Parallel machine scheduling with a total energy consumption limitation for minimizing total completion time

References

• Abedi, Mehdi, Raymond Chiong, Nasimul Noman, and Rui Zhang. 2020. “A Multi-Population, Multi-Objective Memetic Algorithm for Energy-Efficient Job-Shop Scheduling with Deteriorating Machines.” Expert Systems with Applications 157:113348. https://doi.org/10.1016/j.eswa.2020.113348.
   Web of Science ®Google Scholar
• Abikarram, Jose Batista, Katie McConky, and Ruben Proano. 2019. “Energy Cost Minimization for Unrelated Parallel Machine Scheduling Under Real Time and Demand Charge Pricing.” Journal of Cleaner Production 208:232–242. https://doi.org/10.1016/j.jclepro.2018.10.048.
   Web of Science ®Google Scholar
• Aghelinejad, MohammadMohsen, Yassine Ouazene, and Alice Yalaoui. 2018. “Production Scheduling Optimisation with Machine State and Time-Dependent Energy Costs.” International Journal of Production Research 56 (16): 5558–5575. https://doi.org/10.1080/00207543.2017.1414969.
   Web of Science ®Google Scholar
• Al-Dulaimi, Waleed Abood Mohammed, and Suaad Hadi Hassan Al-Taai. 2021. “Pollution and Its Impact on Sustainable Development.” In Proceedings of the First International Virtual Conference on Environment & Natural Resources, IOP Conference Series: Earth and Environmental Science 790:012025. Bristol, UK: IOP Publishing. https://doi.org/10.1088/1755-1315/790/1/012025.
   Google Scholar
• Anghinolfi, Davide, Massimo Paolucci, and Roberto Ronco. 2021. “A Bi-Objective Heuristic Approach for Green Identical Parallel Machine Scheduling.” European Journal of Operational Research 289 (2): 416–434. https://doi.org/10.1016/j.ejor.2020.07.020.
   Web of Science ®Google Scholar
• Bruzzone, Alessandro A. G., Davide Anghinolfi, Massimo Paolucci, and Flavio Tonelli. 2012. “Energy-Aware Scheduling for Improving Manufacturing Process Sustainability: A Mathematical Model for Flexible Flow Shops.” CIRP Annals 61 (1): 459–462. https://doi.org/10.1016/j.cirp.2012.03.084.
   Web of Science ®Google Scholar
• Che, Ada, Ke Lv, Eugene Levner, and Vladimir Kats. 2015. “Energy Consumption Minimization for Single Machine Scheduling with Bounded Maximum Tardiness.” In Proceedings of the 2015 IEEE 12th International Conference on Networking, Sensing and Control, 146–150. Piscataway, NJ: IEEE.
   Google Scholar
• Chen, Tzu-Li, Chen-Yang Cheng, and Yi-Han Chou. 2020. “Multi-Objective Genetic Algorithm for Energy-Efficient Hybrid Flow Shop Scheduling with Lot Streaming.” Annals of Operations Research290 (1–2): 813–836. https://doi.org/10.1007/s10479-018-2969-x.
   Web of Science ®Google Scholar
• Chen, Lu, Jinfeng Wang, and Xianyang Xu. 2019. “An Energy-Efficient Single Machine Scheduling Problem with Machine Reliability Constraints.” Computers & Industrial Engineering 137:106072. https://doi.org/10.1016/j.cie.2019.106072.
   Google Scholar
• Chou, Ywh-Leh, Ju-Min Yang, and Cheng-Hung Wu. 2020. “An Energy-Aware Scheduling Algorithm Under Maximum Power Consumption Constraints.” Journal of Manufacturing Systems 57:182–197. https://doi.org/10.1016/j.jmsy.2020.09.004.
   Web of Science ®Google Scholar
• Cota, Luciano P., Frederico G. Guimarães, Roberto G. Ribeiro, Ivan R. Meneghini, Fernando B. de Oliveira, Marcone J. F. Souza, and Patrick Siarry. 2019. “An Adaptive Multi-Objective Algorithm Based on Decomposition and Large Neighborhood Search for a Green Machine Scheduling Problem.” Swarm and Evolutionary Computation 51:100601. https://doi.org/10.1016/j.swevo.2019.100601.
