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Cited by 18Scheduling and Optimization AlgorithmsAdvanced Manufacturing and Logistics OptimizationHealthcare Operations and Scheduling Optimization

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Integrating sustainability into production scheduling in hybrid flow-shop environments

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Mokhtari-Moghadam, Ali, Pourhejazy, Pourya, Gupta, Deepak·2023·Environmental Science and Pollution Research
DOI 10.1007/s11356-023-26986-3
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Abstract

Global energy consumption is projected to grow by nearly 50% as of 2018, reaching a peak of 910.7 quadrillion BTU in 2050. The industrial sector accounts for the largest share of the energy consumed, making energy awareness on the shop floors imperative for promoting industrial sustainable development. Considering a growing awareness of the importance of sustainability, production planning and control require the incorporation of time-of-use electricity pricing models into scheduling problems for well-informed energy-saving decisions. Besides, modern manufacturing emphasizes the role of human factors in production processes. This study proposes a new approach for optimizing the hybrid flow-shop scheduling problems (HFSP) considering time-of-use electricity pricing, workers’ flexibility, and sequence-dependent setup time (SDST). Novelties of this study are twofold: to extend a new mathematical formulation and to develop an improved multi-objective optimization algorithm. Extensive numerical experiments are conducted to evaluate the performance of the developed solution method, the adjusted multi-objective genetic algorithm (AMOGA), comparing it with the state-of-the-art, i.e., strength Pareto evolutionary algorithm (SPEA2), and Pareto envelop-based selection algorithm (PESA2). It is shown that AMOGA performs better than the benchmarks considering the mean ideal distance, inverted generational distance, diversification, and quality metrics, providing more versatile and better solutions for production and energy efficiency.

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Research works printed but not linked: 45

