Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives
Source abstract
Abstract
In recent years, the application of artificial intelligence has been revolutionizing the manufacturing industry, becoming one of the key pillars of what has been called Industry 4.0. In this context, we focus on the job shop scheduling problem (JSP), which aims at productions orders to be carried out, but considering the reduction of energy consumption as a key objective to fulfill. Finding the best combination of machines and jobs to be performed is not a trivial problem and becomes even more involved when several objectives are taken into account. Among them, the improvement of energy savings may conflict with other objectives, such as the minimization of the makespan. In this paper, we provide an in-depth review of the existing literature on multi-objective job shop scheduling optimization with metaheuristics, in which one of the objectives is the minimization of energy consumption. We systematically reviewed and critically analyzed the most relevant features of both problem formulations and algorithms to solve them effectively. The manuscript also informs with empirical results the main findings of our bibliographic critique with a performance comparison among representative multi-objective evolutionary solvers applied to a diversity of synthetic test instances. The ultimate goal of this article is to carry out a critical analysis, finding good practices and opportunities for further improvement that stem from current knowledge in this vibrant research area.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Scheduling and Optimization Algorithms · Advanced Manufacturing and Logistics Optimization · Advanced Multi-Objective Optimization Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Kurago Software, S.L., Alameda Urquijo 4, 48008 Bilbao, Spain
TECNALIA, Basque Research & Technology Alliance (BRTA), P. Tecnologico Bizkaia, Ed. 700, 48160 Derio, Spain
Department of Communications Engineering, University of the Basque Country UPV/EHU, Alameda Urquijo S/N, 48013 Bilbao, Spain
Departamento de Lenguajes y Ciencias de la Computación, ITIS Software, University of Malaga, 29071 Malaga, Spain
Funding
Basque Government · Spanish Ministry of Science and Innovation · Andalusian PAIDI program
References (100+)
Show source-provided references
1. Industry 4.010.1007/s12599-014-0334-4 ↗
2. Erboz, G. (2017, January 1–2). How to define industry 4.0: Main pillars of industry 4.0. Proceedings of the 7th International Conference on Management (ICoM 2017), Managerial Trends in the Development of Enterprises in Globalization Era, Nitra, Slovakia.
3. Multistage quality control using machine learning in the automotive industry10.1109/ACCESS.2019.2923405 ↗
4. Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.010.1016/j.inffus.2018.10.005 ↗
5. Cheriyan, S., Ibrahim, S., Mohanan, S., and Treesa, S. (2018, January 16–17). Intelligent Sales Prediction Using Machine Learning Techniques. Proceedings of the 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Southend, UK.10.1109/iCCECOME.2018.8659115 ↗
6. Villalba-Diez, J., Schmidt, D., Gevers, R., Ordieres-Meré, J., Buchwitz, M., and Wellbrock, W. (2019). Deep learning for industrial computer vision quality control in the printing industry 4.0. Sensors, 19.10.3390/s19183987 ↗
7. Dynamic optimization of electric arc furnace operation10.1002/aic.11104 ↗
8. Hybrid method for aerodynamic shape optimization in automotive industry10.1016/j.compfluid.2003.06.007 ↗
9. De Muer, B., De Ranter, C., Crols, J., and Steyaert, M. (1999, January 5–8). A simulator-optimizer for the design of very low phase noise cmos lc-oscillators. Proceedings of the ICECS’99—6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No. 99EX357), Paphos, Cyprus.
10. A practical approach to job shop scheduling problems10.1109/70.210791 ↗
11. On the job shop scheduling problem10.1287/opre.8.2.219 ↗
12. Weise, T. (2021, December 22). An Introduction to Optimization Algorithms. Github. Available online: https://thomasweise.github.io/aitoa/.
13. Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison10.1145/937503.937505 ↗
14. A tutorial survey of job shop scheduling problems using genetic algorithms—I. Representation10.1016/0360-8352(96)00047-2 ↗
15. Houghton, J.T., Jenkins, G.J., and Ephraums, J.J. (1990). Climate Change, Cambridge University Press. U.S. Department of Energy, Office of Scientific and Technical Information.
