Reducing noise pollution by flexible job-shop scheduling with worker flexibility: Multi-subpopulation evolutionary algorithm

Document Type : Research Article

Authors

Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.

10.24200/sci.2022.57813.5431

Abstract

Flexible job-shop scheduling is one of the most critical production management topics. In this paper, it is also assumed job interruption due to the machine breakdown is allowed, and the processing time depends on the speed of the machines and requires both human and machine resources to process the jobs. Although, as the speed of the machine increases, the time of job’ completion reduces, an increase in speed results in an increase in noise pollution in the production environment, and with the aim of applying a cleaner production that is a preventative approach, it has been tried to reduce noise pollution by minimizing the increase in speed. After modeling the problem using the mixed -integer programming and solving it using the ε-constraint method, since the problem is NP-hard, a multi-subpopulation evolutionary algorithm is proposed to solve it. The results showed that considering the Mean Ideal Distance (MID) criterion, the ε-constraint method has a better performance than the proposed algorithm but considering other criteria the proposed algorithm has is better. Also, the proposed algorithm was compared with the NSGA-II in large-size instances and the computational results showed that the proposed algorithm performs better than the NSGA-II in most cases.

Keywords

Main Subjects

References

References:
1.Behnamian, J., Fatemi Ghomi, S.M.T., Jolai, F., et al.“Realistic two-stage flowshop batch scheduling problems with transportation capacity and time”, Applied MathematicalModelling, 36, pp. 723–735 (2012). https://doi.org/10.1016/ j.apm.2011.07.011.
2.Hillary, R. Environmental management systems and cleanerproduction, Wiley Toronto (1997).
3.Scarazzato, T., Panossian, Z., Tenório, J., et al. “A review ofcleaner production in electroplating industries usingelectrodialysis”, Journal of Cleaner Production, 168, pp.1590-1602 (2017). https://doi.org/10.1016/j.jclepro.2017.03.152.
4.Mokhtari, H. and Hasani, A. “A multi-objective model forcleaner production-transportation planning in manufacturingplants via fuzzy goal programming”, Journal ofManufacturing Systems, 44, pp. 230-242 (2017). https://doi.org/10.1016/j.jmsy.2017.06.002.
5.Jariwala, H.J., Syed, H.S., Pandya, M.J., et al. “Noise pollution& human health: A review”, Noise and Air Pollutions:Challenges and Opportunities. Ahmedabad: LD College of Engineering (2017). https://www.researchgate.net/publication/319329633_Noise_Pollution_Human_Health_A_Review.
6.Zhang, J., Ding, G., Zou, Y., et al. “Review of job shopscheduling research and its new perspectives under Industry4.0”, Journal of Intelligent Manufacturing, 30(4), pp. 1809-1830 (2019). https://doi.org/10.1007/s10845-017-1350-2.
7.Yazdani, M., Zandieh, M., and Tavakkoli-Moghaddam, R.“Evolutionary algorithms for multi-objective dual-resourceconstrained flexible job-shop scheduling problem”,OPSEARCH, 56(3), pp. 983-1006 (2019). https://doi.org/10.1007/s12597-019-00395-y.
8.Shen, L., Dauzère-Pérès, S., and Neufeld, J.S. “Solving theflexible job shop scheduling problem with sequence-dependent setup times”, European Journal of OperationalResearch, 265(2), pp. 503-516 (2018). https://doi.org/10.1016/j.ejor.2017.08.021.
9.de Oliveira Neto, G.C., Santana, J.C.C., Godinho Filho, M., etal. “Assessment of the environmental impact and economicbenefits of the adoption of cleaner production in a Brazilianmetal finishing industry”, Environmental Technology, 41(14),pp. 1814–1828 (2018). https://doi.org/10.1080/09593330.2018.1551426.
10.Rajaram, R., Jawahar, N., Ponnambalam, S., et al. “Multi-objective optimization of economic and environmental aspectsof a three-echelon supply chain”, In Industry 4.0 and Hyper-Customized Smart Manufacturing Supply Chains, pp. 127-158, IGI Global (2019). https://doi.org/10.4018/978-1-5225-9078-1.ch006.
11.Zarrouk, R., Bennour, I.E., and Jemai, A. “A two-level particle swarm optimization algorithm for the flexible job shopscheduling problem”, Swarm Intelligence, 13(2), pp. 145-168(2019). https://doi.org/10.1007/s11721-019-00167-w.
