Energy-Efficient Hybrid Flow-Shop Scheduling under Time-of-Use and Ladder Electricity Tariffs
Source abstract
Abstract
With the increasing influence of climate change, green development has become an important trend. Since manufacturing represents approximately one-half of total energy consumption, reducing the amount of energy consumed within this industry is imperative. This study provides a hybrid flow shop scheduling issue under a time-of-use and ladder electricity price system to reduce total energy consumption without compromising maximum completion time. An improved non-dominated sorting genetic algorithm II with some optimization strategies is proposed to solve the problem. First, an enhanced constructive heuristic algorithm is used to improve the quantity of initial solution in the initialization. Besides, an adaptive genetic operation is introduced, aiming to avoid the emergence of locally optimal solutions. In addition, the right-shift approach is developed to reduce the total energy consumption without affecting completion time. By maintaining the production efficiency and reducing the energy consumption cost by 4.33%. A trade-off proposal is made between productivity and sustainability in view of the calculation results.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Scheduling and Optimization Algorithms · Smart Grid Energy Management · Energy Efficiency and Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Management and Economics, Tianjin University, Tianjin 300072, China
APEC Sustainable Energy Center, Tianjin University, Tianjin 300072, China
School of Architecture, Tianjin University, Tianjin 300072, China
School of Civil & Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Funding
National Social Science Foundation of China · National Natural Science Foundation of China
References (59)
Show source-provided references
1. Decarbonization of industry: Guidelines towards a harmonized energy efficiency policy program impact evaluation methodology10.1016/j.egyr.2021.02.067 ↗
2. The heterogeneity of renewable energy consumption, carbon emission and financial development in the globe: A panel quantile regression approach10.1016/j.egyr.2020.04.002 ↗
3. (2022, April 28). CEC 2020 Electricity Consumption Data of China Released. Available online: https://english.cec.org.cn/detail/index.html?3-1109.
4. Energy efficiency rebound effect research of China’s coal industry10.1016/j.egyr.2021.08.131 ↗
5. Smart manufacturing technology, market maturity analysis and technology roadmap in the computer and electronic product manufacturing industry10.1016/j.techfore.2018.03.005 ↗
6. Efficiency of the solution representations for the hybrid flow shop scheduling problem with makespan objective10.1016/j.cor.2019.05.002 ↗
7. Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions10.1016/j.jclepro.2018.02.004 ↗
8. Flow shop scheduling problem with conflict graphs10.1007/s10479-017-2560-x ↗
9. Two-machine flow shop and open shop scheduling problems with a single maintenance window10.1016/j.ejor.2018.04.019 ↗
10. An energy-efficient permutation flowshop scheduling problem10.1016/j.eswa.2020.113279 ↗
11. The impact of various carbon reduction policies on green flowshop scheduling10.1016/j.apenergy.2019.04.155 ↗
12. Wu, X., Shen, X., and Cui, Q. (2018). Multi-Objective Flexible Flow Shop Scheduling Problem Considering Variable Processing Time due to Renewable Energy. Sustainability, 10.10.3390/su10030841 ↗
13. A review of energy-efficient scheduling in intelligent production systems10.1007/s40747-019-00122-6 ↗
14. Energy-efficient scheduling in manufacturing companies: A review and research framework10.1016/j.ejor.2015.07.017 ↗
15. Decomposition based multiobjective evolutionary algorithm with adaptive resource allocation for energy-aware welding shop scheduling problem10.1016/j.cie.2021.107778 ↗
16. Solving energy-efficient distributed job shop scheduling via multi-objective evolutionary algorithm with decomposition10.1016/j.swevo.2020.100745 ↗
17. A simulation based approach to realize green factory from unit green manufacturing processes10.1016/j.jclepro.2018.02.025 ↗
18. Energy-efficient scheduling in an unrelated parallel-machine environment under time-of-use electricity tariffs
19. Adaptive selection multi-objective optimization method for hybrid flow shop green scheduling under finite variable parameter constraints: Case study10.1080/00207543.2021.1933239 ↗
20. Multi-process production scheduling with variable renewable integration and demand response10.1016/j.ejor.2019.08.017 ↗
21. A bi-objective flexible flow shop scheduling problem with machine-dependent processing stages: Trade-off between production costs and energy consumption
22. Integrated scheduling of rolling sector in steel production with consideration of energy consumption under time-of-use electricity prices10.1016/j.compchemeng.2017.12.018 ↗
