A memetic algorithm for energy-efficient scheduling of integrated production and shipping

References

• Cattaruzza, D., N. Absi, D. Feillet, and T. Vidal. 2014. “A Memetic Algorithm for the Multi Trip Vehicle Routing Problem.” European Journal of Operational Research 236 (3): 833–848. doi:10.1016/j.ejor.2013.06.012.
   Web of Science ®Google Scholar
• Che, A., X. Wu, J. Peng, and P. Yan. 2017. “Energy-efficient Bi-objective Single-machine Scheduling with Power-down Mechanism.” Computers & Operations Research 85: 172–183. doi:10.1016/j.cor.2017.04.004.
   Web of Science ®Google Scholar
• Chen, J., G. Q. Huang, H. Luo, and J. Wang. 2015. “Synchronisation of Production Scheduling and Shipment in an Assembly Flowshop.” International Journal of Production Research 53 (9): 2787–2802. doi:10.1080/00207543.2014.994075.
   Web of Science ®Google Scholar
• Chen, J., G. Q. Huang, and J.-Q. Wang. 2019. “Synchronized Scheduling of Production and Outbound Shipping Using Bilevel-based Simulated Annealing Algorithm.” Computers & Industrial Engineering 137: 106050. doi:10.1016/j.cie.2019.106050.
   Web of Science ®Google Scholar
• Chen, Z.-L., and G. L. Vairaktarakis. 2005. “Integrated Scheduling of Production and Distribution Operations.” Management Science 51 (4): 614–628. doi:10.1287/mnsc.1040.0325.
   Web of Science ®Google Scholar
• Chen, Z.-L. 2010. “Integrated Production and Outbound Distribution Scheduling: Review and Extensions.” Operations Research 58 (1): 130–148. doi:10.1287/opre.1080.0688.
   Web of Science ®Google Scholar
• Dai, M., D. Tang, A. Giret, M. A. Salido, and W. D. Li. 2013. “Energy-efficient Scheduling for a Flexible Flow Shop Using an Improved Genetic-simulated Annealing Algorithm.” Robotics and Computer-Integrated Manufacturing 29 (5): 418–429. doi:10.1016/j.rcim.2013.04.001.
   Web of Science ®Google Scholar
• Deng, J., and L. Wang. 2017. “A Competitive Memetic Algorithm for Multi-objective Distributed Permutation Flow Shop Scheduling Problem.” Swarm and Evolutionary Computation 32: 121–131. doi:10.1016/j.swevo.2016.06.002.
   Web of Science ®Google Scholar
• Ding, J.-Y., S. Song, and C. Wu. 2016. “Carbon-efficient Scheduling of Flow Shops by Multi-objective Optimization.” European Journal of Operational Research 248 (3): 758–771. doi:10.1016/j.ejor.2015.05.019.
   Web of Science ®Google Scholar
• Fang, K.-T., and B. M. Lin. 2013. “Parallel-machine Scheduling to Minimize Tardiness Penalty and Power Cost.” Computers & Industrial Engineering 64 (1): 224–234. doi:10.1016/j.cie.2012.10.002.
   Web of Science ®Google Scholar
• Fang, K., N. A. Uhan, F. Zhao, and J. W. Sutherland. 2016. “Scheduling on a Single Machine under Time-of-use Electricity Tariffs.” Annals of Operations Research 238 (1–2): 199–227. doi:10.1007/s10479-015-2003-5.
   Web of Science ®Google Scholar
• Gahm, C., F. Denz, M. Dirr, and A. Tuma. 2016. “Energy-efficient Scheduling in Manufacturing Companies: A Review and Research Framework.” European Journal of Operational Research 248 (3): 744–757. doi:10.1016/j.ejor.2015.07.017.
   Web of Science ®Google Scholar
• Gao, K. Z., Z. M. He, Y. Huang, P. Y. Duan, and P. N. Suganthan. 2020. “A Survey on Meta-heuristics for Solving Disassembly Line Balancing, Planning and Scheduling Problems in Remanufacturing.” Swarm and Evolutionary Computation 57: 100719. doi:10.1016/j.swevo.2020.100719.
