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Continuous process improvement implementation framework using multi-objective genetic algorithms and discrete event simulation

机译：使用多目标遗传算法和离散事件模拟的连续过程改进实施框架

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Purpose Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement issues by simultaneously investigating the job sequencing and buffer size optimization problems. Design/methodology/approach This paper proposes a continuous process improvement implementation framework using a modified genetic algorithm (GA) and discrete event simulation to achieve multi-objective optimization. The proposed combinatorial optimization module combines the problem of job sequencing and buffer size optimization under a generic process improvement framework, where lead time and total inventory holding cost are used as two combinatorial optimization objectives. The proposed approach uses the discrete event simulation to mimic the manufacturing environment, the constraints imposed by the real environment and the different levels of variability associated with the resources. Findings Compared to existing evolutionary algorithm-based methods, the proposed framework considers the interrelationship between succeeding and preceding processes and the variability induced by both job sequence and buffer size problems on each other. A computational analysis shows significant improvement by applying the proposed framework. Originality/value Significant body of work exists in the area of continuous process improvement, discrete event simulation and GAs, a little work has been found where GAs and discrete event simulation are used together to implement continuous process improvement as an iterative approach. Also, a modified GA simultaneously addresses the job sequencing and buffer size optimization problems by considering the interrelationships and the effect of variability due to both on each other.
机译：目的持续的流程改进是一个难题，尤其是在高品种/小批量的环境中，由于流程之间的复杂相互关系。本文的目的是通过同时调查作业排序和缓冲区大小优化问题来解决过程改进问题。设计/方法/方法本文提出了一种使用改进的遗传算法（GA）和离散事件模拟的连续过程改进实施框架，以实现多目标优化。提出的组合优化模块在一个通用的流程改进框架下结合了作业排序和缓冲区大小优化的问题，其中提前期和总库存持有成本被用作两个组合优化目标。所提出的方法使用离散事件模拟来模仿制造环境，实际环境施加的约束以及与资源相关的不同程度的可变性。结果与现有的基于进化算法的方法相比，该框架考虑了后续流程与先前流程之间的相互关系以及由作业序列和缓冲区大小问题引起的可变性。通过应用所提出的框架，计算分析显示出显着的改进。独创性/价值在持续改进流程，离散事件模拟和GA方面存在大量工作，已经发现了一些将GA和离散事件模拟一起用于实现连续过程改进的迭代方法。此外，经过修改的GA通过考虑相互之间的相互关系和可变性的影响，同时解决了作业排序和缓冲区大小优化的问题。

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