23. Impact factors analysis on the probability characterized effects of time of use demand response tariffs using association rule mining method10.1016/j.enconman.2019.111891 ↗
24. Optimal scheduling of household appliances for demand response10.1016/j.epsr.2014.04.012 ↗
25. A review of energy efficiency in ports: Operational strategies, technologies and energy management systems10.1016/j.rser.2019.04.069 ↗
26. Identification of reservation capacity in critical peak pricing electricity demand response program for sustainable manufacturing systems10.1002/er.3077 ↗
27. Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty10.1016/j.omega.2021.102445 ↗
28. Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices
29. Federation model of TOU and ladder price based on customer satisfaction and Ramsey pricing
30. A New Many-objective Green Dynamic Scheduling Disruption Management Approach for Machining Workshop Based on Green Manufacturing10.1016/j.jclepro.2021.126489 ↗
31. Zeng, Z., Chen, X., and Wang, K. (2021). Energy Saving for Tissue Paper Mills by Energy-Efficiency Scheduling under Time-of-Use Electricity Tariffs. Processes, 9.10.3390/pr9020274 ↗
32. Energy-conscious unrelated parallel machine scheduling under time-of-use electricity tariffs10.1016/j.jclepro.2017.04.018 ↗
33. Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm10.1007/s10845-017-1385-4 ↗
34. Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times10.1016/j.energy.2020.118420 ↗
35. An energy-efficient bi-objective no-wait permutation flowshop scheduling problem to minimize total tardiness and total energy consumption10.1016/j.cie.2020.106431 ↗
36. Iris, C., and Yenisey, M.M. (2012, January 24–26). Multi-item simultaneous lot sizing and storage allocation with production and warehouse capacities. Proceedings of the Third International Conference on Computational Logistics, Shanghai, China.10.1007/978-3-642-33587-7_10 ↗
37. Algorithms for robust production scheduling with energy consumption limits10.1016/j.cie.2017.08.011 ↗
38. Is it more effective to bring time-of-use pricing into increasing block tariffs? Evidence from evaluation of residential electricity price policy in Anhui province10.1016/j.jclepro.2018.01.209 ↗
39. Research on energy-saving scheduling problem of flow shop under TOU and tiered electricity price
40. An efficient scheduling approach for an iron-steel plant equipped with self-generation equipment under time-of-use electricity tariffs10.1016/j.swevo.2020.100764 ↗
41. A meta-heuristic to solve the just-in-time job-shop scheduling problem10.1016/j.ejor.2020.04.017 ↗
42. Scheduling model of electric vehicles charging considering inconvenience and dynamic electricity prices10.1016/j.apenergy.2020.115455 ↗
43. A “Branch-and-Bound” Algorithm for the Exact Solution of the Three-Machine Scheduling Problem10.1057/jors.1965.7 ↗
44. On the theory of dynamic programming10.1073/pnas.38.8.716 ↗
45. A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem10.1016/0305-0483(83)90088-9 ↗
46. Holland, J.H. (1975). Adaptation in Natural and Artificial Systems, MIT Press.
47. Optimization by simulated annealing10.1126/science.220.4598.671 ↗
48. Glover, F., and Laguna, M. (1998). Tabu search. Handbook of Combinatorial Optimization, Springer.10.1007/978-1-4615-6089-0 ↗
49. Babor, M., Senge, J., and Rosell, C. (2021). Optimization of No-Wait Flowshop Scheduling Problem in Bakery Production with Modified PSO, NEH and SA. Processes, 9.10.3390/pr9112044 ↗
50. A fast and elitist multiobjective genetic algorithm: NSGA-II10.1109/4235.996017 ↗
51. MOEA/D: A multiobjective evolutionary algorithm based on decomposition10.1109/TEVC.2007.892759 ↗
52. An effective multi-objective whale swarm algorithm for energy-efficient scheduling of distributed welding flow shop10.1007/s10479-021-03952-1 ↗
53. Green scheduling of distributed two-stage reentrant hybrid flow shop considering distributed energy resources and energy storage system10.1016/j.cie.2022.108146 ↗
54. Energy-aware flexible job shop scheduling under time-of-use pricing10.1016/j.ijpe.2022.108507 ↗
55. Energy-carbon footprint optimization in sequence-dependent production scheduling10.1016/j.apenergy.2022.118949 ↗
56. Multi-objective Q-learning-based hyper-heuristic with Bi-criteria selection for energy-aware mixed shop scheduling10.1016/j.swevo.2021.100985 ↗
57. Dimensional Metrology of Cell-matrix Interactions in 3D Microscale Fibrous Substrates10.1016/j.procir.2017.04.009 ↗
58. Consumer demand for time of use electricity tariffs: A systematized review of the empirical evidence10.1016/j.rser.2018.08.040 ↗
59. Hassanat, A., Almohammadi, K., Alkafaween, E., Abunawas, E., Hammouri, A., and Prasath, V.B.S. (2019). Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach. Information, 10.10.3390/info10120390 ↗