   Web of Science ®Google Scholar
• Dai, Min, Dunbing Tang, Adriana Giret, and Miguel A. Salido. 2019. “Multi-Objective Optimization for Energy-Efficient Flexible Job Shop Scheduling Problem with Transportation Constraints.” Robotics and Computer-Integrated Manufacturing 59:143–157. https://doi.org/10.1016/j.rcim.2019.04.006.
   Web of Science ®Google Scholar
• Dang, Quang-Vinh, Thijs van Diessen, Tugce Martagan, and Ivo Adan. 2021. “A Matheuristic for Parallel Machine Scheduling with Tool Replacements.” European Journal of Operational Research 291 (2): 640–660. https://doi.org/10.1016/j.ejor.2020.09.050.
   Web of Science ®Google Scholar
• Ding, Jian-Ya, Shiji Song, Rui Zhang, Raymond Chiong, and Cheng Wu. 2015. “Parallel Machine Scheduling Under Time-of-Use Electricity Prices: New Models and Optimization Approaches.” IEEE Transactions on Automation Science and Engineering 13 (2): 1138–1154. https://doi.org/10.1109/TASE.2015.2495328.
   Web of Science ®Google Scholar
• Ebrahimi, Ahmad, Hyun Woo Jeon, Seokgi Lee, and Chao Wang. 2020. “Minimizing Total Energy Cost and Tardiness Penalty for a Scheduling-Layout Problem in a Flexible Job Shop System: A Comparison of Four Metaheuristic Algorithms.” Computers & Industrial Engineering 141:106295. https://doi.org/10.1016/j.cie.2020.106295.
   Web of Science ®Google Scholar
• Fang, Kan, Nelson A. Uhan, Zhao Fu, and John W. Sutherland. 2013. “Flow Shop Scheduling with Peak Power Consumption Constraints.” Annals of Operations Research 206 (1): 115–145. https://doi.org/10.1007/s10479-012-1294-z.
   Web of Science ®Google Scholar
• Fazli Khalaf, Alireza, and Yong Wang. 2018. “Energy-Cost-Aware Flow Shop Scheduling Considering Intermittent Renewables, Energy Storage, and Real-Time Electricity Pricing.” International Journal of Energy Research 42 (12): 3928–3942. https://doi.org/10.1002/er.v42.12.
   Google Scholar
• Framinan, Jose M., and Paz Perez-Gonzalez. 2017. “New Approximate Algorithms for the Customer Order Scheduling Problem with Total Completion Time Objective.” Computers & Operations Research78:181–192. https://doi.org/10.1016/j.cor.2016.09.010.
   Google Scholar
• Gaggero, Mauro, Massimo Paolucci, and Roberto Ronco. 2023. “Exact and Heuristic Solution Approaches for Energy-Efficient Identical Parallel Machine Scheduling with Time-of-Use Costs.” European Journal of Operational Research 311 (3): 845–866. https://doi.org/10.1016/j.ejor.2023.05.040.
   Google Scholar
• Gao, Kaizhou, Yun Huang, Ali Sadollah, and Ling Wang. 2020. “A Review of Energy-Efficient Scheduling in Intelligent Production Systems.” Complex & Intelligent Systems 6 (2): 237–249. https://doi.org/10.1007/s40747-019-00122-6.
   Web of Science ®Google Scholar
• Ghorbanzadeh, Masoumeh, and Mohammad Ranjbar. 2023. “Energy-Aware Production Scheduling in the Flow Shop Environment Under Sequence-Dependent Setup Times, Group Scheduling and Renewable Energy Constraints.” European Journal of Operational Research 307 (2): 519–537. https://doi.org/10.1016/j.ejor.2022.09.034.
