Abstract
Energy-awareness in the industrial sectors has become a global consensus in recent decades. Green scheduling is acknowledged as an effective weapon to reduce energy consumption in the industrial sectors. Therefore, this paper is devoted to the green scheduling of flexible manufacturing cells (FMC) with auto-guided vehicle transportation, where conflict-free routing of the vehicles is considered. To deal with this problem, a bi-objective optimization model is proposed to achieve the minimization of the maximum completion time and the total energy consumption in an FMC. The studied problem is an extension of flexible job shop problem which is NP-hard. Thus, an improved bi-objective salp swarm algorithm based on decomposition (IMOSSA/D) is proposed and applied to the problem. The approach is based on the decomposition of the bi-objective problem. Salp swarm intelligence along with three stochastic-distribution-based operators are incorporated into the approach, to enhance and balance its exploring and exploiting ability. Computational experiments are performed to compare the proposed approach with two state-of-the-art algorithms. This study allows the decision makers to better trade-off between energy savings and production efficiency in flexible manufacturing cellular environment.
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Data availability
The datasets generated during and/or analysed during the current study are not publicly available due to that the data also forms part of an ongoing study, but are available from the corresponding author on reasonable request.
References
Balakrishnan K, Dhanalakshmi R, Khaire UM (2022) A novel control factor and brownian motion-based improved harris hawks optimization for feature selection. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03621-y
Barshandeh S, Dana R, Eskandarian P (2022) A learning automata-based hybrid mpa and js algorithm for numerical optimization problems and its application on data clustering. Knowledge-Based Syst 236:42. https://doi.org/10.1016/j.knosys.2021.107682
Bechtsis D, Tsolakis N, Vlachos D, Iakovou E (2017) Sustainable supply chain management in the digitalisation era: the impact of automated guided vehicles. J Clean Prod 142:3970–3984. https://doi.org/10.1016/j.jclepro.2016.10.057
Chou YL, Yang JM, Wu CH (2020) An energy-aware scheduling algorithm under maximum power consumption constraints. J Manuf Syst 57:182–197. https://doi.org/10.1016/j.jmsy.2020.09.004
Chutima P, Arayikanon K (2020) Many-objective low-cost airline cockpit crew rostering optimisation. Comput Ind Eng 150:12. https://doi.org/10.1016/j.cie.2020.106844
Dagal I, Akin B, Akboy E (2022) Improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems. Int J Energy Res 46(7):8742–8759. https://doi.org/10.1002/er.7753
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182–197
Deliktas D, Ozcan E, Ustun O, Torkul O (2021) Evolutionary algorithms for multi-objective flexible job shop cell scheduling. Appl Soft Comput 113:18. https://doi.org/10.1016/j.asoc.2021.107890
Diaz JL, Ocampo-Martinez C (2019) Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies. J Manuf Syst 52:131–145. https://doi.org/10.1016/j.jmsy.2019.05.002
Dong H, Xu YL, Li XP, Yang ZL, Zou CH (2021) An improved antlion optimizer with dynamic random walk and dynamic opposite learning. Knowledge-Based Syst. https://doi.org/10.1016/j.knosys.2021.106752
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: Optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26(1):29–41. https://doi.org/10.1109/3477.484436
Feng YX, Hong ZX, Li ZW, Zheng H, Tan JR (2020) Integrated intelligent green scheduling of sustainable flexible workshop with edge computing considering uncertain machine state. J Clean Prod 246:18. https://doi.org/10.1016/j.jclepro.2019.119070
Fontes D, Homayouni SM (2019) Joint production and transportation scheduling in flexible manufacturing systems. J Global Optim 74(4):879–908. https://doi.org/10.1007/s10898-018-0681-7
