Flexible Job Scheduling under Consideration of Time and Energy Consumption Using Enhanced Iterative Deferred Acceptance Algorithm

Abdulkadiroğlu, A., Pathak, P. A., & Roth, A. E. (2009). Strategy-Proofness versus Efficiency in Matching with Indifferences: Redesigning the NYC High School Match. American Economic Review, 99(5), 1954-1978. https://doi.org/https://www.doi.org/10.1257/aer.99.5.1954

Abdulkadiroğlu, A., & Sönmez, T. (2003). School Choice: A Mechanism Design Approach. American Economic Review, 93(3), 729-747. https://doi.org/http://doi.org/10.1257/000282803322157061

Ahmadian, M. M., Khatami, M., Salehipour, A., & Cheng, T. C. E. (2021). Four decades of research on the open-shop scheduling problem to minimize the makespan. European Journal of Operational Research. https://doi.org/https://doi.org/10.1016/j.ejor.2021.03.026

Armendariz-Lopez, J. F., Arena-Granados, A. P., Gonzalez-Trevizo, M. E., Luna-Leon, A., & Bojorquez-Morales, G. (2018). Energy payback time and Greenhouse Gas emissions: Studying the international energy agency guidelines architecture. Journal of Cleaner Production, 196, 1566-1575. https://doi.org/https://doi.org/10.1016/j.jclepro.2018.06.134

Bando, K. (2014a). A modified deferred acceptance algorithm for many-to-one matching markets with externalities among firms. Journal of Mathematical Economics, 52, 173-181. https://doi.org/https://doi.org/10.1016/j.jmateco.2014.01.001

Bando, K. (2014b). On the existence of a strictly strong Nash equilibrium under the student-optimal deferred acceptance algorithm. Games and Economic Behavior, 87, 269-287. https://doi.org/https://doi.org/10.1016/j.geb.2014.05.009

Baykasoğlu, A., Madenoğlu, F. S., & Hamzadayı, A. (2020). Greedy randomized adaptive search for dynamic flexible job-shop scheduling. Journal of Manufacturing Systems, 56, 425-451. https://doi.org/https://doi.org/10.1016/j.jmsy.2020.06.005

Berterottière, L., Dauzère-Pérès, S., & Yugma, C. (2024). Flexible job-shop scheduling with transportation resources. European Journal of Operational Research, 312(3), 890-909. https://doi.org/https://doi.org/10.1016/j.ejor.2023.07.036

Bie, Z., Xie, H., Hu, G., & Li, G. (2016). Optimal scheduling of power systems considering demand response. Journal of Modern Power Systems and Clean Energy, 4(2), 180-187. https://doi.org/https://doi.org/10.1007/s40565-015-0136-9

Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research, 41(3), 157-183. https://doi.org/10.1007/BF02023073

Bruni, M. E., Khodaparasti, S., & Demeulemeester, E. (2020). The distributionally robust machine scheduling problem with job selection and sequence-dependent setup times. Computers & Operations Research, 123, 105017. https://doi.org/https://doi.org/10.1016/j.cor.2020.105017

Bürgy, R. (2017). A neighborhood for complex job shop scheduling problems with regular objectives. Journal of Scheduling, 20(4), 391-422. https://doi.org/https://doi.org/10.1007/s10951-017-0532-2

Cai, W., Liu, F., Zhou, X., & Xie, J. (2016). Fine energy consumption allowance of workpieces in the mechanical manufacturing industry. Energy, 114, 623-633. https://doi.org/https://doi.org/10.1016/j.energy.2016.08.028

Chaudhry, I. A., & Khan, A. A. (2016). A research survey: review of flexible job shop scheduling techniques. International Transactions in Operational Research, 23(3), 551-591. https://doi.org/https://doi.org/10.1111/itor.12199

Chou, Y.-L., Yang, J.-M., & Wu, C.-H. (2020). An energy-aware scheduling algorithm under maximum power consumption constraints. Journal of Manufacturing Systems, 57, 182-197. https://doi.org/https://doi.org/10.1016/j.jmsy.2020.09.004