   Google Scholar
• Gong, Guiliang, Raymond Chiong, Qianwang Deng, Xuran Gong, Wenhui Lin, Wenwu Han, and Like Zhang. 2022. “A Two-Stage Memetic Algorithm for Energy-Efficient Flexible Job Shop Scheduling by Means of Decreasing the Total Number of Machine Restarts.” Swarm and Evolutionary Computation75:101131. https://doi.org/10.1016/j.swevo.2022.101131.
   Web of Science ®Google Scholar
• Gong, Guiliang, Raymond Chiong, Qianwang Deng, Wenwu Han, Like Zhang, Wenhui Lin, and Kexin Li. 2020. “Energy-Efficient Flexible Flow Shop Scheduling with Worker Flexibility.” Expert Systems with Applications 141:112902. https://doi.org/10.1016/j.eswa.2019.112902.
   Web of Science ®Google Scholar
• Graham, Ronald Lewis, Eugene Leighton Lawler, Jan Karel Lenstra, and A. H. G. Rinnooy Kan. 1979. “Optimization and Approximation in Deterministic Sequencing and Scheduling: A Survey.” Annals of Discrete Mathematics 5:287–326. https://doi.org/10.1016/S0167-5060(08)70356-X.
   Google Scholar
• He, Lijun, Yulian Cao, Wenfeng Li, Jingjing Cao, and Lingchong Zhong. 2022. “Optimization of Energy-Efficient Open Shop Scheduling with An Adaptive Multi-Objective Differential Evolution Algorithm.” Applied Soft Computing 118:108459. https://doi.org/10.1016/j.asoc.2022.108459.
   Google Scholar
• He, Lijun, Raymond Chiong, and Wenfeng Li. 2022. “Energy-Efficient Open-Shop Scheduling with Multiple Automated Guided Vehicles and Deteriorating Jobs.” Journal of Industrial Information Integration 30:100387. https://doi.org/10.1016/j.jii.2022.100387.
   Google Scholar
• Heydar, Mojtaba, Elham Mardaneh, and Ryan Loxton. 2022. “Approximate Dynamic Programming for An Energy-Efficient Parallel Machine Scheduling Problem.” European Journal of Operational Research302 (1): 363–380. https://doi.org/10.1016/j.ejor.2021.12.041.
   Web of Science ®Google Scholar
• Ji, Min, Jen-Ya Wang, and Wen-Chiung Lee. 2013. “Minimizing Resource Consumption on Uniform Parallel Machines with a Bound on Makespan.” Computers & Operations Research 40 (12): 2970–2974. https://doi.org/10.1016/j.cor.2013.06.011.
   Web of Science ®Google Scholar
• Jiang, Xiaojuan, Kangbok Lee, and Michael L. Pinedo. 2021. “Ideal Schedules in Parallel Machine Settings.” European Journal of Operational Research 290 (2): 422–434. https://doi.org/10.1016/j.ejor.2020.08.010.
   Web of Science ®Google Scholar
• Jiang, Yiwei, Xuelian Tang, Kai Li, T. C. E. Cheng, and Min Ji. 2023. “Approximation Algorithms for Bi-Objective Parallel-Machine Scheduling in Green Manufacturing.” Computers & Industrial Engineering 176:108949. https://doi.org/10.1016/j.cie.2022.108949.
   Google Scholar
• Lee, Chung-Yee, and Surya Danusaputro Liman. 1993. “Capacitated Two-Parallel Machines Scheduling to Minimize Sum of Job Completion Times.” Discrete Applied Mathematics 41 (3): 211–222. https://doi.org/10.1016/0166-218X(90)90055-H.
   Web of Science ®Google Scholar
• Li, Kai, Jianfu Chen, Hong Fu, Zhaohong Jia, and Weizhong Fu. 2019. “Uniform Parallel Machine Scheduling with Fuzzy Processing Times Under Resource Consumption Constraint.” Applied Soft Computing 82:105585. https://doi.org/10.1016/j.asoc.2019.105585.
   Web of Science ®Google Scholar
• Li, Weidong, and Jinwen Ou. 2023. “Approximation Algorithms for Scheduling Parallel Machines with An Energy Constraint in Green Manufacturing.” European Journal of Operational Research 314 (3): 882–893. https://doi.org/10.1016/j.ejor.2023.11.008.