  1. Adler L, Fraiman N, Kobacker E et al (1993) BPSS: a scheduling support system for the packaging industry. Oper Res 41:641–648. https://doi.org/10.1287/opre.41.4.641 · DOI
  2. Chen L, Bostel N, Dejax P et al (2007) A tabu search algorithm for the integrated scheduling problem of container handling systems in a maritime terminal. Eur J Oper Res 181:40–58. https://doi.org/10.1016/j.ejor.2006.06.033 · DOI
  3. Cheng C-Y, Lin S-W, Pourhejazy P et al (2020) Greedy-based non-dominated sorting genetic algorithm III for optimizing single-machine scheduling problem with interfering jobs. IEEE Access 8:142543–142556. https://doi.org/10.1109/ACCESS.2020.3014134 · DOI
  4. CoelloCoello CA, Reyes Sierra M (2004) A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm. In: Monroy R, Arroyo-Figueroa G, Sucar LE, Sossa H (eds) MICAI 2004: Advances in Artificial Intelligence. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 688–697
  5. Corne DW, Jerram NR, Knowles JD, Oates MJ (2001) PESA-II: region-based selection in evolutionary multiobjective optimization. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation (GECCO'01). Morgan Kaufmann Publishers Inc., San Francisco, CA, pp 283–290
  6. Dai M, Tang D, Giret A et al (2013) Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robot Comput Integr Manuf 29:418–429. https://doi.org/10.1016/j.rcim.2013.04.001 · DOI
  7. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197. https://doi.org/10.1109/4235.996017 · DOI · Paper page
  8. Gong G, Chiong R, Deng Q et al (2020) Energy-efficient flexible flow shop scheduling with worker flexibility. Expert Syst Appl 141:112902. https://doi.org/10.1016/j.eswa.2019.112902 · DOI
  9. Gong G, Deng Q, Gong X et al (2018a) A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators. J Clean Prod 174:560–576. https://doi.org/10.1016/j.jclepro.2017.10.188 · DOI
  10. Gong X, Deng Q, Gong G et al (2018b) A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility. Int J Prod Res 56:2506–2522. https://doi.org/10.1080/00207543.2017.1388933 · DOI
  11. Gong G, Deng Q, Gong X, Huang D (2021) A non-dominated ensemble fitness ranking algorithm for multi-objective flexible job-shop scheduling problem considering worker flexibility and green factors. Knowl-Based Syst 231:107430. https://doi.org/10.1016/j.knosys.2021.107430 · DOI
  12. Jin ZH, Ohno K, Ito T, Se E (2002) scheduling hybrid flowshops in printed circuit board assembly lines*. Prod Oper Manag 11:216–230. https://doi.org/10.1111/j.1937-5956.2002.tb00492.x · DOI
  13. Lee T-S, Loong Y (2019) A review of scheduling problem and resolution methods in flexible flow shop. Int J Ind Eng Comput 10:67–88
  14. Meng L, Zhang C, Zhang B, Ren Y (2023) Mathematical modeling and optimization of energy-conscious flexible job shop scheduling problem with worker flexibility. IEEE Access 7:68043–68059. https://doi.org/10.1109/ACCESS.2019.2916468 · DOI
  15. Li X, Lu C, Gao L et al (2018) An effective multiobjective algorithm for energy-efficient scheduling in a real-life welding shop. IEEE Trans Ind Informatics 14:5400–5409. https://doi.org/10.1109/TII.2018.2843441 · DOI
  16. Lin S-W, Cheng C-Y, Pourhejazy P et al (2021) New benchmark algorithm for hybrid flowshop scheduling with identical machines. Expert Syst Appl 183:115422. https://doi.org/10.1016/j.eswa.2021.115422 · DOI
  17. Xue L, Wang X (2023) A multi-objective discrete differential evolution algorithm for energy-efficient two-stage flow shop scheduling under time-of-use electricity tariffs. Appl Soft Comput 133:109946. https://doi.org/10.1016/j.asoc.2022.109946 · DOI
  18. Linn R, Zhang W (1999) Hybrid flow shop scheduling: a survey. Comput Ind Eng 37:57–61. https://doi.org/10.1016/S0360-8352(99)00023-6 · DOI
  19. Luo H, Du B, Huang GQ et al (2013) Hybrid flow shop scheduling considering machine electricity consumption cost. Int J Prod Econ 146:423–439. https://doi.org/10.1016/j.ijpe.2013.01.028 · DOI
  20. Meng L, Zhang C, Zhang B, Ren Y (2019) Mathematical modeling and optimization of energy-conscious flexible job shop scheduling problem with worker flexibility. IEEE Access 7:68043–68059. https://doi.org/10.1109/ACCESS.2019.2916468 · DOI
  21. Moghadam MA, Wong KY, Piroozfard H (2018) Solving a hybrid job-shop scheduling problem with space constraints and reentrant process by a genetic algorithm: a case study. Int J Ind Eng Theory Appl Pract 24(5). https://doi.org/10.23055/ijietap.2017.24.5.2371 · DOI
  22. Moghadam AM, Wong KY, Piroozfard H, et al (2014) Solving an industrial shop scheduling problem using genetic algorithm. In: Materials, Industrial, and Manufacturing Engineering Research Advances 1.1. Trans Tech Publications Ltd, pp 564–568. https://doi.org/10.4028/www.scientific.net/amr.845.564 · DOI
  23. Moradi H, Zandieh M, Mahdavi I (2011) Non-dominated ranked genetic algorithm for a multi-objective mixed-model assembly line sequencing problem. Int J Prod Res 49:3479–3499. https://doi.org/10.1080/00207540903433882 · DOI
  24. Mouzon G, Yildirim MB, Twomey J (2007) Operational methods for minimization of energy consumption of manufacturing equipment. Int J Prod Res 45:4247–4271. https://doi.org/10.1080/00207540701450013 · DOI
  25. NabipoorAfruzi E, Roghanian E, Najafi AA, Mazinani M (2013) A multi-mode resource-constrained discrete time–cost tradeoff problem solving using an adjusted fuzzy dominance genetic algorithm. Sci Iran 20:931–944. https://doi.org/10.1016/j.scient.2012.12.024 · DOI
  26. Pan Q, Wang L, Mao K et al (2013) An effective artificial bee colony algorithm for a real-world hybrid flowshop problem in steelmaking process. IEEE Trans Autom Sci Eng 10:307–322. https://doi.org/10.1109/TASE.2012.2204874 · DOI
  27. Piroozfard H, Wong KY, Wong WP (2018) Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resour Conserv Recycl 128:267–283 · DOI
  28. Luo Q, Deng Q, Xie G, Gong G (2023) A Pareto-based two-stage evolutionary algorithm for flexible job shop scheduling problem with worker cooperation flexibility. Robot Comput Integr Manuf 82:102534. https://doi.org/10.1016/j.rcim.2023.102534 · DOI
  29. Ribas I, Leisten R, Framiñan JM (2010) Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective. Comput Oper Res 37:1439–1454. https://doi.org/10.1016/j.cor.2009.11.001 · DOI
  30. Ruiz R, Maroto C (2006) A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility. Eur J Oper Res 169:781–800. https://doi.org/10.1016/j.ejor.2004.06.038 · DOI
  31. Ruiz R, Vázquez-Rodríguez JA (2010) The hybrid flow shop scheduling problem. Eur J Oper Res 1–18. https://doi.org/10.1016/j.ejor.2009.09.024 · DOI
  32. Salvador MS (1973) A solution to a special class of flow shop scheduling problems. In: Elmaghraby SE (ed) Symposium on the Theory of Scheduling and Its Applications. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 83–91 · DOI
  33. Tang D, Dai M, Salido MA, Giret A (2016) Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Comput Ind 81:82–95. https://doi.org/10.1016/j.compind.2015.10.001 · DOI
  34. Tang L, Wang X (2011) A scatter search algorithm for a multistage production scheduling problem with blocking and semi-continuous batching machine. IEEE Trans Control Syst Technol 19:976–989. https://doi.org/10.1109/TCST.2010.2060201 · DOI
  35. Tavakkoli-Moghaddam R, Azarkish M, Sadeghnejad-Barkousaraie A (2011) A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem. Expert Syst Appl 38:10812–10821. https://doi.org/10.1016/j.eswa.2011.02.050 · DOI
  36. Wang S, Wang X, Chu F, Yu J (2020) An energy-efficient two-stage hybrid flow shop scheduling problem in a glass production. Int J Prod Res 58:2283–2314. https://doi.org/10.1080/00207543.2019.1624857 · DOI
  37. Wardono B, Fathi Y (2004) A tabu search algorithm for the multi-stage parallel machine problem with limited buffer capacities. Eur J Oper Res 155:380–401. https://doi.org/10.1016/S0377-2217(02)00873-1 · DOI
  38. Wittrock RJ (1988) An adaptable scheduling algorithm for flexible flow lines. Oper Res 36:445–453. https://doi.org/10.1287/opre.36.3.445 · DOI
  39. Yan J, Li L, Zhao F et al (2016) A multi-level optimization approach for energy-efficient flexible flow shop scheduling. J Clean Prod 137:1543–1552. https://doi.org/10.1016/j.jclepro.2016.06.161 · DOI
  40. Yazdani M, Zandieh M, Tavakkoli-Moghaddam R (2019) Evolutionary algorithms for multi-objective dual-resource constrained flexible job-shop scheduling problem. Opsearch 56:983–1006. https://doi.org/10.1007/s12597-019-00395- · DOI
  41. Zandieh M, Fatemi Ghomi SMT, Moattar Husseini SM (2006) An immune algorithm approach to hybrid flow shops scheduling with sequence-dependent setup times. Appl Math Comput 180:111–127. https://doi.org/10.1016/j.amc.2005.11.136 · DOI
  42. Zhang M, Yan J, Zhang Y, Yan S (2019) Optimization for energy-efficient flexible flow shop scheduling under time of use electricity tariffs. Procedia CIRP 80:251–256. https://doi.org/10.1016/j.procir.2019.01.062 · DOI
  43. Zheng X, Wang L (2016) A knowledge-guided fruit fly optimization algorithm for dual resource constrained flexible job-shop scheduling problem. Int J Prod Res 54:5554–5566. https://doi.org/10.1080/00207543.2016.1170226 · DOI
  44. Zhang TW, Dornfeld DA (2007) Energy use per worker-hour: evaluating the contribution of labor to manufacturing energy use. In: Takata S, Umeda Y (eds) Advances in Life Cycle Engineering for Sustainable Manufacturing Businesses. Springer, London. https://doi.org/10.1007/978-1-84628-935-4_33 · DOI
  45. Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength pareto evolutionary algorithm. TIK-report 103. https://doi.org/10.3929/ethz-a-004284029 · DOI

