Preventive maintenance for the flexible flowshop scheduling under uncertainty: a waste-to-energy system.
Flexible flowshop scheduling
Genetic algorithms
Preventive maintenance
Robust optimization
Waste-to-energy system
Journal
Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769
Informations de publication
Date de publication:
14 Sep 2021
14 Sep 2021
Historique:
received:
02
06
2021
accepted:
25
08
2021
entrez:
14
9
2021
pubmed:
15
9
2021
medline:
15
9
2021
Statut:
aheadofprint
Résumé
Nowadays, an efficient and robust plan for maintenance activities can reduce the total cost significantly in the equipment-driven industry. Maintenance activities are directly associated with the impact on the plant output, production quality, production cost, safety, and the environmental performance. To address this challenge more broadly, this paper presents an optimization model for the problem of flexible flowshop scheduling in a series-parallel waste-to-energy (WTE) system. To this end, a preventive maintenance (PM) policy is proposed to find an optimal sequence for processing tasks and minimize the delays. To deal with the uncertainty of the flexible flowshop scheduling of waste-to-energy in practice, the work processing time is modeled to be uncertain in this study. Therefore, a robust optimization model is applied to address the proposed problem. Due to the computational complexity of this model, a novel scenario-based genetic algorithm is proposed to solve it. The applicability of this research is shown by a real-life case study for a WTE system in Iran. The proposed algorithm is compared against an exact optimization method and a canonical genetic algorithm. The findings confirm a competitive performance of the proposed method in terms of time savings that will ultimately save the cost of the proposed PM policy.
Identifiants
pubmed: 34519989
doi: 10.1007/s11356-021-16234-x
pii: 10.1007/s11356-021-16234-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Alaswad S, Xiang Y (2017) A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliab Eng Syst Saf 157:54–63
doi: 10.1016/j.ress.2016.08.009
Alimian M, Saidi-Mehrabad M, Jabbarzadeh A (2019) A robust integrated production and preventive maintenance planning model for multi-state systems with uncertain demand and common cause failures. J Manuf Syst 50:263–277
doi: 10.1016/j.jmsy.2018.12.001
Bappy MM, Ali SM, Kabir G, Paul SK (2019) Supply chain sustainability assessment with Dempster-Shafer evidence theory: Implications in cleaner production. J Clean Prod 237:117771
doi: 10.1016/j.jclepro.2019.117771
Chen YL (2019) Optimal scheduling replacement policies for a system with multiple random works. Communications in Statistics-Theory and Methods 48(3):676–688
doi: 10.1080/03610926.2017.1417434
Chang CC (2018) Optimal age replacement scheduling for a random work system with random lead time. Int J Prod Res 56(16):5511–5521
doi: 10.1080/00207543.2018.1425017
Chowdhury NA, Ali SM, Mahtab Z, Rahman T, Kabir G, Paul SK (2019) A structural model for investigating the driving and dependence power of supply chain risks in the readymade garment industry. J Retail Consum Serv 51:102–113
doi: 10.1016/j.jretconser.2019.05.024
Cui W (2020) Approximate approach to deal with the uncertainty in integrated production scheduling and maintenance planning. J Shanghai Jiaotong Univ (Science) 25(1):106–117
doi: 10.1007/s12204-019-2086-2
Cui NF, Liang YY (2018) Robust project scheduling based on the integrated optimization between resource flow network and time buffers. Syst Eng Theory & Pract 38(1):102–112
Diallo C, Venkatadri U, Khatab A, Liu Z (2018) Optimal selective maintenance decisions for large serial k-out-of-n: G systems under imperfect maintenance. Reliab Eng Syst Saf 175:234–245
doi: 10.1016/j.ress.2018.03.023