   Google Scholar
• Li, Debiao, Jing Wang, Rui Qiang, and Raymond Chiong. 2021. “A Hybrid Differential Evolution Algorithm for Parallel Machine Scheduling of Lace Dyeing Considering Colour Families, Sequence-Dependent Setup and Machine Eligibility.” International Journal of Production Research 59 (9): 2722–2738. https://doi.org/10.1080/00207543.2020.1740341.
   Web of Science ®Google Scholar
• Li, Kai, Xun Zhang, Joseph Y.-T. Leung, and Shan-Lin Yang. 2016. “Parallel Machine Scheduling Problems in Green Manufacturing Industry.” Journal of Manufacturing Systems 38:98–106. https://doi.org/10.1016/j.jmsy.2015.11.006.
   Web of Science ®Google Scholar
• Liu, Zhaohui, Wen-Chiung Lee, and Jen-Ya Wang. 2016. “Resource Consumption Minimization with a Constraint of Maximum Tardiness on Parallel Machines.” Computers & Industrial Engineering97:191–201. https://doi.org/10.1016/j.cie.2016.05.011.
   Web of Science ®Google Scholar
• Liu, Xiang, Fengxing Zou, and Xiangping Zhang. 2008. “Mathematical Model and Genetic Optimization for Hybrid Flow Shop Scheduling Problem Based on Energy Consumption.” In Proceedings of the 2008 Chinese Control and Decision Conference, 1002–1007. Piscataway, NJ: IEEE.
   Google Scholar
• Marichelvam, M. K., M. Geetha, and Ömür Tosun. 2020. “An Improved Particle Swarm Optimization Algorithm to Solve Hybrid Flowshop Scheduling Problems with the Effect of Human Factors—A Case Study.” Computers & Operations Research 114:104812. https://doi.org/10.1016/j.cor.2019.104812.
   Web of Science ®Google Scholar
• Masmoudi, Oussama, Xavier Delorme, and Paolo Gianessi. 2019. “Job-Shop Scheduling Problem with Energy Consideration.” International Journal of Production Economics 216:12–22. https://doi.org/10.1016/j.ijpe.2019.03.021.
   Web of Science ®Google Scholar
• Mellouli, Racem, Chérif Sadfi, Chengbin Chu, and Imed Kacem. 2009. “Identical Parallel-Machine Scheduling Under Availability Constraints to Minimize the Sum of Completion Times.” European Journal of Operational Research 197 (3): 1150–1165. https://doi.org/10.1016/j.ejor.2008.03.043.
   Web of Science ®Google Scholar
• Mitra, Amitava. 2011. “The Taguchi Method.” Wiley Interdisciplinary Reviews: Computational Statistics3 (5): 472–480. https://doi.org/10.1002/wics.v3.5.
   Google Scholar
• Módos, István, Přemysl Šucha, and Zdeněk Hanzálek. 2021. “On Parallel Dedicated Machines Scheduling Under Energy Consumption Limit.” Computers & Industrial Engineering159:107209. https://doi.org/10.1016/j.cie.2021.107209.
   Web of Science ®Google Scholar
• Moser, Maximilian, Nysret Musliu, Andrea Schaerf, and Felix Winter. 2022. “Exact and Metaheuristic Approaches for Unrelated Parallel Machine Scheduling.” Journal of Scheduling 25 (5): 507–534. https://doi.org/10.1007/s10951-021-00714-6.
   Web of Science ®Google Scholar
• Mouzon, Gilles, Mehmet B. Yildirim, and Janet Twomey. 2007. “Operational Methods for Minimization of Energy Consumption of Manufacturing Equipment.” International Journal of Production Research45 (18-19): 4247–4271. https://doi.org/10.1080/00207540701450013.
   Web of Science ®Google Scholar
• Nikolaev, Alexander G., and Sheldon H. Jacobson. 2010. “Simulated Annealing.” In Handbook of Metaheuristics, 1–39. 2nd ed. Boston, MA: Springer. https://doi.org/10.1007/978-1-4419-1665-5_1.