Dai, M., Tang, D., Giret, A., & Salido, M. A. (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/https://doi.org/10.1016/j.rcim.2019.04.006

Dai, M., Tang, D., Giret, A., Salido, M. A., & Li, W. D. (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. https://doi.org/https://doi.org/10.1016/j.rcim.2013.04.001

de Arruda, A. C., Weigang, L., & Milea, V. (2015). A new Airport Collaborative Decision Making algorithm based on Deferred Acceptance in a two-sided market. Expert Systems with Applications, 42(7), 3539-3550. https://doi.org/https://doi.org/10.1016/j.eswa.2014.11.060

Delaram, J., Fatahi Valilai, O., Houshamand, M., & Ashtiani, F. (2021). A matching mechanism for public cloud manufacturing platforms using intuitionistic Fuzzy VIKOR and deferred acceptance algorithm. International Journal of Management Science and Engineering Management, 1-16. https://doi.org/https://doi.org/10.1080/17509653.2021.1892549

Dworczak, P. (2021). Deferred Acceptance with Compensation Chains. Operations Research, 69(2), 456-468. https://doi.org/https://doi.org/10.1287/opre.2020.2042

Fan, J., Shen, W., Gao, L., Zhang, C., & Zhang, Z. (2021). A hybrid Jaya algorithm for solving flexible job shop scheduling problem considering multiple critical paths. Journal of Manufacturing Systems, 60, 298-311. https://doi.org/https://doi.org/10.1016/j.jmsy.2021.05.018

Gale, D., & Shapley, L. S. (1962). College Admissions and the Stability of Marriage. The American Mathematical Monthly, 69(1), 9-15. https://doi.org/10.1080/00029890.1962.11989827

Gong, G., Deng, Q., Gong, X., Liu, W., & Ren, Q. (2018). A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators. Journal of Cleaner Production, 174, 560-576. https://doi.org/https://doi.org/10.1016/j.jclepro.2017.10.188

Gong, X., De Pessemier, T., Martens, L., & Joseph, W. (2019). Energy- and labor-aware flexible job shop scheduling under dynamic electricity pricing: A many-objective optimization investigation. Journal of Cleaner Production, 209, 1078-1094. https://doi.org/https://doi.org/10.1016/j.jclepro.2018.10.289

Guo, W., Lei, Q., Song, Y., & Lyu, X. (2021). A learning interactive genetic algorithm based on edge selection encoding for assembly job shop scheduling problem. Computers & Industrial Engineering, 159, 107455. https://doi.org/https://doi.org/10.1016/j.cie.2021.107455

Harjunkoski, I., Maravelias, C. T., Bongers, P., Castro, P. M., Engell, S., Grossmann, I. E., Hooker, J., Méndez, C., Sand, G., & Wassick, J. (2014). Scope for industrial applications of production scheduling models and solution methods. Computers & Chemical Engineering, 62, 161-193. https://doi.org/https://doi.org/10.1016/j.compchemeng.2013.12.001

Honda, E. (2020). A modified deferred acceptance algorithm for conditionally lexicographic-substitutable preferences. Journal of Mathematical Economics, 102458. https://doi.org/https://doi.org/10.1016/j.jmateco.2020.102458

Iqbal, M. W., Kang, Y., & Jeon, H. W. (2020). Zero waste strategy for green supply chain management with minimization of energy consumption. Journal of Cleaner Production, 245, 118827. https://doi.org/https://doi.org/10.1016/j.jclepro.2019.118827

Li, X., & Gao, L. (2016). An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. International Journal of Production Economics, 174, 93-110. https://doi.org/https://doi.org/10.1016/j.ijpe.2016.01.016

Li, Z., Tang, Q., & Zhang, L. (2016). Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm. Journal of Cleaner Production, 135, 508-522. https://doi.org/https://doi.org/10.1016/j.jclepro.2016.06.131

Liu, M., Lv, J., Du, S., Deng, Y., Shen, X., & Zhou, Y. (2024). Multi-resource constrained flexible job shop scheduling problem with fixture-pallet combinatorial optimisation. Computers & Industrial Engineering, 188, 109903. https://doi.org/https://doi.org/10.1016/j.cie.2024.109903