Dulebenets MA (2019) A Delayed Start Parallel Evolutionary Algorithm for just-in-time truck scheduling at a cross-docking facility. Int J Prod Econ 212:236–258. https://doi.org/10.1016/j.ijpe.2019.02.017
doi: 10.1016/j.ijpe.2019.02.017
Du Y, Li JQ, Luo C, Meng LL (2021) A hybrid estimation of distribution algorithm for distributed flexible job shop scheduling with crane transportations. Swarm Evol Comput 62:100861
doi: 10.1016/j.swevo.2021.100861
Ebrahimi M, Ghomi SMTF, Karimi B (2020) Application of the preventive maintenance scheduling to increase the equipment reliability: case study-bag filters in cement factory. J Ind Manag Optim 16(1):189–205
Eryilmaz S (2017) Computing optimal replacement time and mean residual life in reliability shock models. Comput Ind Eng 103:40–45
doi: 10.1016/j.cie.2016.11.017
Edwards GTC, Hinge J, Skou-Nielsen N, Villa-Henriksen A, Sørensen CAG, Green O (2017) Route planning evaluation of a prototype optimized infield route planner for neutral material flow agricultural operations. Biosyst Eng 153:149e157
doi: 10.1016/j.biosystemseng.2016.10.007
Escamilla J, Salido MA, Giret A, Barber F (2016) A metaheuristic technique for energy-efficiency in job-shop scheduling. Knowl Eng Rev 31(5):475–485
doi: 10.1017/S026988891600031X
Fallahpour A, Nayeri S, Sheikhalishahi M, Wong KY, Tian G, Fathollahi-Fard AM (2021a) A hyper-hybrid fuzzy decision-making framework for the sustainable-resilient supplier selection problem: a case study of Malaysian Palm oil industry. Environ Sci Pollut Res:1–21
Fallahpour A, Wong KY, Rajoo S, Tian G (2021b) An evolutionary-based predictive soft computing model for the prediction of electricity consumption using multi expression programming. J Clean Prod 283:125287
doi: 10.1016/j.jclepro.2020.125287
Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Tian G, Li Z (2020a) An adaptive Lagrangian relaxation-based algorithm for a coordinated water supply and wastewater collection network design problem. Inf Sci 512:1335–1359. https://doi.org/10.1016/j.ins.2019.10.062
doi: 10.1016/j.ins.2019.10.062
Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Tavakkoli-Moghaddam R (2020b) Red deer algorithm (RDA): a new nature-inspired meta-heuristic. Soft Comput 24:14637–14665. https://doi.org/10.1007/s00500-020-04812-z
doi: 10.1007/s00500-020-04812-z
Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Tavakkoli-Moghaddam R (2018) The social engineering optimizer (SEO). Eng Appl Artif Intell 72:267–293
doi: 10.1016/j.engappai.2018.04.009
Fathollahi-Fard AM, Woodward L, Akhrif O (2021) Sustainable distributed permutation flow-shop scheduling model based on a triple bottom line concept. J Ind Inf Integr:100233
Fazlollahtabar H, Gholizadeh H (2020) Fuzzy possibility regression integrated with fuzzy adaptive neural network for predicting and optimizing electrical discharge machining parameters. Comput Ind Eng 140:106225
doi: 10.1016/j.cie.2019.106225
Fu Y, Tian G, Fathollahi-Fard AM, Ahmadi A, Zhang C (2019) Stochastic multi-objective modelling and optimization of an energy-conscious distributed permutation flow shop scheduling problem with the total tardiness constraint. J Clean Prod 226:515–525. https://doi.org/10.1016/j.jclepro.2019.04.046
doi: 10.1016/j.jclepro.2019.04.046
Gholizadeh H, Fazlollahtabar H, Gholizadeh R (2019) A modified branch and bound algorithm for a vague flow-shop scheduling problem. Iran J Fuzzy Syst 16(4):55–64
Gholizadeh H, Tajdin A, Javadian N (2020a) A closed-loop supply chain robust optimization for disposable appliances. Neural Comput & Applic 32(8):3967–3985
doi: 10.1007/s00521-018-3847-9
Gholizadeh H, Fazlollahtabar H, Khalilzadeh M (2020b) A robust fuzzy stochastic programming for sustainable procurement and logistics under hybrid uncertainty using big data. J Clean Prod 258:120640
doi: 10.1016/j.jclepro.2020.120640
Gholizadeh H, Fazlollahtabar H (2020) Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: case study in melting industry. Comput Ind Eng 147:106653
