How to cite this paper
Lingkon, M & Dash, A. (2024). Multi-objective flexible job-shop scheduling in hospital using discrete particle swarm optimization algorithm with adaptive inertia weight (DPSO-AIW.Journal of Project Management, 9(4), 387-402.
Refrences
Dai, Y. (2021). Improved NSGA-II Algorithm for multi-objective flexible job shop scheduling problem. Journal of Physics: Conference Series, 1952(4). https://doi.org/10.1088/1742-6596/1952/4/042065
Ding, H., & Gu, X. (2020a). Hybrid of human learning optimization algorithm and particle swarm optimization algo-rithm with scheduling strategies for the flexible job-shop scheduling problem. Neurocomputing, 414, 313–332. https://doi.org/10.1016/j.neucom.2020.07.004
Ding, H., & Gu, X. (2020b). Improved particle swarm optimization algorithm based novel encoding and decoding schemes for flexible job shop scheduling problem. Computers and Operations Research, 121. https://doi.org/10.1016/j.cor.2020.104951
Fattahi, P., Bagheri Rad, N., Daneshamooz, F., & Ahmadi, S. (2020). A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process. Assembly Automation, 40(3), 419–432. https://doi.org/10.1108/AA-11-2018-0178
Fontes, D. B. M. M., Homayouni, S. M., & Gonçalves, J. F. (2023). A hybrid particle swarm optimization and simulated annealing algorithm for the job shop scheduling problem with transport resources. European Journal of Operational Research, 306(3), 1140–1157. https://doi.org/10.1016/j.ejor.2022.09.006
Gu, X. L., Huang, M., & Liang, X. (2020). A Discrete Particle Swarm Optimization Algorithm with Adaptive Inertia Weight for Solving Multiobjective Flexible Job-shop Scheduling Problem. IEEE Access, 8, 33125–33136. https://doi.org/10.1109/ACCESS.2020.2974014
Huang, S., Tian, N., Wang, Y., & Ji, Z. (2016a). Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization. SpringerPlus, 5(1). https://doi.org/10.1186/s40064-016-3054-z
Huang, S., Tian, N., Wang, Y., & Ji, Z. (2016b). Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization. SpringerPlus, 5(1). https://doi.org/10.1186/s40064-016-3054-z
Huang, X., & Yang, L. (2019). A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time. International Journal of Intelligent Computing and Cybernetics, 12(2), 154–174. https://doi.org/10.1108/IJICC-10-2018-0136
Hui, H. (2012). Approach for multi-objective flexible job shop scheduling. Advanced Materials Research, 542–543, 407–410. https://doi.org/10.4028/www.scientific.net/AMR.542-543.407
Institute of Electrical and Electronics Engineers. (n.d.). Evolutionary Computation (CEC), 2010 IEEE Congress on : date, 18-23 July 2010.
Jin, X., & Wang, F. (2022). A Multioffspring Genetic Algorithm Based on Sorting Grouping Selection and Combination Pairing Crossover. Mathematical Problems in Engineering, 2022, 1–20. https://doi.org/10.1155/2022/4203082
Kacem, I., Hammadi, S., & Borne, P. (2002). Pareto-optimality approach for flexible job-shop scheduling problems: hy-bridization of evolutionary algorithms and fuzzy logic. In Mathematics and Computers in Simulation (Vol. 60).