   Google Scholar
• Pan, Zixiao, Deming Lei, and Ling Wang. 2020. “A Knowledge-Based Two-Population Optimization Algorithm for Distributed Energy-Efficient Parallel Machines Scheduling.” IEEE Transactions on Cybernetics 52 (6): 5051–5063. https://doi.org/10.1109/TCYB.2020.3026571.
   Web of Science ®Google Scholar
• Paolucci, Massimo, Davide Anghinolfi, and Flavio Tonelli. 2017. “Facing Energy-Aware Scheduling: A Multi-Objective Extension of a Scheduling Support System for Improving Energy Efficiency in a Moulding Industry.” Soft Computing 21 (13): 3687–3698. https://doi.org/10.1007/s00500-015-1987-8.
   Google Scholar
• Pinedo, Michael L. 2012. Scheduling—Theory, Algorithms, and Systems. 6th ed. Vol. 29. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-031-05921-6.
   Google Scholar
• Pisinger, David, and Stefan Ropke. 2019. “Large Neighborhood Search.” In Handbook of Metaheuristics, 99–127. 3rd ed. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-91086-4_4.
   Google Scholar
• Qin, Hao-Xiang, Yu-Yan Han, Biao Zhang, Lei-Lei Meng, Yi-Ping Liu, Quan-Ke Pan, and Dun-Wei Gong. 2022. “An Improved Iterated Greedy Algorithm for the Energy-Efficient Blocking Hybrid Flow Shop Scheduling Problem.” Swarm and Evolutionary Computation 69:100992. https://doi.org/10.1016/j.swevo.2021.100992.
   Web of Science ®Google Scholar
• Rakovitis, Nikolaos, Dan Li, Nan Zhang, Jie Li, Liping Zhang, and Xin Xiao. 2022. “Novel Approach to Energy-Efficient Flexible Job-Shop Scheduling Problems.” Energy 238:121773. https://doi.org/10.1016/j.energy.2021.121773.
   Web of Science ®Google Scholar
• Rolim, Gustavo Alencar, Marcelo Seido Nagano, and Bruno de Athayde Prata. 2023. “Formulations and An Adaptive Large Neighborhood Search for Just-in-Time Scheduling of Unrelated Parallel Machines with a Common Due Window.” Computers & Operations Research 153:106159. https://doi.org/10.1016/j.cor.2023.106159.
   Google Scholar
• Safarzadeh, Hamid, and Seyed Taghi Akhavan Niaki. 2019. “Bi-Objective Green Scheduling in Uniform Parallel Machine Environments.” Journal of Cleaner Production 217:559–572. https://doi.org/10.1016/j.jclepro.2019.01.166.
   Web of Science ®Google Scholar
• Shahsavaripour, Maryam, Sajjad Abbasi, Moghaddameh Mirzaee, and Hoda Amiri. 2023. “Human Occupational Exposure to Microplastics: A Cross-Sectional Study in a Plastic Products Manufacturing Plant.” Science of the Total Environment 882:163576. https://doi.org/10.1016/j.scitotenv.2023.163576.
   Google Scholar
• Shen, Liji, Stéphane Dauzère-Pérès, and Söhnke Maecker. 2023. “Energy Cost Efficient Scheduling in Flexible Job-Shop Manufacturing Systems.” European Journal of Operational Research 310 (3): 992–1016. https://doi.org/10.1016/j.ejor.2023.03.041.
   Google Scholar
• Shrouf, Fadi, Joaquin Ordieres-Meré, Alvaro García-Sánchez, and Miguel Ortega-Mier. 2014. “Optimizing the Production Scheduling of a Single Machine to Minimize Total Energy Consumption Costs.” Journal of Cleaner Production 67:197–207. https://doi.org/10.1016/j.jclepro.2013.12.024.
   Web of Science ®Google Scholar
• Wang, Yusheng, Ada Che, and Jianguang Feng. 2023. “Energy-Efficient Unrelated Parallel Machine Scheduling with General Position-Based Deterioration.” International Journal of Production Research61 (17): 5886–5900. https://doi.org/10.1080/00207543.2022.2118887.
