Wu, X., & Sun, Y. (2018). A green scheduling algorithm for flexible job shop with energy-saving measures. Journal of Cleaner Production, 172, 3249-3264. doi:10.1016/j.jclepro.2017.10.342
Wang, Q., Tang, D., Li, S., Yang, J., Salido, M., Giret, A., & Zhu, H. (2019). An Optimization Approach for the Coordinated Low-Carbon Design of Product Family and Remanufactured Products. Sustainability, 11(2), 460. doi:10.3390/su11020460
Meng, Y., Yang, Y., Chung, H., Lee, P.-H., & Shao, C. (2018). Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review. Sustainability, 10(12), 4779. doi:10.3390/su10124779
[+]
Wu, X., & Sun, Y. (2018). A green scheduling algorithm for flexible job shop with energy-saving measures. Journal of Cleaner Production, 172, 3249-3264. doi:10.1016/j.jclepro.2017.10.342
Wang, Q., Tang, D., Li, S., Yang, J., Salido, M., Giret, A., & Zhu, H. (2019). An Optimization Approach for the Coordinated Low-Carbon Design of Product Family and Remanufactured Products. Sustainability, 11(2), 460. doi:10.3390/su11020460
Meng, Y., Yang, Y., Chung, H., Lee, P.-H., & Shao, C. (2018). Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review. Sustainability, 10(12), 4779. doi:10.3390/su10124779
Gahm, C., Denz, F., Dirr, M., & Tuma, A. (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
Giret, A., Trentesaux, D., & Prabhu, V. (2015). Sustainability in manufacturing operations scheduling: A state of the art review. Journal of Manufacturing Systems, 37, 126-140. doi:10.1016/j.jmsy.2015.08.002
Akbar, M., & Irohara, T. (2018). Scheduling for sustainable manufacturing: A review. Journal of Cleaner Production, 205, 866-883. doi:10.1016/j.jclepro.2018.09.100
Che, A., Wu, X., Peng, J., & Yan, P. (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
Lee, S., Do Chung, B., Jeon, H. W., & Chang, J. (2017). A dynamic control approach for energy-efficient production scheduling on a single machine under time-varying electricity pricing. Journal of Cleaner Production, 165, 552-563. doi:10.1016/j.jclepro.2017.07.102
Rubaiee, S., & Yildirim, M. B. (2019). An energy-aware multiobjective 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
Zhang, M., Yan, J., Zhang, Y., & Yan, S. (2019). Optimization for energy-efficient flexible flow shop scheduling under time of use electricity tariffs. Procedia CIRP, 80, 251-256. doi:10.1016/j.procir.2019.01.062
Li, J., Sang, H., Han, Y., Wang, C., & Gao, K. (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
Lu, C., Gao, L., Li, X., Pan, Q., & Wang, Q. (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
Fu, Y., Tian, G., Fathollahi-Fard, A. M., Ahmadi, A., & Zhang, C. (2019). Stochastic multi-objective modelling and optimization of an energy-conscious distributed permutation flow shop scheduling problem with the total tardiness constraint. Journal of Cleaner Production, 226, 515-525. doi:10.1016/j.jclepro.2019.04.046
Schulz, S., Neufeld, J. S., & Buscher, U. (2019). A multi-objective iterated local search algorithm for comprehensive energy-aware hybrid flow shop scheduling. Journal of Cleaner Production, 224, 421-434. doi:10.1016/j.jclepro.2019.03.155
Liu, Y., Dong, H., Lohse, N., Petrovic, S., & Gindy, N. (2014). An investigation into minimising total energy consumption and total weighted tardiness in job shops. Journal of Cleaner Production, 65, 87-96. doi:10.1016/j.jclepro.2013.07.060
Liu, Y., Dong, H., Lohse, N., & Petrovic, S. (2016). A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance. International Journal of Production Economics, 179, 259-272. doi:10.1016/j.ijpe.2016.06.019
May, G., Stahl, B., Taisch, M., & Prabhu, V. (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
Zhang, R., & Chiong, R. (2016). Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption. Journal of Cleaner Production, 112, 3361-3375. doi:10.1016/j.jclepro.2015.09.097
Salido, M. A., Escamilla, J., Giret, A., & Barber, F. (2015). A genetic algorithm for energy-efficiency in job-shop scheduling. The International Journal of Advanced Manufacturing Technology, 85(5-8), 1303-1314. doi:10.1007/s00170-015-7987-0
