Continuous process improvement implementation framework using multi-objective genetic algorithms and discrete event simulation
| dc.contributor.author | Kang, Parminder Singh | |
| dc.contributor.author | Bhatti, Rajbir | |
| dc.date.accessioned | 2024-01-19T21:41:14Z | |
| dc.date.available | 2024-01-19T21:41:14Z | |
| dc.date.issued | 2019 | |
| dc.description.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. | |
| dc.identifier.citation | Kang, P.S. and Bhatti, R.S. (2019), "Continuous process improvement implementation framework using multi-objective genetic algorithms and discrete event simulation", Business Process Management Journal, Vol. 25 No. 5, pp. 1020-1039. https://doi.org/10.1108/BPMJ-07-2017-0188 | |
| dc.identifier.doi | https://doi.org/10.1108/BPMJ-07-2017-0188 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14078/3381 | |
| dc.language.iso | en | |
| dc.rights | Attribution-NonCommercial (CC BY-NC) | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.subject | process improvement | |
| dc.subject | optimization | |
| dc.subject | simulation | |
| dc.subject | genetic algorithms | |
| dc.title | Continuous process improvement implementation framework using multi-objective genetic algorithms and discrete event simulation | en |
| dc.type | Article |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Kang-continuous-process-Improvement.pdf
- Size:
- 759.4 KB
- Format:
- Adobe Portable Document Format