   Web of Science ®Google Scholar
• Gao, K., F. Yang, M. C. Zhou, Q. Pan, and P. N. Suganthan. 2019b. “Flexible Job-Shop Rescheduling for New Job Insertion by Using Discrete Jaya Algorithm.” IEEE Transactions on Cybernetics 49 (5): 1944–1955. doi:10.1109/TCYB.2018.2817240.
   PubMed Web of Science ®Google Scholar
• Gao, K., Z. Cao, L. Zhang, Z. Chen, Y. Han, and Q. Pan. 2019a. “A Review on Swarm Intelligence and Evolutionary Algorithms for Solving Flexible Job Shop Scheduling Problems.” IEEE/CAA Journal of Automatica Sinica 6 (4): 904–916. doi:10.1109/JAS.2019.1911540.
   Google Scholar
• Gawiejnowicz, S., and C. Suwalski. 2014. “Scheduling Linearly Deteriorating Jobs by Two Agents to Minimize the Weighted Sum of Two Criteria.” Computers & Operations Research 52: 135–146. doi:10.1016/j.cor.2014.06.020.
   Web of Science ®Google Scholar
• Goh, S. L., G. Kendall, and N. R. Sabar. 2017. “Improved Local Search Approaches to Solve the Post Enrolment Course Timetabling Problem.” European Journal of Operational Research 261 (1): 17–29. doi:10.1016/j.ejor.2017.01.040.
   Web of Science ®Google Scholar
• Gong, G., R. Chiong, Q. Deng, and Q. Luo. 2020b. “A Memetic Algorithm for Multi-objective Distributed Production Scheduling: Minimizing the Makespan and Total Energy Consumption.” Journal of Intelligent Manufacturing 31 (6): 1443–1466. doi:10.1007/s10845-019-01521-9.
   Web of Science ®Google Scholar
• Gong, G., R. Chiong, Q. Deng, W. Han, L. Zhang, W. Lin, and K. Li. 2020a. “Energy-efficient Flexible Flow Shop Scheduling with Worker Flexibility.” Expert Systems with Applications 141: 112902. doi:10.1016/j.eswa.2019.112902.
   Web of Science ®Google Scholar
• Guo, Z., L. Shi, L. Chen, and Y. Liang. 2017. “A Harmony Search-based Memetic Optimization Model for Integrated Production and Transportation Scheduling in MTO Manufacturing.” Omega 66: 327–343. doi:10.1016/j.omega.2015.10.012.
   Web of Science ®Google Scholar
• IBM. 2019. ”User's manual for CPLEX of ILOG CPLEX Optimization Studio 12.9.0.”. https://www.ibm.com/docs/en/icos/12.9.0?topic=smippqt-miqcp-mixed-integer-programs-quadratic-terms-in-constraints
   Google Scholar
• Lei, D., L. Gao, and Y. Zheng. 2017. “A Novel Teaching-learning-based Optimization Algorithm for Energy-efficient Scheduling in Hybrid Flow Shop.” IEEE Transactions on Engineering Management 65 (2): 330–340. doi:10.1109/TEM.2017.2774281.
   Web of Science ®Google Scholar
• Leung, J. Y.-T., and Z.-L. Chen. 2013. “Integrated Production and Distribution with Fixed Delivery Departure Dates.” Operations Research Letters 41 (3): 290–293. doi:10.1016/j.orl.2013.02.006.
   Web of Science ®Google Scholar
• Li, J., H. Sang, Y. Han, C. Wang, and K. Gao. 2018. “Efficient Multi-objective Optimization Algorithm for Hybrid Flow Shop Scheduling Problems with Setup Energy Consumptions.” Journal of Cleaner Production 181: 584–598. doi:10.1016/j.jclepro.2018.02.004.