Liu, Y., Zhang, L., Tao, F., & Wang, L. (2017). Resource service sharing in cloud manufacturing based on the Gale–Shapley algorithm: advantages and challenge. International Journal of Computer Integrated Manufacturing, 30(4-5), 420-432. https://doi.org/10.1080/0951192X.2015.1067916

Liu, Z., Wang, J., Zhang, C., Chu, H., Ding, G., & Zhang, L. (2021). A hybrid genetic-particle swarm algorithm based on multilevel neighbourhood structure for flexible job shop scheduling problem. Computers & Operations Research, 105431. https://doi.org/https://doi.org/10.1016/j.cor.2021.105431

Lunardi, W. T., Birgin, E. G., Ronconi, D. P., & Voos, H. (2021). Metaheuristics for the online printing shop scheduling problem. European Journal of Operational Research, 293(2), 419-441. https://doi.org/https://doi.org/10.1016/j.ejor.2020.12.021

Mansouri, S. A., Aktas, E., & Besikci, U. (2016). Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption. European Journal of Operational Research, 248(3), 772-788. https://doi.org/https://doi.org/10.1016/j.ejor.2015.08.064

Maschler, M., Solan, E., & Zamir, S. (2013). Game Theory. Cambridge University Press. https://doi.org/https://doi.org/10.1017/CBO9780511794216

Masmoudi, O., Delorme, X., & Gianessi, P. (2019). Job-shop scheduling problem with energy consideration. International Journal of Production Economics, 216, 12-22. https://doi.org/https://doi.org/10.1016/j.ijpe.2019.03.021

Meng, L., Zhang, C., Shao, X., & Ren, Y. (2019). MILP models for energy-aware flexible job shop scheduling problem. Journal of Cleaner Production, 210, 710-723. https://doi.org/https://doi.org/10.1016/j.jclepro.2018.11.021

Meng, L., Zhang, C., Zhang, B., Gao, K., Ren, Y., & Sang, H. (2023). MILP modeling and optimization of multi-objective flexible job shop scheduling problem with controllable processing times. Swarm and Evolutionary Computation, 82, 101374. https://doi.org/https://doi.org/10.1016/j.swevo.2023.101374

Pezzella, F., Morganti, G., & Ciaschetti, G. (2008). A genetic algorithm for the Flexible Job-shop Scheduling Problem. Computers & Operations Research, 35(10), 3202-3212. https://doi.org/https://doi.org/10.1016/j.cor.2007.02.014

Pinedo, M. L. (2012). Scheduling (Vol. 29). Springer.

Raileanu, S., Anton, F., Iatan, A., Borangiu, T., Anton, S., & Morariu, O. (2017). Resource scheduling based on energy consumption for sustainable manufacturing. Journal of Intelligent Manufacturing, 28(7), 1519-1530. https://doi.org/10.1007/s10845-015-1142-5

Roth, A. E. (2003). The Origins, History, and Design of the Resident Match. JAMA, 289(7), 909-912. https://doi.org/https://www.doi.org/10.1001/jama.289.7.909

Roth, A. E. (2008). Deferred acceptance algorithms: history, theory, practice, and open questions. International Journal of Game Theory, 36(3), 537-569. https://doi.org/http://doi.org/10.1007/s00182-008-0117-6

Şahman, M. A. (2021). A discrete spotted hyena optimizer for solving distributed job shop scheduling problems. Applied Soft Computing, 106, 107349. https://doi.org/https://doi.org/10.1016/j.asoc.2021.107349

Sinha, R. K., & Chaturvedi, N. D. (2018). A graphical dual objective approach for minimizing energy consumption and carbon emission in production planning. Journal of Cleaner Production, 171, 312-321. https://doi.org/https://doi.org/10.1016/j.jclepro.2017.09.272

Sun, J., Zhang, G., Lu, J., & Zhang, W. (2021). A hybrid many-objective evolutionary algorithm for flexible job-shop scheduling problem with transportation and setup times. Computers & Operations Research, 105263. https://doi.org/https://doi.org/10.1016/j.cor.2021.105263