doi: 10.1016/j.cie.2020.106653
Govindan K, Gholizadeh H (2021) Robust network design for sustainable-resilient reverse logistics network using big data: a case study of end-of-life vehicles. Transp Res E: Logist Transp Rev 149:102279
doi: 10.1016/j.tre.2021.102279
Halvorsen-Weare EE, Norstad I, Stålhane M, Nonås LM (2017) A metaheuristic solution method for optimizing vessel fleet size and mix for maintenance operations at offshore wind farms under uncertainty. Energy Procedia 137:531–538
doi: 10.1016/j.egypro.2017.10.382
Hosseini S, Kalam S, Barker K, Ramirez-Marquez JE (2020) Scheduling multi-component maintenance with a greedy heuristic local search algorithm. Soft Comput 24(1):351–366
doi: 10.1007/s00500-019-03914-7
Kavoosi M, Dulebenets MA, Abioye OF, Pasha J, Wang H, Chi H (2019) An augmented self-adaptive parameter control in evolutionary computation: a case study for the berth scheduling problem. Adv Eng Inform 42:100972. https://doi.org/10.1016/j.aei.2019.100972
doi: 10.1016/j.aei.2019.100972
Khatab A (2018) Maintenance optimization in failure-prone systems under imperfect preventive maintenance. J Intell Manuf 4:1–11
Jiang Z, Le Z (2014) Study on multi-objective flexible job-shop scheduling problem considering energy consumption. J Ind Eng Manag 7(3):589–604
Jovanović P, Kecman P, Bojović N, Mandić D (2017) Optimal allocation of buffer times to increase train schedule robustness. Eur J Oper Res 256(1):44–54
doi: 10.1016/j.ejor.2016.05.013
Lia F, Park J, Tressler J, Martukanitz R (2017) Partitioning of laser energy during directed energy deposition. Additive Manufacturing 18:31–39
Liu CH, Huang DH (2014) Reduction of power consumption and carbon footprints by applying multi-objective optimisation via genetic algorithms. Int J Prod Res 52(2):337–352.168 116-127
doi: 10.1080/00207543.2013.825740
Liu Q, Dong M, Chen FF, Lv W, Ye C (2019) Single-machine-based joint optimization of predictive maintenance planning and production scheduling. Robot Comput Integr Manuf 55:173–182
doi: 10.1016/j.rcim.2018.09.007
Liao W, Zhang X, Jiang M (2017) Multi-objective group scheduling optimization integrated with preventive maintenance. Eng Optim 49(11):1890–1904
doi: 10.1080/0305215X.2017.1280258
Lu B, Zhou X (2017) Opportunistic preventive maintenance scheduling for serial-parallel multistage manufacturing systems with multiple streams of deterioration. Reliab Eng Syst Saf 34:176–189
Lu C, Gao L, Gong W, Hu C, Yan X, Li X (2021) Sustainable scheduling of distributed permutation flow-shop with non-identical factory using a knowledge-based multi-objective memetic optimization algorithm. Swarm Evol Comput 60:100803
doi: 10.1016/j.swevo.2020.100803
May G, Stahl B, Taisch M, Prabhu V (2015) Multi-objective genetic algorithm for energy-efficient job shop scheduling. Int J Prod Res 53(23):7071–7089
doi: 10.1080/00207543.2015.1005248
Maatouk I, Jarkass I, Châtelet E, Chebbo N (2019) Preventive maintenance optimization and comparison of genetic algorithm models in a series–parallel multi-state system. J Intell Syst 28(2):219–230
doi: 10.1515/jisys-2017-0096
Malekpour H, Hafezalkotob A, Khalili-Damghani K (2021) Product processing prioritization in hybrid flow shop systems supported on Nash bargaining model and simulation-optimization. Expert Syst Appl 180:115066
doi: 10.1016/j.eswa.2021.115066
Mao, J., Hu, X., Pan, Q. K., Miao, Z., He, C., & Tasgetiren, M. F. (2020a). An improved discrete artificial bee colony algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance. In 2020 39th Chinese Control Conference (CCC) (pp. 1679-1684). IEEE.
Mao, J., Hu, X., Pan, Q. K., Miao, Z., He, C., & Tasgetiren, M. F. (2020b). An iterated greedy algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance to minimize total flowtime. In 2020 39th Chinese Control Conference (CCC) (pp. 1507-1512). IEEE.