   Web of Science ®Google Scholar
• Liu, C.-H. 2014. “Approximate Trade-off between Minimisation of Total Weighted Tardiness and Minimisation of Carbon Dioxide (CO2) Emissions in Bi-criteria Batch Scheduling Problem.” International Journal of Computer Integrated Manufacturing 27 (8): 759–771. doi:10.1080/0951192X.2013.834479.
   Web of Science ®Google Scholar
• Liu, Y., Y. Zhang, S. Ren, M. Yang, Y. Wang, and D. Huisingh. 2020. “How Can Smart Technologies Contribute to Sustainable Product Lifecycle Management?” Journal of Cleaner Production 249: 119423. doi:10.1016/j.jclepro.2019.119423.
   Web of Science ®Google Scholar
• Lu, C., L. Gao, X. Li, Q. Pan, and Q. Wang. 2017. “Energy-efficient Permutation Flow Shop Scheduling Problem Using a Hybrid Multi-objective Backtracking Search Algorithm.” Journal of Cleaner Production 144: 228–238. doi:10.1016/j.jclepro.2017.01.011.
   Web of Science ®Google Scholar
• Lü, Z., and J.-K. Hao. 2010. “A Memetic Algorithm for Graph Coloring.” European Journal of Operational Research 203 (1): 241–250. doi:10.1016/j.ejor.2009.07.016.
   Web of Science ®Google Scholar
• Luo, H., B. Du, G. Q. Huang, H. Chen, and X. Li. 2013. “Hybrid Flow Shop Scheduling considering Machine Electricity Consumption Cost.” International Journal of Production Economics 146 (2): 423–439. doi:10.1016/j.ijpe.2013.01.028.
   Web of Science ®Google Scholar
• Mansouri, S. A., E. Aktas, and U. Besikci. 2016. “Green Scheduling of a Two-machine Flowshop: Trade-off between Makespan and Energy Consumption.” European Journal of Operational Research 248 (3): 772–788. doi:10.1016/j.ejor.2015.08.064.
   Web of Science ®Google Scholar
• May, G., B. Stahl, M. Taisch, and V. Prabhu. 2015. “Multi-objective Genetic Algorithm for Energy-efficient Job Shop Scheduling.” International Journal of Production Research 53 (23): 7071–7089. doi:10.1080/00207543.2015.1005248.
   Web of Science ®Google Scholar
• Meng, L., C. Zhang, X. Shao, Y. Ren, and C. Ren. 2019. “Mathematical Modelling and Optimisation of Energy-conscious Hybrid Flow Shop Scheduling Problem with Unrelated Parallel Machines.” International Journal of Production Research 57 (4): 1119–1145. doi:10.1080/00207543.2018.1501166.
   Web of Science ®Google Scholar
• Mokhtari, H., and A. Hasani. 2017. “An Energy-efficient Multi-objective Optimization for Flexible Job-shop Scheduling Problem.” Computers & Chemical Engineering 104: 339–352. doi:10.1016/j.compchemeng.2017.05.004.
   Web of Science ®Google Scholar
• Moscato, P., and C. Cotta. 2003. “A Gentle Introduction to Memetic Algorithms.“ In Handbook of Metaheuristics, edited by F. Glover, and G. A. Kochenberger. International Series in Operations Research & Management Science, 105–144.
   Google Scholar
• Mouzon, G., and M. B. Yildirim. 2008. “A Framework to Minimise Total Energy Consumption and Total Tardiness on A Single Machine.” International Journal of Sustainable Engineering 1 (2): 105–116. doi:10.1080/19397030802257236.
   Google Scholar
• Mouzon, G., M. B. Yildirim, and J. Twomey. 2007. “Operational Methods for Minimization of Energy Consumption of Manufacturing Equipment.” International Journal of Production Research 45 (18–19): 4247–4271. doi:10.1080/00207540701450013.