Kong, J., & Wang, Z. (2024). Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm. Applied Sciences, 14(6), 2586. https://doi.org/10.3390/app14062586
Lai, R., Gao, B., & Lin, W. (2021). Solving No-Wait Flow Shop Scheduling Problem Based on Discrete Wolf Pack Al-gorithm. Scientific Programming, 2021. https://doi.org/10.1155/2021/4731012
Li, M., Qianting, L., Meiqiong, M., & Sicong, L. (2016). Optimization and Application of Single-point Crossover and Multi-offspring Genetic Algorithm. International Journal of Hybrid Information Technology, 9(1), 1–8. https://doi.org/10.14257/ijhit.2016.9.1.01
Liu, C., Yao, Y., & Zhu, H. (2022). Hybrid salp swarm algorithm for solving the green scheduling problem in a double-flexible job shop. Applied Sciences (Switzerland), 12(1). https://doi.org/10.3390/app12010205
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 and Operations Research, 135. https://doi.org/10.1016/j.cor.2021.105431
Moslehi, G., & Mahnam, M. (2011). A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. International Journal of Production Economics, 129(1), 14–22. https://doi.org/10.1016/j.ijpe.2010.08.004
Nouiri, M., Bekrar, A., Jemai, A., Niar, S., & Ammari, A. C. (2018). An effective and distributed particle swarm optimi-zation algorithm for flexible job-shop scheduling problem. Journal of Intelligent Manufacturing, 29(3), 603–615. https://doi.org/10.1007/s10845-015-1039-3
Pezzella, F., Morganti, G., & Ciaschetti, G. (2008). A genetic algorithm for the Flexible Job-shop Scheduling Problem. Computers and Operations Research, 35(10), 3202–3212. https://doi.org/10.1016/j.cor.2007.02.014
Piroozfard, H., Wong, K. Y., & Wong, W. P. (2018). Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resources, Conservation and Recycling, 128, 267–283. https://doi.org/10.1016/j.resconrec.2016.12.001
Ren, W., Wen, J., Yan, Y., Hu, Y., Guan, Y., & Li, J. (2021). Multi-objective optimisation for energy-aware flexible job-shop scheduling problem with assembly operations. International Journal of Production Research, 59(23), 7216–7231. https://doi.org/10.1080/00207543.2020.1836421
Tan, W., Yuan, X., Huang, G., & Liu, Z. (2021). Low-carbon joint scheduling in flexible open-shop environment with constrained automatic guided vehicle by multi-objective particle swarm optimization. Applied Soft Computing, 111. https://doi.org/10.1016/j.asoc.2021.107695
Wang, L., Wang, S., & Liu, M. (2013). A Pareto-based estimation of distribution algorithm for the multi-objective flex-ible job-shop scheduling problem. International Journal of Production Research, 51(12), 3574–3592. https://doi.org/10.1080/00207543.2012.752588
Wang, X., Gao, L., Zhang, C., & Shao, X. (2010). A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem. International Journal of Advanced Manufacturing Technology, 51(5–8), 757–767. https://doi.org/10.1007/s00170-010-2642-2
Wu, M., Yang, D., & Liu, T. (2022). An Improved Particle Swarm Algorithm with the Elite Retain Strategy for Solving Flexible Jobshop Scheduling Problem. Journal of Physics: Conference Series, 2173(1). https://doi.org/10.1088/1742-6596/2173/1/012082
Xu, M., Lu, J., Zhu, F., Yu, F., Han, T., & Xu, M. (2021). Research and application for hydraulic cylinder workshop scheduling considering on time delivery rate. Chinese Control Conference, CCC, 2021-July, 1905–1910. https://doi.org/10.23919/CCC52363.2021.9550547
Xu, X., & Wang, L. (2021). An Improved Gaming Particle Swarm Algorithm Based the Rules of Flexible Job Shop Scheduling. ICSAI 2021 - 7th International Conference on Systems and Informatics. https://doi.org/10.1109/ICSAI53574.2021.9664124
Zhang, J., Jie, J., Wang, W., & Xu, X. (2017). A hybrid particle swarm optimisation for multi-objective flexible job-shop scheduling problem with dual-resources constrained. In Int. J. Computing Science and Mathematics (Vol. 8, Is-sue 6).