Mao JY, Pan QK, Miao ZH, Gao L (2021) An effective multi-start iterated greedy algorithm to minimize makespan for the distributed permutation flowshop scheduling problem with preventive maintenance. Expert Syst Appl 169:114495
doi: 10.1016/j.eswa.2020.114495
Miyata HH, Nagano MS, Gupta JN (2019) Integrating preventive maintenance activities to the no-wait flow shop scheduling problem with dependent-sequence setup times and makespan minimization. Comput Ind Eng 135:79–104
doi: 10.1016/j.cie.2019.05.034
Moosavi J, Naeni LM, Fathollahi-Fard AM, Fiore U (2021) Blockchain in supply chain management: a review, bibliometric, and network analysis. Environ Sci Pollut Res:1–15
Mojtahedi M, Fathollahi-Fard AM, Tavakkoli-Moghaddam R, Newton S (2021) Sustainable vehicle routing problem for coordinated solid waste management. J Ind Inf Integr 23:100220
Naderi B, Ghomi SF, Aminnayeri M, Zandieh M (2011) Modeling and scheduling open shops with sequence-dependent setup times to minimize total completion time. Int J Adv Manuf Technol 53(5-8):751–760
doi: 10.1007/s00170-010-2853-6
Naderi B, Zandieh M, Balagh AKG, Roshanaei V (2009) An improved simulated annealing for hybrid flowshops with sequence-dependent setup and transportation times to minimize total completion time and total tardiness. Expert Syst Appl 36(6):9625–9633
doi: 10.1016/j.eswa.2008.09.063
Pasha J, Dulebenets MA, Fathollahi-Fard AM, Tian G, Lau YY, Singh P, Liang B (2021) An integrated optimization method for tactical-level planning in liner shipping with heterogeneous ship fleet and environmental considerations. Adv Eng Inform 48:101299
doi: 10.1016/j.aei.2021.101299
Razavi N, Gholizadeh H, Nayeria S, Ashrafi TA (2020) A robust optimization model of the field hospitals in the sustainable blood supply chain in crisis logistics. J Oper Res Soc:1–26
Schrotenboer AH, Ursavas E, Vis IF (2020) Mixed Integer Programming models for planning maintenance at offshore wind farms under uncertainty. Transp Res Part C Emerg Technol 112:180–202
doi: 10.1016/j.trc.2019.12.014
Seidgar H, Zandieh M, Mahdavi I (2016) Bi-objective optimization for integrating production and preventive maintenance scheduling in two-stage assembly flow shop problem. J Ind Prod Eng 33(6):404–425. https://doi.org/10.1080/21681015.2016.1173599
doi: 10.1080/21681015.2016.1173599
Tambe PP, Kulkarni MS (2016) Selective maintenance optimization under schedule and quality constraints. Int J Qual Reliab Manag 24:635–657
Wang X, Li L, Xie M (2019) Optimal preventive maintenance strategy for leased equipment under successive usage-based contracts. Int J Prod Res 57(18):5705–5724
doi: 10.1080/00207543.2018.1542181
Wang W, Tian G, Chen M, Tao F, Zhang C, Abdulraham AA et al (2020) Dual-objective program and improved artificial bee colony for the optimization of energy-conscious milling parameters subject to multiple constraints. J Clean Prod 245:118714
doi: 10.1016/j.jclepro.2019.118714
Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85
doi: 10.1007/BF00175354
Yahyatabar A, Najafi AA (2017) A quadratic reproduction based invasive weed optimization algorithm to minimize periodic preventive maintenance cost for series-parallel systems. Comput Ind Eng 110:436–461
doi: 10.1016/j.cie.2017.06.024
Yu C, Andreotti P, Semeraro Q (2020) Multi-objective scheduling in hybrid flow shop: Evolutionary algorithms using multi-decoding framework. Comput Ind Eng 147:106570
doi: 10.1016/j.cie.2020.106570
Zandieh M, Khatami AR, Rahmati SHA (2017) Flexible job shop scheduling under condition-based maintenance: improved version of imperialist competitive algorithm. Appl Soft Comput 58:449–464
doi: 10.1016/j.asoc.2017.04.060
Zhang M, Xie M (2017) An ameliorated improvement factor model for imperfect maintenance and its goodness of fit. Technometrics 59(2):237–246
doi: 10.1080/00401706.2016.1164757
Zhou X, Lu B (2018) Preventive maintenance scheduling for serial multi-station manufacturing systems with interaction between station reliability and product quality. Comput Ind Eng 122:283–291
doi: 10.1016/j.cie.2018.06.009
Zhou X, Shi K (2019) Capacity failure rate based opportunistic maintenance modeling for series-parallel multi-station manufacturing systems. Reliab Eng Syst Saf 181:46–53
doi: 10.1016/j.ress.2018.09.007
Zhang C, Tian G, Fathollahi-Fard AM, Li Z (2020) Interval-valued intuitionistic uncertain linguistic cloud petri net and its application in risk assessment for subway fire accident. IEEE Trans Autom Sci Eng:1–15. https://doi.org/10.1109/TASE.2020.3014907
Zhang, C., Fathollahi-Fard, A. M., Li, J., Tian, G., & Zhang, T. (2021). disassembly sequence planning for intelligent manufacturing using social engineering optimizer. Symmetry, 13(4), 663.
Zhang Z, Tang Q (2021) Integrating preventive maintenance to two-stage assembly flow shop scheduling: MILP model, constructive heuristics and meta-heuristics. Flex Serv Manuf J:1–48