16. Scientists’ warning to humanity: Microorganisms and climate change10.1038/s41579-019-0222-5 ↗
17. Benchmarks for basic scheduling problems10.1016/0377-2217(93)90182-M ↗
18. Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, Inc.
19. Coello Coello, C., Lamont, G., and van Veldhuizen, D. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems, John Wiley & Sons, Inc.. [2nd ed.].
20. Miettinen, K. (1999). Nonlinear Multiobjective Optimization, Kluwer Academic Publishers.10.1007/978-1-4615-5563-6 ↗
21. Why Is Optimization Difficult?10.1007/978-3-642-00267-0_1 ↗
22. Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations10.1007/s12559-020-09730-8 ↗
23. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II10.1109/4235.996017 ↗
24. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition10.1109/TEVC.2007.892759 ↗
25. SMS-EMOA: Multiobjective selection based on dominated hypervolume10.1016/j.ejor.2006.08.008 ↗
26. The job shop scheduling problem: Conventional and new solution techniques10.1016/0377-2217(95)00362-2 ↗
27. Scheduling theory and its applications10.1057/palgrave.jors.2600829 ↗
28. Davis, L. (1985, January 24–26). Job shop scheduling with genetic algorithms. Proceedings of the An International Conference on Genetic Algorithms and Their Applications, Pittsburgh, PA, USA.
29. A generalized permutation approach to job shop scheduling with genetic algorithms10.1007/BF01719250 ↗
30. Yamada, T., and Nakano, R. (1997, January 18–19). Genetic algorithms for job shop scheduling problems. Proceedings of the Modern Heuristic for Decision Support, London, UK.10.1049/PBCE055E_ch7 ↗
31. A novel hybrid meta-heuristic algorithm for solving multi objective flexible job shop scheduling10.1016/j.jmsy.2013.04.015 ↗
32. Genetic algorithms with feasible operators for solving job shop scheduling problem
33. A multi-objective PSO for job shop scheduling problems10.1016/j.eswa.2009.06.041 ↗
34. Multiobjective evolutionary algorithm for manufacturing scheduling problems: State-of-the-art survey10.1007/s10845-013-0804-4 ↗
35. Scopus database: A review10.1186/1742-5581-3-1 ↗
36. Hybrid flow shop scheduling considering machine electricity consumption cost10.1016/j.ijpe.2013.01.028 ↗
37. Parallel machine scheduling problems in green manufacturing industry10.1016/j.jmsy.2015.11.006 ↗
38. Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions10.1016/j.jclepro.2018.02.004 ↗
39. A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance10.1016/j.ijpe.2016.06.019 ↗
40. Stochastic multi-objective modelling and optimization of an energy-conscious distributed permutation flow shop scheduling problem with the total tardiness constraint10.1016/j.jclepro.2019.04.046 ↗
41. Bending, buckling and free vibration characteristics of FG-CNT-reinforced polymer composite beam under non-uniform thermal load
42. A multiobjective evolutionary algorithm based on decomposition for hybrid flowshop green scheduling problem10.1016/j.cie.2019.07.036 ↗
43. Energy-oriented scheduling for hybrid flow shop with limited buffers through efficient multi-objective optimization10.1109/ACCESS.2019.2904848 ↗
44. Öztop, H., Tasgetiren, M.F., Eliiyi, D.T., and Pan, Q.K. (2018). Green permutation flowshop scheduling: A trade-off-between energy consumption and total flow time. International Conference on Intelligent Computing, Springer.10.1007/978-3-319-95957-3_79 ↗
45. The flexible job shop scheduling problem considering deterioration effect and energy consumption simultaneously10.1016/j.cie.2019.06.048 ↗
46. Kinematic analysis and design of a 3-DOF translational parallel robot10.1007/s11633-017-1066-y ↗
47. Discrete evolutionary multi-objective optimization for energy-efficient blocking flow shop scheduling with setup time10.1016/j.asoc.2020.106343 ↗
48. Energy-efficient scheduling for multi-objective two-stage flow shop using a hybrid ant colony optimisation algorithm10.1080/00207543.2019.1642529 ↗
49. Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices10.1007/s11573-020-00971-5 ↗
50. Peng, C., Peng, T., Zhang, Y., Tang, R., and Hu, L. (2018). Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop. Energies, 11.10.3390/en11123382 ↗
51. The mixed production mode considering continuous and intermittent processing for an energy-efficient hybrid flow shop scheduling10.1016/j.jclepro.2019.119071 ↗
52. Decomposition-based 2-echelon multi-objective evolutionary algorithm with energy-efficient local search strategies for shop floor multi-crane scheduling problems10.1007/s00521-019-04608-9 ↗
53. Multi-objective green flowshop scheduling problem under uncertainty: Estimation of distribution algorithm10.1016/j.jclepro.2019.119734 ↗
54. Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization10.1016/j.jclepro.2019.06.151 ↗
55. An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time10.1080/00207543.2018.1504251 ↗
56. A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution10.1016/j.asoc.2018.11.043 ↗
57. A multi-objective differential evolution algorithm for parallel batch processing machine scheduling considering electricity consumption cost10.1016/j.cor.2018.04.009 ↗
58. A Multi-Objective Whale Swarm Algorithm for Energy-Efficient Distributed Permutation Flow shop Scheduling Problem with Sequence Dependent Setup Times10.1016/j.ifacol.2019.11.142 ↗
59. Bi-Objective Re-Entrant Hybrid Flow Shop Scheduling considering Energy Consumption Cost under Time-of-Use Electricity Tariffs10.1155/2020/8565921 ↗
60. A memetic algorithm for multi-objective distributed production scheduling: Minimizing the makespan and total energy consumption10.1007/s10845-019-01521-9 ↗
61. A collaborative optimization algorithm for energy-efficient multi-objective distributed no-idle flow-shop scheduling10.1016/j.swevo.2019.100557 ↗
62. Energy-Aware Production Scheduling in Flow Shop and Job Shop Environments Using a Multi-Objective Genetic Algorithm10.1080/10429247.2018.1544798 ↗
63. Multi-objective optimization of material delivery for mixed model assembly lines with energy consideration10.1016/j.jclepro.2018.04.251 ↗
64. Multi-objective programming of pumped-hydro-thermal scheduling problem using normal boundary intersection and VIKOR10.1016/j.energy.2017.09.144 ↗
65. A novel teaching-learning-based optimization algorithm for energy-efficient scheduling in hybrid flow shop10.1109/TEM.2017.2774281 ↗
66. A novel mathematical model and multi-objective method for the low-carbon flexible job shop scheduling problem
67. Reducing environmental impact of production during a Rolling Blackout policy—A multi-objective schedule optimisation approach10.1016/j.jclepro.2015.04.038 ↗
68. Investigation on the effectiveness of ABC algorithm for hydrothermal energy management considering emission aspects10.1108/IJESM-11-2013-0004 ↗
69. Optimal scheduling of single stage batch plants with direct heat integration10.1016/j.compchemeng.2015.07.006 ↗
70. Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm10.1016/j.rcim.2013.04.001 ↗
71. Kou, Y., Li, C., Li, L., Tang, Y., and Li, X. (2019, January 6–9). Energy-efficient rescheduling for the flexible machining systems with random machine breakdown and urgent job arrival. Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy.10.1109/SMC.2019.8914436 ↗
72. Energy aware scheduling in flexible flow shops with hybrid particle swarm optimization10.1016/j.cor.2020.105088 ↗
73. A bi-objective flexible flow shop scheduling problem with machine-dependent processing stages: Trade-off between production costs and energy consumption
74. Yang, D., Zhou, X., Yang, Z., Jiang, Q., and Feng, W. (2020, January 19–24). Multi-objective optimization Model for Flexible Job Shop Scheduling Problem Considering Transportation Constraints: A Comparative Study. Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK.10.1109/CEC48606.2020.9185653 ↗