12.Amjad, M.K., Butt, S.I., Kousar, R., et al. “Recent researchtrends in genetic algorithm based flexible job shop schedulingproblems”, Mathematical Problems in Engineering, 2018, 9270802 (2018). https://doi.org/10.1155/2018/9270802.
13.Min, D., Dunbing, T., Adriana, G., et al. “Multi-objectiveoptimization for energy-efficient flexible job shop schedulingproblem with transportation constraints”, Robotics andComputer-Integrated Manufacturing, 59, pp. 143-157 (2019).https://doi.org/10.1016/j.rcim.2019.04.006.
14.Abedi, M., Chiong, R., Noman, N., et al. “A multi-population,multi-objective memetic algorithm for energy-efficient job-shop scheduling with deteriorating machines”, Expert Systemswith Applications, 157, pp. 1-33 (2020). https://doi.org/10.1016/j.eswa.2020.113348.
15.Li, X. and Gao, L. “An effective hybrid genetic algorithm andtabu search for flexible job shop scheduling problem”,International Journal of Production Economics, 174, pp. 93-110 (2016). https://doi.org/10.1016/j.ijpe.2016.01.016.
16.Kundakcı, N. and Kulak, O. “Hybrid genetic algorithms forminimizing makespan in dynamic job shop schedulingproblem”, Computers & Industrial Engineering, 96, pp. 31-51(2016). https://doi.org/10.1016/j.cie.2016.03.011.
17.AitZai, A., Benmedjdoub, B., Boudhar, M. “Branch-and-bound and PSO algorithms for no-wait job shop scheduling”,Journal of Intelligent Manufacturing, 27(3), pp. 679-688(2016). https://doi.org/10.1007/s10845-014-0906-7.
18.Wu, J., Wu, G., and Wang, J. “Flexible job-shop schedulingproblem based on hybrid ACO algorithm”, InternationalJournal of Simulation Modelling, 16(3), pp. 497-505 (2017).https://doi.org/10.2507/IJSIMM16(3)CO11.
19.Wang, L., Cai, J., Li, M., et al. “Flexible job-shop schedulingproblem using an improved ant colony optimization”,Scientific Programming, 2017, 9016303 (2017).https://doi.org/10.1155/2017/9016303.
20.Jamrus, T., Chien, C.-F., Gen, M., et al. “Hybrid particleswarm optimization combined with genetic operators for flexible job-shop scheduling under uncertain processing timefor semiconductor manufacturing”, IEEE Transactions onSemiconductor Manufacturing, 31(1), pp. 32-41 (2017). https://doi.org/10.1109/TSM.2017.2758380.
21.Gong, X., Deng, Q., Gong, G., et al. “A memetic algorithm for multi-objective flexible job-shop problem with workerflexibility”, International Journal of Production Research,7(56), pp. 2506-2522 (2017). https://doi.org/10.1080/00207543.2017.1388933.
22.Peng, C., Fang, Y., Lou, P., et al. “Analysis of double resourceflexible job-shop scheduling problem based on geneticalgorithm”, 15th International Conference on Networking,Sensing and Control, pp. 1-6 (2018). https://doi.org/10.1109/ICNSC.2018.8361360.
23.Tamssaouet, K., Dauzère-Pérès, S., and Yugma, C. “Metaheuristics for the Job-Shop Scheduling Problem with Machine Availability Constraints”, Computers & IndustrialEngineering, 125, pp. 1-16 (2018). https://doi.org/10.1016/j.cie.2018.08.008.
24.Gong, G., Deng, Q., Chiong, R., et al. “An effective memeticalgorithm for multi-objective job-shop scheduling”,Knowledge-Based Systems, 182, pp. 1-14 (2019).https://doi.org/10.1016/j.knosys.2019.07.011.
25.Zhang, G., Hu, Y., Sun, J., et al. “An improved geneticalgorithm for the flexible job shop scheduling problem withmultiple time constraints”, Swarm and EvolutionaryComputation, 54, pp. 1-15 (2020). https://doi.org/10.1016/j.swevo.2020.100664.
26.Ding, H. and Gu, X. “Improved particle swarm optimizationalgorithm based novel encoding and decoding schemes forflexible job shop scheduling problem”, Computers andOperations Research, 121, pp. 1-15 (2020). https://doi.org/10.1016/j.cor.2020.104951.
27.Yang, D., Zhou, X., Yang, Zh., et al. “Multi-objective optimization model for flexible job shop scheduling problem consideringtransportation constraints: A comparative study”, IEEE Congresson Evolutionary Computation (CEC), pp. 1-18 (2020). https://doi.org/10.1109/CEC48606.2020.9185653.
28.Heydari, M. and Aazami, A. “Minimizing the maximumtardiness and makespan criteria in a job shop schedulingproblem with sequence dependent setup times”, Journal ofIndustrial and Systems Engineering, 11(2), pp. 134-150(2018). https://doi.org/10.1186/2251-712X-8-25.