Tao, N., & Xu-ping, W. (2018). Study on disruption management strategy of job-shop scheduling problem based on prospect theory. Journal of Cleaner Production, 194, 174-178. https://doi.org/https://doi.org/10.1016/j.jclepro.2018.05.139

Türkyılmaz, A., Şenvar, Ö., Ünal, İ., & Bulkan, S. (2020). A research survey: heuristic approaches for solving multi objective flexible job shop problems. Journal of Intelligent Manufacturing, 31(8), 1949-1983. https://doi.org/https://doi.org/10.1007/s10845-020-01547-4

Vital-Soto, A., Azab, A., & Baki, M. F. (2020). Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility. Journal of Manufacturing Systems, 54, 74-93. https://doi.org/https://doi.org/10.1016/j.jmsy.2019.11.010

Wang, J., Liu, Y., Ren, S., Wang, C., & Wang, W. (2021). Evolutionary game based real-time scheduling for energy-efficient distributed and flexible job shop. Journal of Cleaner Production, 293, 126093. https://doi.org/https://doi.org/10.1016/j.jclepro.2021.126093

Wang, L., Wang, S., Xu, Y., Zhou, G., & Liu, M. (2012). A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem. Computers & Industrial Engineering, 62(4), 917-926. https://doi.org/https://doi.org/10.1016/j.cie.2011.12.014

Wang, L., Zhou, G., Xu, Y., & Liu, M. (2013). A hybrid artificial bee colony algorithm for the fuzzy flexible job-shop scheduling problem. International Journal of Production Research, 51(12), 3593-3608. https://doi.org/https://doi.org/10.1080/00207543.2012.754549

Wang, S., Lu, X., Li, X. X., & Li, W. D. (2015). A systematic approach of process planning and scheduling optimization for sustainable machining. Journal of Cleaner Production, 87, 914-929. https://doi.org/https://doi.org/10.1016/j.jclepro.2014.10.008

Wu, X., Shen, X., & Li, C. (2019). The flexible job-shop scheduling problem considering deterioration effect and energy consumption simultaneously. Computers & Industrial Engineering, 135, 1004-1024. https://doi.org/https://doi.org/10.1016/j.cie.2019.06.048

Wu, X., & Sun, Y. (2018). A green scheduling algorithm for flexible job shop with energy-saving measures. Journal of Cleaner Production, 172, 3249-3264. https://doi.org/https://doi.org/10.1016/j.jclepro.2017.10.342

Wu, Z., Sun, S., & Yu, S. (2020). Optimizing makespan and stability risks in job shop scheduling. Computers & Operations Research, 122, 104963. https://doi.org/https://doi.org/10.1016/j.cor.2020.104963

Xie, J., Li, X., Gao, L., & Gui, L. (2023). A hybrid genetic tabu search algorithm for distributed flexible job shop scheduling problems. Journal of Manufacturing Systems, 71, 82-94. https://doi.org/https://doi.org/10.1016/j.jmsy.2023.09.002

Xie, N., & Chen, N. (2018). Flexible job shop scheduling problem with interval grey processing time. Applied Soft Computing, 70, 513-524. https://doi.org/https://doi.org/10.1016/j.asoc.2018.06.004

Xin, X., Jiang, Q., Li, S., Gong, S., & Chen, K. (2021). Energy-efficient scheduling for a permutation flow shop with variable transportation time using an improved discrete whale swarm optimization. Journal of Cleaner Production, 293, 126121. https://doi.org/https://doi.org/10.1016/j.jclepro.2021.126121

Zhang, Y., Wang, J., Liu, S., & Qian, C. (2017). Game Theory Based Real-Time Shop Floor Scheduling Strategy and Method for Cloud Manufacturing. International Journal of Intelligent Systems, 32(4), 437-463. https://doi.org/https://doi.org/10.1002/int.21868

Zhou, G., Jiang, P., & Huang, G. Q. (2009). A game-theory approach for job scheduling in networked manufacturing. The International Journal of Advanced Manufacturing Technology, 41(9), 972-985. https://doi.org/https://doi.org/10.1007/s00170-008-1539-9