能导致更严格的约束,这意味着决策者变得更加保守,从而倾向于以增加产量和库存为代价来满足更多的外部需求。

研究限制/影响

批量和调度联合决策在行业中得到广泛应用,可以有效避免批量决策的不可行性,即批量决策的单独决策能力和产品需求、生产成本等复杂的不确定性。也具有挑战性。

原创性/价值

本研究为决策者提供了更多的选择,也可以帮助生产计划者找到生产系统中的瓶颈资源,从而在复杂的不确定环境中制定出更加可行、合理的生产计划。

更新日期:2022-06-28
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