Masmoudi, O., Delorme, X., & Gianessi, P. (2019). Job-shop scheduling problem with energy consideration. International Journal of Production Economics, 216, 12-22. doi:10.1016/j.ijpe.2019.03.021
Mokhtari, H., & Hasani, A. (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
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. doi:10.1016/j.jclepro.2018.11.021
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. doi:10.1016/j.rcim.2019.04.006
Lacomme, P., Larabi, M., & Tchernev, N. (2013). Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Economics, 143(1), 24-34. doi:10.1016/j.ijpe.2010.07.012
Nageswararao, M., Narayanarao, K., & Ranagajanardhana, G. (2014). Simultaneous Scheduling of Machines and AGVs in Flexible Manufacturing System with Minimization of Tardiness Criterion. Procedia Materials Science, 5, 1492-1501. doi:10.1016/j.mspro.2014.07.336
Saidi-Mehrabad, M., Dehnavi-Arani, S., Evazabadian, F., & Mahmoodian, V. (2015). An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Computers & Industrial Engineering, 86, 2-13. doi:10.1016/j.cie.2015.01.003
Guo, Z., Zhang, D., Leung, S. Y. S., & Shi, L. (2016). A bi-level evolutionary optimization approach for integrated production and transportation scheduling. Applied Soft Computing, 42, 215-228. doi:10.1016/j.asoc.2016.01.052
Karimi, S., Ardalan, Z., Naderi, B., & Mohammadi, M. (2017). Scheduling flexible job-shops with transportation times: Mathematical models and a hybrid imperialist competitive algorithm. Applied Mathematical Modelling, 41, 667-682. doi:10.1016/j.apm.2016.09.022
Liu, Z., Guo, S., & Wang, L. (2019). Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption. Journal of Cleaner Production, 211, 765-786. doi:10.1016/j.jclepro.2018.11.231
Tang, D., & Dai, M. (2015). Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem. Chinese Journal of Mechanical Engineering, 28(5), 1048-1055. doi:10.3901/cjme.2015.0617.082
Hao, X., Lin, L., Gen, M., & Ohno, K. (2013). Effective Estimation of Distribution Algorithm for Stochastic Job Shop Scheduling Problem. Procedia Computer Science, 20, 102-107. doi:10.1016/j.procs.2013.09.246
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. doi:10.1016/j.cie.2011.12.014
Jarboui, B., Eddaly, M., & Siarry, P. (2009). An estimation of distribution algorithm for minimizing the total flowtime in permutation flowshop scheduling problems. Computers & Operations Research, 36(9), 2638-2646. doi:10.1016/j.cor.2008.11.004
Hauschild, M., & Pelikan, M. (2011). An introduction and survey of estimation of distribution algorithms. Swarm and Evolutionary Computation, 1(3), 111-128. doi:10.1016/j.swevo.2011.08.003
Liu, F., Xie, J., & Liu, S. (2015). A method for predicting the energy consumption of the main driving system of a machine tool in a machining process. Journal of Cleaner Production, 105, 171-177. doi:10.1016/j.jclepro.2014.09.058
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. doi:10.1016/j.rcim.2013.04.001
Beasley, J. E. (1990). OR-Library: Distributing Test Problems by Electronic Mail. Journal of the Operational Research Society, 41(11), 1069-1072. doi:10.1057/jors.1990.166
Zhao, F., Shao, Z., Wang, J., & Zhang, C. (2015). A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems. International Journal of Production Research, 54(4), 1039-1060. doi:10.1080/00207543.2015.1041575
Van Laarhoven, P. J. M., Aarts, E. H. L., & Lenstra, J. K. (1992). Job Shop Scheduling by Simulated Annealing. Operations Research, 40(1), 113-125. doi:10.1287/opre.40.1.113
Wang, L., & Zheng, D.-Z. (2001). An effective hybrid optimization strategy for job-shop scheduling problems. Computers & Operations Research, 28(6), 585-596. doi:10.1016/s0305-0548(99)00137-9
Dorndorf, U., & Pesch, E. (1995). Evolution based learning in a job shop scheduling environment. Computers & Operations Research, 22(1), 25-40. doi:10.1016/0305-0548(93)e0016-m
Park, B. J., Choi, H. R., & Kim, H. S. (2003). A hybrid genetic algorithm for the job shop scheduling problems. Computers & Industrial Engineering, 45(4), 597-613. doi:10.1016/s0360-8352(03)00077-9
[-]