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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流程改进实施框架

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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，连续过程改进是一种难题，尤其是由于过程之间的相互关系复杂的相互关系，尤其是高品质/低批量环境。本文的目的是通过同时调查作业排序和缓冲大小优化问题来解决流程改进问题。设计/方法/方法本文采用了使用改进的遗传算法（GA）和离散事件模拟来实现连续的过程改进实施框架，以实现多目标优化。所提出的组合优化模块在通用过程改进框架下结合了作业排序和缓冲大小优化的问题，其中提前期和总库存保持成本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拟制造环境，由实际环境施加的约束以及与资源相关的不同的可变性。结果与现有的基于进化算法的方法相比，所提出的框架考虑了成功和前面的过程之间的相互关系以及由作业序列和缓冲区大小问题所引起的变化。计算分析通过应用提出的框架显示出显着的改进。原创性/价值在连续过程改进的领域存在的原创性/值存在，离散的事件仿真和气体，已经找到了一点工作，其中气体和离散事件仿真一起使用以实现连续的过程改进作为迭代方法。此外，修改的GA通过考虑相互关系和彼此的可变性的效果来同时解决作业排序和缓冲大小优化问题。

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