Zhang, S., & Gu, X. (2023). A discrete whale optimization algorithm for the no-wait flow shop scheduling problem. Measurement and Control (United Kingdom), 56(9–10), 1764–1779. https://doi.org/10.1177/00202940231180622
Zhang, Y., Cheng, S., Shi, Y., Gong, D. W., & Zhao, X. (2019). Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm. Expert Systems with Applications, 137, 46-58. https://doi.org/10.1016/j.eswa.2019.06.044
Zhang, Y., Zhu, H., & Tang, D. (2020). An improved hybrid particle swarm optimization for multi-objective flexible job-shop scheduling problem. Kybernetes, 49(12), 2873–2892. https://doi.org/10.1108/K-06-2019-0430
Zhu, Z., & Zhou, X. (2021). A multi-objective multi-micro-swarm leadership hierarchy-based optimizer for uncertain flexible job shop scheduling problem with job precedence constraints. Expert Systems with Applications, 182. https://doi.org/10.1016/j.eswa.2021.115214
Ding, H., & Gu, X. (2020a). Hybrid of human learning optimization algorithm and particle swarm optimization algo-rithm with scheduling strategies for the flexible job-shop scheduling problem. Neurocomputing, 414, 313–332. https://doi.org/10.1016/j.neucom.2020.07.004
Ding, H., & Gu, X. (2020b). Improved particle swarm optimization algorithm based novel encoding and decoding schemes for flexible job shop scheduling problem. Computers and Operations Research, 121. https://doi.org/10.1016/j.cor.2020.104951
Fattahi, P., Bagheri Rad, N., Daneshamooz, F., & Ahmadi, S. (2020). A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process. Assembly Automation, 40(3), 419–432. https://doi.org/10.1108/AA-11-2018-0178
Fontes, D. B. M. M., Homayouni, S. M., & Gonçalves, J. F. (2023). A hybrid particle swarm optimization and simulated annealing algorithm for the job shop scheduling problem with transport resources. European Journal of Operational Research, 306(3), 1140–1157. https://doi.org/10.1016/j.ejor.2022.09.006
Gu, X. L., Huang, M., & Liang, X. (2020). A Discrete Particle Swarm Optimization Algorithm with Adaptive Inertia Weight for Solving Multiobjective Flexible Job-shop Scheduling Problem. IEEE Access, 8, 33125–33136. https://doi.org/10.1109/ACCESS.2020.2974014
Huang, S., Tian, N., Wang, Y., & Ji, Z. (2016a). Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization. SpringerPlus, 5(1). https://doi.org/10.1186/s40064-016-3054-z
Huang, S., Tian, N., Wang, Y., & Ji, Z. (2016b). Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization. SpringerPlus, 5(1). https://doi.org/10.1186/s40064-016-3054-z
Huang, X., & Yang, L. (2019). A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time. International Journal of Intelligent Computing and Cybernetics, 12(2), 154–174. https://doi.org/10.1108/IJICC-10-2018-0136
Hui, H. (2012). Approach for multi-objective flexible job shop scheduling. Advanced Materials Research, 542–543, 407–410. https://doi.org/10.4028/www.scientific.net/AMR.542-543.407
Institute of Electrical and Electronics Engineers. (n.d.). Evolutionary Computation (CEC), 2010 IEEE Congress on : date, 18-23 July 2010.
Jin, X., & Wang, F. (2022). A Multioffspring Genetic Algorithm Based on Sorting Grouping Selection and Combination Pairing Crossover. Mathematical Problems in Engineering, 2022, 1–20. https://doi.org/10.1155/2022/4203082
Kacem, I., Hammadi, S., & Borne, P. (2002). Pareto-optimality approach for flexible job-shop scheduling problems: hy-bridization of evolutionary algorithms and fuzzy logic. In Mathematics and Computers in Simulation (Vol. 60).