   Web of Science ®Google Scholar
• Wang, Yi-Chi, Ming-Jun Wang, and Sung-Chi Lin. 2017. “Selection of Cutting Conditions for Power Constrained Parallel Machine Scheduling.” Robotics and Computer-Integrated Manufacturing43:105–110. https://doi.org/10.1016/j.rcim.2015.10.010.
   Web of Science ®Google Scholar
• Wu, Xueqi, and Ada Che. 2019. “A Memetic Differential Evolution Algorithm for Energy-Efficient Parallel Machine Scheduling.” Omega 82:155–165. https://doi.org/10.1016/j.omega.2018.01.001.
   Web of Science ®Google Scholar
• Wu, Ting-Yun, Yun-Zhi Jiang, Yi-Zhu Su, and Wei-Chang Yeh. 2020. “Using Simplified Swarm Optimization on Multiloop Fuzzy PID Controller Tuning Design for Flow and Temperature Control System.” Applied Sciences 10 (23): 8472. https://doi.org/10.3390/app10238472.
   Google Scholar
• Wu, Xiuli, Xianli Shen, and Congbo Li. 2019. “The Flexible Job-Shop Scheduling Problem Considering Deterioration Effect and Energy Consumption Simultaneously.” Computers & Industrial Engineering135:1004–1024. https://doi.org/10.1016/j.cie.2019.06.048.
   Web of Science ®Google Scholar
• Wu, Xiuli, and Yangjun Sun. 2018. “A Green Scheduling Algorithm for Flexible Job Shop with Energy-Saving Measures.” Journal of Cleaner Production 172:3249–3264. https://doi.org/10.1016/j.jclepro.2017.10.342.
   Web of Science ®Google Scholar
• Yeh, Wei-Chang, Mei-Chi Chuang, and Wen-Chiung Lee. 2015. “Uniform Parallel Machine Scheduling with Resource Consumption Constraint.” Applied Mathematical Modelling 39 (8): 2131–2138. https://doi.org/10.1016/j.apm.2014.10.012.
   Web of Science ®Google Scholar
• Yeh, Wei-Chang, Wenbo Zhu, Shi-Yi Tan, Gai-Ge Wang, and Yuan-Hui Yeh. 2022. “Novel General Active Reliability Redundancy Allocation Problems and Algorithm.” Reliability Engineering & System Safety218:108167. https://doi.org/10.1016/j.ress.2021.108167.
   Google Scholar
• Zeng, YiZeng, Ada Che, and Xueqi Wu. 2018. “Bi-Objective Scheduling on Uniform Parallel Machines Considering Electricity Cost.” Engineering Optimization 50 (1): 19–36. https://doi.org/10.1080/0305215X.2017.1296437.
   Web of Science ®Google Scholar
• Zhang, Shibohua, Ada Che, Xueqi Wu, and Chengbin Chu. 2018. “Improved Mixed-Integer Linear Programming Model and Heuristics for Bi-Objective Single-Machine Batch Scheduling with Energy Cost Consideration.” Engineering Optimization 50 (8): 1380–1394. https://doi.org/10.1080/0305215X.2017.1400026.
   Web of Science ®Google Scholar
• Zhang, Hongliang, Gongjie Xu, Ruilin Pan, and Haijiang Ge. 2022. “A Novel Heuristic Method for the Energy-Efficient Flexible Job-Shop Scheduling Problem with Sequence-Dependent Set-Up and Transportation Time.” Engineering Optimization 54 (10): 1646–1667. https://doi.org/10.1080/0305215X.2021.1949007.
   Web of Science ®Google Scholar
• Zhou, Shengchao, Mingzhou Jin, and Ni Du. 2020. “Energy-Efficient Scheduling of a Single Batch Processing Machine with Dynamic Job Arrival Times.” Energy 209:118420. https://doi.org/10.1016/j.energy.2020.118420.
   Web of Science ®Google Scholar