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Metadata

Authors
Mokhtari-Moghadam, A., Pourhejazy, P., & Gupta, D.
Affiliations
  • UiT The Arctic University of Norway (NO)
  • Wichita State University (US)
Corresponding author
Pourhejazy, Pourya
ORCID
Pourhejazy, Pourya
Venue
Environmental Science and Pollution Research
Publisher
Springer Science and Business Media LLC
Published
2023-04-24
ISSN
  • 0944-1344 (ISSN-L)
  • 1614-7499
DOI
10.1007/s11356-023-26986-3
OpenAlex
W4366816593
Cited by
18
References
0
Citations by year
2026: 2 · 2025: 5 · 2024: 9 · 2023: 2
FWCI
2.39
Type
journal article
Language
en
Open access
Licence
Locations
  • Environmental Science and Pollution Research (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Holmes Museum Of Anthropology (Wichita State University) (submittedVersion)
  • NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
Funders
UiT The Arctic University of Norway
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Scheduling and Optimization Algorithms
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Advanced Manufacturing and Logistics Optimization
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Healthcare Operations and Scheduling Optimization
Keywords
hybrid flow shop scheduling, sustainability, energy efficiency, time-of-use electricity pricing, worker flexibility, sequence-dependent setup times, multi-objective optimization, multi-objective genetic algorithm, production planning and control, industrial energy consumption

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