Kong, J., & Wang, Z. (2024). Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm. Applied Sciences, 14(6), 2586. https://doi.org/10.3390/app14062586
Lai, R., Gao, B., & Lin, W. (2021). Solving No-Wait Flow Shop Scheduling Problem Based on Discrete Wolf Pack Al-gorithm. Scientific Programming, 2021. https://doi.org/10.1155/2021/4731012
Li, M., Qianting, L., Meiqiong, M., & Sicong, L. (2016). Optimization and Application of Single-point Crossover and Multi-offspring Genetic Algorithm. International Journal of Hybrid Information Technology, 9(1), 1–8. https://doi.org/10.14257/ijhit.2016.9.1.01
Liu, C., Yao, Y., & Zhu, H. (2022). Hybrid salp swarm algorithm for solving the green scheduling problem in a double-flexible job shop. Applied Sciences (Switzerland), 12(1). https://doi.org/10.3390/app12010205
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 and Operations Research, 135. https://doi.org/10.1016/j.cor.2021.105431
Moslehi, G., & Mahnam, M. (2011). A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. International Journal of Production Economics, 129(1), 14–22. https://doi.org/10.1016/j.ijpe.2010.08.004
Nouiri, M., Bekrar, A., Jemai, A., Niar, S., & Ammari, A. C. (2018). An effective and distributed particle swarm optimi-zation algorithm for flexible job-shop scheduling problem. Journal of Intelligent Manufacturing, 29(3), 603–615. https://doi.org/10.1007/s10845-015-1039-3
Pezzella, F., Morganti, G., & Ciaschetti, G. (2008). A genetic algorithm for the Flexible Job-shop Scheduling Problem. Computers and Operations Research, 35(10), 3202–3212. https://doi.org/10.1016/j.cor.2007.02.014
Piroozfard, H., Wong, K. Y., & Wong, W. P. (2018). Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resources, Conservation and Recycling, 128, 267–283. https://doi.org/10.1016/j.resconrec.2016.12.001
Ren, W., Wen, J., Yan, Y., Hu, Y., Guan, Y., & Li, J. (2021). Multi-objective optimisation for energy-aware flexible job-shop scheduling problem with assembly operations. International Journal of Production Research, 59(23), 7216–7231. https://doi.org/10.1080/00207543.2020.1836421
Tan, W., Yuan, X., Huang, G., & Liu, Z. (2021). Low-carbon joint scheduling in flexible open-shop environment with constrained automatic guided vehicle by multi-objective particle swarm optimization. Applied Soft Computing, 111. https://doi.org/10.1016/j.asoc.2021.107695
Wang, L., Wang, S., & Liu, M. (2013). A Pareto-based estimation of distribution algorithm for the multi-objective flex-ible job-shop scheduling problem. International Journal of Production Research, 51(12), 3574–3592. https://doi.org/10.1080/00207543.2012.752588
Wang, X., Gao, L., Zhang, C., & Shao, X. (2010). A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem. International Journal of Advanced Manufacturing Technology, 51(5–8), 757–767. https://doi.org/10.1007/s00170-010-2642-2
Wu, M., Yang, D., & Liu, T. (2022). An Improved Particle Swarm Algorithm with the Elite Retain Strategy for Solving Flexible Jobshop Scheduling Problem. Journal of Physics: Conference Series, 2173(1). https://doi.org/10.1088/1742-6596/2173/1/012082
Xu, M., Lu, J., Zhu, F., Yu, F., Han, T., & Xu, M. (2021). Research and application for hydraulic cylinder workshop scheduling considering on time delivery rate. Chinese Control Conference, CCC, 2021-July, 1905–1910. https://doi.org/10.23919/CCC52363.2021.9550547
Xu, X., & Wang, L. (2021). An Improved Gaming Particle Swarm Algorithm Based the Rules of Flexible Job Shop Scheduling. ICSAI 2021 - 7th International Conference on Systems and Informatics. https://doi.org/10.1109/ICSAI53574.2021.9664124
Zhang, J., Jie, J., Wang, W., & Xu, X. (2017). A hybrid particle swarm optimisation for multi-objective flexible job-shop scheduling problem with dual-resources constrained. In Int. J. Computing Science and Mathematics (Vol. 8, Is-sue 6).
Zhang, S., & Gu, X. (2023). A discrete whale optimization algorithm for the no-wait flow shop scheduling problem. Measurement and Control (United Kingdom), 56(9–10), 1764–1779. https://doi.org/10.1177/00202940231180622
Zhang, Y., Cheng, S., Shi, Y., Gong, D. W., & Zhao, X. (2019). Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm. Expert Systems with Applications, 137, 46-58. https://doi.org/10.1016/j.eswa.2019.06.044
Zhang, Y., Zhu, H., & Tang, D. (2020). An improved hybrid particle swarm optimization for multi-objective flexible job-shop scheduling problem. Kybernetes, 49(12), 2873–2892. https://doi.org/10.1108/K-06-2019-0430
Zhu, Z., & Zhou, X. (2021). A multi-objective multi-micro-swarm leadership hierarchy-based optimizer for uncertain flexible job shop scheduling problem with job precedence constraints. Expert Systems with Applications, 182. https://doi.org/10.1016/j.eswa.2021.115214