Gao KZ, Cao ZG, Zhang L, Chen ZH, Han YY, Pan QK (2019) A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems. Ieee-Caa J Autom Sinica 6(4):904–916. https://doi.org/10.1109/jas.2019.1911540
Gao KZ, Huang Y, Sadollah A, Wang L (2020) A review of energy-efficient scheduling in intelligent production systems. Complex Intell Syst 6(2):237–249. https://doi.org/10.1007/s40747-019-00122-6
Gupta S, Deep K (2019) A novel random walk grey wolf optimizer. Swarm Evol Comput 44:101–112. https://doi.org/10.1016/j.swevo.2018.01.001
He LJ, Chiong R, Li WF, Budhi GS, Zhang Y (2022) A multiobjective evolutionary algorithm for achieving energy efficiency in production environments integrated with multiple automated guided vehicles. Knowledge-Based Syst 243:24. https://doi.org/10.1016/j.knosys.2022.108315
Hegazy AE, Makhlouf MA, El-Tawel GS (2020) Improved salp swarm algorithm for feature selection. J King Saud Univ-Comput Inf Sci 32(3):335–344. https://doi.org/10.1016/j.jksuci.2018.06.003
Ito Y (2013) Similarity evaluation for flexible manufacturing cell: an interesting application of graph theory to manufacture. Thought-Evoking Approaches in Engineering Problems
Jurisch B (1995) Lower bounds for the job-shop scheduling problem on multipurpose machines. Discret Appl Math 58(2):145–156. https://doi.org/10.1016/0166-218x(93)e0124-h
Kansal V, Dhillon JS (2020) Emended salp swarm algorithm for multiobjective electric power dispatch problem. Appl Soft Comput 90:26. https://doi.org/10.1016/j.asoc.2020.106172
Kaya S, Gumuscu A, Aydilek IB, Karacizmeli IH, Tenekeci ME (2021) Solution for flow shop scheduling problems using chaotic hybrid firefly and particle swarm optimization algorithm with improved local search. Soft Comput 25(10):7143–7154. https://doi.org/10.1007/s00500-021-05673-w
Luo JP, Yang Y, Li X, Liu QQ, Chen MR, Gao KZ (2018) A decomposition-based multi-objective evolutionary algorithm with quality indicator. Swarm Evol Comput 39:339–355. https://doi.org/10.1016/j.swevo.2017.11.004
Luo S, Zhang LX, Fan YS (2019) Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. J Clean Prod 234:1365–1384. https://doi.org/10.1016/j.jclepro.2019.06.151
Mashwani WK, Salhi A (2014) Multiobjective memetic algorithm based on decomposition. Appl Soft Comput 21:221–243. https://doi.org/10.1016/j.asoc.2014.03.007
Metzler R, Klafter J (2000) The random walk’s guide to anomalous diffusion: a fractional dynamics approach. Phys Rep-Rev Sec Phys Lett 339(1):1–77. https://doi.org/10.1016/s0370-1573(00)00070-3
Mirjalili S, Saremi S, Mirjalili SM, Coelho LD (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119. https://doi.org/10.1016/j.eswa.2015.10.039
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
Pan JS, Shan J, Zheng SG, Chu SC, Chang CK (2021) Wind power prediction based on neural network with optimization of adaptive multi-group salp swarm algorithm. Cluster Comput. https://doi.org/10.1007/s10586-021-03247-x
Peng W, Zhang QF (2008) A decomposition-based multi-objective particle swarm optimization algorithm for continuous optimization problems. Ieee, New York
Shao WS, Shao ZS, Pi DC (2022) An ant colony optimization behavior-based moea/d for distributed heterogeneous hybrid flow shop scheduling problem under nonidentical time-of-use electricity tariffs. IEEE Trans Autom Sci Eng 19(4):3379–3394. https://doi.org/10.1109/tase.2021.3119353
Tan WH, Yuan XF, Yang YH, Wu LH (2022) Multi-objective casting production scheduling problem by a neighborhood structure enhanced discrete nsga-ii: an application from real-world workshop. Soft Comput 26(17):8911–8928. https://doi.org/10.1007/s00500-021-06697-y
Tuysuz F, Kahraman C (2010) Modeling a flexible manufacturing cell using stochastic petri nets with fuzzy parameters. Expert Syst Appl 37(5):3910–3920. https://doi.org/10.1016/j.eswa.2009.11.026