29.Gao, K.-Z., Suganthan, P.N., Pan, Q.-K., et al. “Discreteharmony search algorithm for flexible job shop schedulingproblem with multiple objectives”, Journal of IntelligentManufacturing, 27(2), pp. 363-374 (2016). https://doi.org/10.1007/s10845-014-0869-8.
30.Ebrahimi, A., Jeon, H.W., Lee, S., et al. “Minimizing totalenergy cost and tardiness penalty for a scheduling-layoutproblem in a flexible job shop system: A comparison of fourmetaheuristic algorithms”, Computers and IndustrialEngineering, 141, 106295 (2020). https://doi.org/10.1016/j.cie.2020.106295.
31.Yazdani, M., Aleti, A., Khalili, S. M., et al. “Optimizing thesum of maximum earliness and tardiness of the job shopscheduling problem”, Computers & Industrial Engineering,107, pp. 12-24 (2017). https://doi.org/10.1016/j.cie.2017.02.019.
32.Yu, J.-M. and Lee, D.-H. “Solution algorithms to minimise the total family tardiness for job shop scheduling with jobfamilies”, European Journal of Industrial Engineering, 12(1),pp. 1-23 (2018). https://doi.org/10.1016/j.cie.2017.02.019.
33.Mendoza, A.M., Acosta, R.H., and Reyes, J.C. “Productionscheduling for a job shop using a mathematical model”,Methodology, 11(12), pp. 13 (2018). https://doi.org/10.12988/ces.2018.87335.
34.Sadaghiani, J., Boroujerdi, S., Mirhabibi, M., et al. “A paretoarchive floating search procedure for solving multi-objectiveflexible job shop scheduling problem”, Decision ScienceLetters, 3(2), pp. 157-168 (2014). https://doi.org/10.5267/j.dsl.2013.12.001.
35.Dalfard, V.M. and Mohammadi, G. “Two meta-heuristicalgorithms for solving multi-objective flexible job-shopscheduling with parallel machine and maintenanceconstraints”, Computers and Mathematics with Applications,64(6), pp. 2111-2117 (2012). https://doi.org/10.1016/j.camwa.2012.04.007.
36.Huang, S., Tian, N., Wang, Y., et al. “Multi-objective flexiblejob-shop scheduling problem using modified discrete particleswarm optimization”, SpringerPlus, 5(1), pp. 1432 (2016). https://doi.org/10.1186/s40064-016-3054-z.
37.Aurich, J.C., Yang, X., Schröder, S., et al. “Noise investigation in manufacturing systems: An acoustic simulation and virtualreality enhanced method”, CIRP Journal of ManufacturingScience and Technology, 5(4), pp. 337-347 (2012). https://doi.org/10.1016/j.cirpj.2012.09.010.
38.Behnamian, J., Zandieh, M., and Fatemi Ghomi, S.M.T. “Amulti-phase covering Pareto-optimal front method to multi-objective parallel machine scheduling”, International Journalof Production Research, 48(17), pp. 4949–4976 (2010).https://doi.org/10.1016/j.eswa.2009.02.080.
39.Behnamian, J., Fatemi Ghomi, S.M.T., Jolai, F., et al.“Minimizing makespan on a three-machine flowshop batchscheduling problem with transportation using geneticalgorithm”, Applied Soft Computing, 12, pp. 768-777 (2012).https://doi.org/10.1016/j.asoc.2011.10.015.
40.Ahmadi, E., Zandieh, M., Farrokh, M., et al. “A multi objective optimization approach for flexible job shop schedulingproblem under random machine breakdown by evolutionaryalgorithms”, Computers & Operations Research, 73, pp. 56-66 (2016). https://doi.org/10.1016/j.cor.2016.03.009.
41.Cuiyu, W., Yang, L., and Xinyu, L. “Solving flexible job shopscheduling problem by a multi-swarm collaborative geneticalgorithm”, Journal of Systems Engineering and Electronics,32(2), pp. 261-271 (2021).https://doi.org/10.23919/JSEE.2021.000023.
42.Ehtesham Rasi, R. “Optimization of the multi-objectiveflexible job shop scheduling model by applying NSGAII andNRGA algorithms”, Journal of Industrial Engineering andManagement Studies, 8(1), pp. 45-71 (2021). https://doi.org/10.22116/jiems.2021.170958.1244.
43.Lu, Y. and Jiang, T. “Bi-population based discrete batalgorithm for the low-carbon job shop schedulingproblem”, IEEE Access, 7, pp. 14513-14522 (2019).https://doi.org/10.1109/ACCESS.2019.2892826.
Scientia Iranica
Volume 32, Issue 8
Transactions on Industrial Engineering (E)
March and April 2025 Article ID:5431

Files

History

  • Receive Date: 26 February 2021
  • Revise Date: 20 December 2021
  • Accept Date: 07 February 2022

Share

How to cite

Statistics