Optimization of Production Scheduling using Multi-Tasking by Multi-Population Genetic Algorithm

Authors

  • Isha Sanitha S. Dept. of ME, ASET, Palakkad, Kerala, India
  • Tom Zacharia Dept. of ME, St. Joseph’s College of Engineering and Technology, Kottayam, Kerala, India
  • Pradeepmon T.G. Dept. of ME, Mar Baselios Institute of Technology and Science, Ernakulam, Kerala, India

DOI:

https://doi.org/10.37591/joprm.v5i1.7171

Keywords:

Parallel scheduling, Multi-population genetic algorithm

Abstract

Production scheduling is an important activity for manufacturing and engineering, where it can have a major impact on the productivity of a production system. Traditional in-house manufacturing involves production machines with dedicated operators. Here, multitasking technique is used to optimize the production scheduling problem. In parallel machine scheduling, there are n jobs and m machines and each job needs to be executed on one of the machines during a fixed processing time, without pre-emption. This type of scheduling can be applied to identical or non-identical machines. The objective of this paper is to develop a multi-population genetic algorithm for optimizing non-identical parallel machine scheduling problem of a discreet manufacturing unit, by employing multitasking techniques. Company have well developed supply chain management system in addition to manufacturing facilities of critical high valued items for their product range. The algorithm will be developed for specific scheduling requirements. The benefit in enhancing the throughput will be demonstrated by Primavera P6 Professional.

References

Cochran Jeffery K, Shwu-Min Horng, Fowler John W. A Multi-Population Genetic Algorithm to Solve Multi-Objective Scheduling Problems for Parallel Machines. Computer and Operation Research. 2003; 1087–1102p.

Savas Balin. Non-Identical Parallel Machine Scheduling using Genetic Algorithm. Expert Syst. Appl. 2011; 6814–6821p.

Ali Husseinzadeh Kashan, Behrooz Karimi, Masoud Jenabi. A Hybrid Genetic Heuristic for Scheduling Parallel Batch Processing Machines with Arbitrary Job Sizes. Computer and Operation Research. 2008; 35(4): 1084–1098p.

Chan Felix TS, Choy KL, Bibhushan. A Genetic Algorithm-Based Scheduler for Multiproduct Parallel Machine Sheet Metal Job Shop. Expert Syst. Appl. 2011; 38(7): 8703–8715p.

Hong-Sen Yan, Hao-Xiang Wang, Xiao-Dong Zhang. Simultaneous Batch Splitting and Scheduling on Identical Parallel Production Lines. Information Sciences. 2013; 221: 501–519p.

Liu Min, Wu Cheng. Genetic Algorithms for the Optimal Common Due Date Assignment and the Optimal Scheduling Policy in Parallel Machine Earliness/Tardiness Scheduling Problems. Robot Comput Integr Manuf. 2006; 22(4): 279–287p.

Vidal Juan C, Manuel Mucientes, Alberto Bugarin et al. Machine Scheduling in Custom Furniture Industry through Nero-Evalutionary Hybridization. Appl. Soft Comput. 2011; 1600–1613p.

Bo Liu, Ling Wang, Ying Liu et al. An Effective Hybrid Particle Swarm Optimization for Batch Scheduling of Polypropylene Process. Comput. Chem. Eng. 2010; 518–528p.

Paneerselvam R. Production and Operations Management. 3rd Edn. 437–455p.

Hamdy Elwany, Mohamed Shouman, Mohamed Abou Ali. Production Scheduling Techniques. 1–13p.

Eugene Levner. Multi-Processor Scheduling Theory and Applications. ITECH Publishers. 1–445p.

Pinedo Michael L. Scheduling Theory Algorithms and Systems. 3rd Edn. 1–664p.

Vinicius Amaral Armentano, Moacir Felizardo de Franca Filho. Minimizing Total Tardiness in Parallel Machine Scheduling with Setup Times: An Adaptive Memory-Based GRASP Approach. Eur J. Oper. Res. 2007; 183(1): 100–114p.

Journal of Production Research & Management

Volume 5, Issue 1

ISSN: 2249-4766(online), ISSN: 2347-9930(print)

JoPRM (2015) 18-26 © STM Journals 2015. All Rights Reserved Page 26

Youichi Nonaka, Gabor Erdos, Tamas Kis et al. Generating Alternative Process Plans for Complex Parts. CIRP Ann Manuf. Technol. 2013; 453–458p.

Yue Xi, Jaejin Jang. Scheduling Jobs on Identical Parallel Machines with Unequal Future Ready Time and Sequence Dependent Setup: An Experimental Study. Int J Prod Econ. 2012; 137(1): 1–10p.

Production Schedule Data for the 4th Quarter of (Jan–Mar) 2013–2014.

Saricicek .J. Cooperative Decision making with Scheduler agents. Cooperative Design, Visualization, and Engineering. 4th International Conference. Shanghai, China, 2007.

Li, J., Melenhorst, J., Hensel, N., Rezvani, K. et al. T-cell responsesto peptide fragments of the BK virus T antigen: implications for cross-reactivity of immune response to JC virus. J. Gen. Virol. 2006.

Downloads

Published

2023-07-26

Issue

Vol. 5 No. 1 (2015)

Section

Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.