Wang WX, Li KS, Tao XZ, Gu FH (2020) An improved moea/d algorithm with an adaptive evolutionary strategy. Inf Sci 539:1–15. https://doi.org/10.1016/j.ins.2020.05.082
Wang H, Sheng BY, Lu QB, Yin XY, Zhao FY, Lu XC, Luo RP et al (2021) A novel multi-objective optimization algorithm for the integrated scheduling of flexible job shops considering preventive maintenance activities and transportation processes. Soft Comput 25(4):2863–2889. https://doi.org/10.1007/s00500-020-05347-z
Yi Z, Yangkun Z, Hongda Y, Hong W (2022) Application of an improved discrete salp swarm algorithm to the wireless rechargeable sensor network problem. Front Bioeng Biotechnol 10:18. https://doi.org/10.3389/fbioe.2022.923798
Yin Y, Stecke KE, Li DN (2018) The evolution of production systems from industry 2.0 through industry 4.0. Int J Prod Res 56(1–2):848–861. https://doi.org/10.1080/00207543.2017.1403664
Zhang Q, Hui L (2008) Moea/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731
Zhao XF, Liu HZ, Lin SX, Chen YK (2020) Design and implementation of a multiple agv scheduling algorithm for a job-shop. Int J Simul Model 19(1):134–145. https://doi.org/10.2507/ijsimm19-1-co2
Zhou B, He Z (2020) A material handling scheduling method for mixed-model automotive assembly lines based on an improved static kitting strategy. Comput Ind Eng. https://doi.org/10.1016/j.cie.2020.106268
Zhou B, He Z (2021) A static semi-kitting strategy system of jit material distribution scheduling for mixed-flow assembly lines. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2021.115523
Zhou B, Lei Y (2021) Bi-objective grey wolf optimization algorithm combined levy flight mechanism for the fmc green scheduling problem. Soft Comput, Appl. https://doi.org/10.1016/j.asoc.2021.107717
Zhou BH, Liao XM (2020) Particle filter and levy flight-based decomposed multi-objective evolution hybridized particle swarm for flexible job shop greening scheduling with crane transportation. Appl Soft Comput 91:18. https://doi.org/10.1016/j.asoc.2020.106217
Zhou B, Zhu Z (2021a) Multi-objective optimization of greening scheduling problems of part feeding for mixed model assembly lines based on the robotic mobile fulfillment system. Neural Comput Appl 33(16):9913–9937. https://doi.org/10.1007/s00521-021-05761-w
Zhu YW, Qin YH, Yang D, Xu HY, Zhou HB (2023) An enhanced decomposition-based multi-objective evolutionary algorithm with a self-organizing collaborative scheme. Expert Syst Appl 213:25. https://doi.org/10.1016/j.eswa.2022.118915
Ziaee M, Mortazavi J, Amra M (2022) Flexible job shop scheduling problem considering machine and order acceptance, transportation costs, and setup times. Soft Comput 26(7):3527–3543. https://doi.org/10.1007/s00500-021-06481-y
Zitzler E and Künzli S (2004) Indicator-based selection in multiobjective search. Lecture notes in computer science
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All authors contributed to the study conception and design. BZ Proposed the research goals and aims. Verified the overall results, experiments and other outputs. Provided the computing resources and analysis tools. Conducted the statistical analysis. Reviewed the initial draft and revised the manuscript. In charge of acquisition of the financial support for the project leading to this publication. JZ Developed and designed the mathematical model and methodology. Performed the software development and experiments. Conducted the statistical analysis. Wrote the initial draft and revised the manuscript. All authors read and approved the final manuscript.
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Zhou, BH., Zhang, JH. An improved bi-objective salp swarm algorithm based on decomposition for green scheduling in flexible manufacturing cellular environments with multiple automated guided vehicles. Soft Comput 27, 16717–16740 (2023). https://doi.org/10.1007/s00500-023-09016-9
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DOI: https://doi.org/10.1007/s00500-023-09016-9