75. Yuksel, D., Tasgetiren, M.F., Kandiller, L., and Pan, Q.K. (2020, January 19–24). Metaheuristics for Energy-Efficient No-Wait Flowshops: A Trade-off Between Makespan and Total Energy Consumption. Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK.10.1109/CEC48606.2020.9185554 ↗
76. A novel multi-objective optimization algorithm for the integrated scheduling of flexible job shops considering preventive maintenance activities and transportation processes10.1007/s00500-020-05347-z ↗
77. A New Multi-Objective Hybrid Flow Shop Scheduling Method to Fully Utilize the Residual Forging Heat10.1109/ACCESS.2020.3017239 ↗
78. Multi-objective integrated scheduling optimization of semi-combined marine crankshaft structure production workshop for green manufacturing10.1177/0142331220945917 ↗
79. Research on multi-objective optimization and simulation to HFSSP hybrid flow-shop scheduling problem for energy saving10.1088/1757-899X/504/1/012108 ↗
80. A Variable Iterated Local Search Algorithm for Energy-Efficient No-idle Flowshop Scheduling Problem10.1016/j.promfg.2020.01.351 ↗
81. Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times10.1016/j.energy.2020.118420 ↗
82. Study on multi-objective flexible job shop scheduling problem considering energy consumption
83. Efficient multi-objective algorithm for the lot-streaming hybrid flowshop with variable sub-lots10.1016/j.swevo.2019.100600 ↗
84. Energy-efficient job shop scheduling problem with variable spindle speed using a novel multi-objective algorithm10.1177/1687814017695959 ↗
85. Multi-objective reentrant hybrid flowshop scheduling with machines turning on and off control strategy using improved multi-verse optimizer algorithm10.1155/2019/2573873 ↗
86. Flexible Job-Shop Scheduling Problem with Maintenance Activities Considering Energy Consumption10.4028/www.scientific.net/AMM.521.707 ↗
87. An efficient memetic algorithm for distributed flexible job shop scheduling problem with transfers10.1016/j.eswa.2020.113721 ↗
88. Gao, Y., Wang, Q., Feng, Y., Zheng, H., Zheng, B., and Tan, J. (2018). An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line. Energies, 11.10.3390/en11051261 ↗
89. An energy-efficient multi-objective optimization for flexible job shop scheduling problem10.1016/j.compchemeng.2017.05.004 ↗
90. Energy-efficient flexible flow shop scheduling with worker flexibility10.1016/j.eswa.2019.112902 ↗
91. A multi-objective iterated local search algorithm for comprehensive energy-aware hybrid flow shop scheduling10.1016/j.jclepro.2019.03.155 ↗
92. A multi-objective approach to welding shop scheduling for makespan, noise pollution and energy consumption10.1016/j.jclepro.2018.06.137 ↗
93. Li, J., Li, Y., Yang, H., Gao, K., Wang, Y., and Sun, T. (2011). Flexible Job Shop Scheduling Problem by Chemical-Reaction Optimization Algorithm. Advanced Intelligent Computing, Springer.10.1007/978-3-642-24728-6_79 ↗
94. Game theory based real-time multi-objective flexible job shop scheduling considering environmental impact10.1016/j.jclepro.2017.08.068 ↗
95. Many-objective flexible job shop scheduling using NSGA-III combined with multi-attribute decision making10.1142/S0217984918401103 ↗
96. Pais, E. (2021, December 22). Keys to Understanding the Rising Cost of Electricity in Spain. Available online: https://english.elpais.com/economy-and-business/2021-08-13/keys-to-understanding-the-rising-cost-of-electricity-in-spain.html.
97. jMetal: A Java framework for multi-objective optimization10.1016/j.advengsoft.2011.05.014 ↗
98. A computational study of the job shop scheduling problem10.1287/ijoc.3.2.149 ↗
99. Machine sequencing via disjunctive graphs: An implicit enumeration algorithm10.1287/opre.17.6.941 ↗
100. The Shifting Bottleneck Procedure for Job Shop Scheduling10.1287/mnsc.34.3.391 ↗