   Web of Science ®Google Scholar
• Nalepa, J., and M. Blocho. 2016. “Adaptive Memetic Algorithm for Minimizing Distance in the Vehicle Routing Problem with Time Windows.” Soft Computing 20 (6): 2309–2327. doi:10.1007/s00500-015-1642-4.
   Web of Science ®Google Scholar
• Nouiri, M., A. Bekrar, and D. Trentesaux. 2020. “An Energy-efficient Scheduling and Rescheduling Method for Production and Logistics Systems †.” International Journal of Production Research 58 (11): 3263–3283. doi:10.1080/00207543.2019.1660826.
   Web of Science ®Google Scholar
• Öztop, H., M. F. Tasgetiren, D. T. Eliiyi, Q.-K. Pan, and L. Kandiller. 2020. “An Energy-efficient Permutation Flowshop Scheduling Problem.” Expert Systems with Applications 150: 113279. doi:10.1016/j.eswa.2020.113279.
   Web of Science ®Google Scholar
• Park, C.-W., K.-S. Kwon, W.-B. Kim, B.-K. Min, S.-J. Park, I.-H. Sung, Y. S. Yoon, K.-S. Lee, J.-H. Lee, and J. Seok. 2009. “Energy Consumption Reduction Technology in Manufacturing — A Selective Review of Policies, Standards, and Research.” International Journal of Precision Engineering and Manufacturing 10 (5): 151–173. doi:10.1007/s12541-009-0107-z.
   Web of Science ®Google Scholar
• Pinedo, M. 2012. Scheduling. New York: Springer.
   Google Scholar
• Rager, M., C. Gahm, and F. Denz. 2015. “Energy-oriented Scheduling Based on Evolutionary Algorithms.” Computers & Operations Research 54: 218–231. doi:10.1016/j.cor.2014.05.002.
   Web of Science ®Google Scholar
• Rubaiee, S., and M. B. Yildirim. 2019. “An Energy-aware Multi-objective Ant Colony Algorithm to Minimize Total Completion Time and Energy Cost on a Single-machine Preemptive Scheduling.” Computers & Industrial Engineering 127: 240–252. doi:10.1016/j.cie.2018.12.020.
   Web of Science ®Google Scholar
• Ruiz, R., and C. Maroto. 2005. “A Comprehensive Review and Evaluation of Permutation Flowshop Heuristics.” European Journal of Operational Research, Project Management and Scheduling 165 (2): 479–494. doi:10.1016/j.ejor.2004.04.017.
   Web of Science ®Google Scholar
• Shioura, A., N. V. Shakhlevich, V. A. Strusevich, and B. Primas. 2018. “Models and Algorithms for Energy-efficient Scheduling with Immediate Start of Jobs.” Journal of Scheduling 21 (5): 505–516. doi:10.1007/s10951-017-0552-y.
   Web of Science ®Google Scholar
• Shrouf, F., J. Ordieres-Meré, A. García-Sánchez, and M. Ortega-Mier. 2014. “Optimizing the Production Scheduling of a Single Machine to Minimize Total Energy Consumption Costs.” Journal of Cleaner Production 67: 197–207. doi:10.1016/j.jclepro.2013.12.024.
   Web of Science ®Google Scholar
• Subai, C., P. Baptiste, and E. Niel. 2006. “Scheduling Issues for Environmentally Responsible Manufacturing: The Case of Hoist Scheduling in an Electroplating Line.” International Journal of Production Economics 99 (1–2): 74–87. doi:10.1016/j.ijpe.2004.12.008.
   Web of Science ®Google Scholar
• Wang, S.-Y., and L. Wang. 2015. “An Estimation of Distribution Algorithm-based Memetic Algorithm for the Distributed Assembly Permutation Flow-shop Scheduling Problem.” IEEE Transactions on Systems, Man, and Cybernetics: Systems 46 (1): 139–149. doi:10.1109/TSMC.2015.2416127.
