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

Abstract

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.

An integrated Bayesian–Markovian framework for ascertaining cost of executing quality improvement programs in manufacturing industry

“…The research was infact a one of the few work that successfully integrated cost of poor quality into the paradigm of supply chain modeling. Kang et al, (2018) devised a multi-objective and discrete event simulation based framework to model the process improvement implementation framework. Specifically, within the related research framework, reduction in lead time and…”

“…The research was infact a one of the few works that successfully integrated the cost of poor quality into the paradigm of supply chain modeling. Kang and Bhatti (2018) devised a multi-objective and discrete event simulation-based framework to model the process improvement implementation framework. Specifically, within the related research framework, reduction in lead time and rationalization of total inventory holding cost were modeled as surrogate measure of process improvement, thus contributing to the extant literature in terms of a methodological contribution to the research literature pertaining to continuous process improvement.…”

Improvement opportunities of a Simulation/Expert System Approach for Manufacturing System Sizing: A review and proposal

“…In fact, simulation based optimization approaches have received considerable attention from many recent researchers to solve MS problems. [62] proposed an improvement framework using genetic algorithm and DES to investigate the buffer size and job sequencing optimization problems. [63] combined the DES models and artificial neural network models of the production system.…”

Solving Non-Permutation Flow Shop Scheduling Problem with Time Couplings

“…In experiments for 4, 7, and 10 different criteria, the approach was shown to outperform multiple existing algorithms, including the popular NSGA-III [8,10]. GA approaches are commonly used for solving a wide range of problems ranging from multi-objective train scheduling [11] to continuous process improvement [12].…”