   Web of Science ®Google Scholar
• Wang, S., X. Wang, F. Chu, and J. Yu. 2020. “An Energy-efficient Two-stage Hybrid Flow Shop Scheduling Problem in a Glass Production.” International Journal of Production Research 58 (8): 2283–2314. doi:10.1080/00207543.2019.1624857.
   Web of Science ®Google Scholar
• Wang, W., H. Yang, Y. Zhang, and J. Xu. 2018. “IoT-enabled Real-time Energy Efficiency Optimisation Method for Energy-intensive Manufacturing Enterprises.” International Journal of Computer Integrated Manufacturing 31 (4–5): 362–379. doi:10.1080/0951192X.2017.1337929.
   Web of Science ®Google Scholar
• Wang, Y.-C., M.-J. Wang, and S.-C. Lin. 2017. “Selection of Cutting Conditions for Power Constrained Parallel Machine Scheduling.” Robotics and Computer-Integrated Manufacturing 43: 105–110. doi:10.1016/j.rcim.2015.10.010.
   Web of Science ®Google Scholar
• Wu, X., and A. Che. 2019. “A Memetic Differential Evolution Algorithm for Energy-efficient Parallel Machine Scheduling.” Omega 82: 155–165. doi:10.1016/j.omega.2018.01.001.
   Web of Science ®Google Scholar
• Yan, J., L. Li, F. Zhao, F. Zhang, and Q. Zhao. 2016. “A Multi-level Optimization Approach for Energy-efficient Flexible Flow Shop Scheduling.” Journal of Cleaner Production 137: 1543–1552. doi:10.1016/j.jclepro.2016.06.161.
   Web of Science ®Google Scholar
• Yao, F., A. Demers, and S. Shenker. 1995. “A scheduling model for reduced CPU energy.” Proceedings of IEEE 36th Annual Foundations of Computer Science, 374–382.
   Google Scholar
• Yu, X., and M. Gen. 2010. Introduction to Evolutionary Algorithms. London: Springer Science & Business Media.
   Google Scholar
• Yuan, M., H. Yu, J. Huang, and A. Ji. 2019. “Reconfigurable Assembly Line Balancing for Cloud Manufacturing.” Journal of Intelligent Manufacturing 30 (6): 2391–2405. doi:10.1007/s10845-018-1398-7.
   Web of Science ®Google Scholar
• Yüksel, D., M. F. Taşgetiren, L. Kandiller, and L. Gao. 2020. “An Energy-efficient Bi-objective No-wait Permutation Flowshop Scheduling Problem to Minimize Total Tardiness and Total Energy Consumption.” Computers & Industrial Engineering 145: 106431. doi:10.1016/j.cie.2020.106431.
   Web of Science ®Google Scholar
• Zeng, Z., M. Hong, J. Li, Y. Man, H. Liu, Z. Li, and H. Zhang. 2018. “Integrating Process Optimization with Energy-efficiency Scheduling to Save Energy for Paper Mills.” Applied Energy 225: 542–558. doi:10.1016/j.apenergy.2018.05.051.
   Web of Science ®Google Scholar
• Zhang, H., Y. Wu, R. Pan, and G. Xu. 2021. “Two-stage Parallel Speed-scaling Machine Scheduling under Time-of-use Tariffs.” Journal of Intelligent Manufacturing 32 (1): 91–112.
   Web of Science ®Google Scholar
• Zhang, Y., J. Wang, and Y. Liu. 2017. “Game Theory Based Real-time Multi-objective Flexible Job Shop Scheduling considering Environmental Impact.” Journal of Cleaner Production 167: 665–679. doi:10.1016/j.jclepro.2017.08.068.
   Web of Science ®Google Scholar
• Zheng, X., S. Zhou, R. Xu, and H. Chen. 2020. “Energy-efficient Scheduling for Multi-objective Two-stage Flow Shop Using a Hybrid Ant Colony Optimisation Algorithm.” International Journal of Production Research 58 (13): 4103–4120.
   Web of Science ®Google Scholar