How to cite this paper
Meng, L., Cheng, W., Zhang, B., Zou, W & Duan, P. (2024). A novel hybrid algorithm of genetic algorithm, variable neighborhood search and constraint programming for distributed flexible job shop scheduling problem.International Journal of Industrial Engineering Computations , 15(3), 813-832.
Refrences
Bagheri Rad, N., & Behnamian, J. (2023). Multi-objective collaborative job shop scheduling in a dynamic environment: Non-dominated sorting memetic algorithm. Journal of Ambient Intelligence and Humanized Computing, 14(3), 2657-2671.
Bukchin, Y., & Raviv, T. (2018). Constraint programming for solving various assembly line balancing problems. Omega, 78, 57-68.
Chan, F. T., Chung, S. H., Chan, L. Y., Finke, G., & Tiwari, M. K. (2006). Solving distributed FMS scheduling problems subject to maintenance: Genetic algorithms approach. Robotics and Computer-Integrated Manufacturing, 22(5-6), 493-504.
Chan, F. T., Chung, S. H., & Chan, P. L. Y. (2005). An adaptive genetic algorithm with dominated genes for distributed scheduling problems. Expert Systems with Applications, 29(2), 364-371.
Chang, H. C., & Liu, T. K. (2017). Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms. Journal of Intelligent Manufacturing, 28, 1973-1986.
Chung, S. H., Chan, F. T., & Chan, H. K. (2009). A modified genetic algorithm approach for scheduling of perfect maintenance in distributed production scheduling. Engineering Applications of Artificial Intelligence, 22(7), 1005-1014.
Dai, L. L., Pan, Q. K., Miao, Z. H., Suganthan, P. N., & Gao, K. Z. (2023). Multi-Objective Multi-Picking-Robot Task Allocation: Mathematical Model and Discrete Artificial Bee Colony Algorithm. IEEE Transactions on Intelligent Transportation Systems.
De Abreu, L. R., Araújo, K. A. G., de Athayde Prata, B., Nagano, M. S., & Moccellin, J. V. (2022). A new variable neighbourhood search with a constraint programming search strategy for the open shop scheduling problem with operation repetitions. Engineering Optimization, 54(9), 1563-1582.
De Giovanni, L., & Pezzella, F. (2010). An improved genetic algorithm for the distributed and flexible job-shop scheduling problem. European journal of operational research, 200(2), 395-408.
Du, Y., Li, J. Q., Luo, C., & Meng, L. L. (2021). A hybrid estimation of distribution algorithm for distributed flexible job shop scheduling with crane transportations. Swarm and Evolutionary Computation, 62, 100861.
Gedik, R., Kalathia, D., Egilmez, G., & Kirac, E. (2018). A constraint programming approach for solving unrelated parallel machine scheduling problem. Computers & Industrial Engineering, 121, 139-149.
Ham, A. M., & Cakici, E. (2016). Flexible job shop scheduling problem with parallel batch processing machines: MIP and CP approaches. Computers & Industrial Engineering, 102, 160-165.
Ham, A., Park, M. J., & Kim, K. M. (2021). Energy-aware flexible job shop scheduling using mixed integer programming and constraint programming. Mathematical Problems in Engineering, 2021, 1-12.
He, X., Pan, Q. K., Gao, L., Neufeld, J. S., & Gupta, J. N. (2024). Historical information based iterated greedy algorithm for distributed flowshop group scheduling problem with sequence-dependent setup times. Omega, 123, 102997.
Li, J., Gu, X., Zhang, Y., & Zhou, X. (2022). Distributed flexible job-shop scheduling problem based on hybrid chemical reaction optimization algorithm. Complex System Modeling and Simulation, 2(2), 156-173.
Jia, H. Z., Nee, A. Y., Fuh, J. Y., & Zhang, Y. F. (2003). A modified genetic algorithm for distributed scheduling problems. Journal of Intelligent Manufacturing, 14, 351-362.
Jia, H. Z., Fuh, J. Y., Nee, A. Y., & Zhang, Y. F. (2002). Web-based multi-functional scheduling system for a distributed manufacturing environment. Concurrent Engineering, 10(1), 27-39.
Karimi, H., Rahmati, S. H. A., & Zandieh, M. (2012). An efficient knowledge-based algorithm for the flexible job shop scheduling problem. Knowledge-Based Systems, 36, 236-244.
Li, J. Q., Duan, P., Cao, J., Lin, X. P., & Han, Y. Y. (2018). A hybrid Pareto-based tabu search for the distributed flexible job shop scheduling problem with E/T criteria. IEEE Access, 6, 58883-58897.
Li, X., Xie, J., Ma, Q., Gao, L., & Li, P. (2022). Improved gray wolf optimizer for distributed flexible job shop scheduling problem. Science China Technological Sciences, 65(9), 2105-2115.
Lin, C. S., Lee, I. L., & Wu, M. C. (2019). Merits of using chromosome representations and shadow chromosomes in genetic algorithms for solving scheduling problems. Robotics and computer-integrated manufacturing, 58, 196-207.
Liu, T. K., Chen, Y. P., & Chou, J. H. (2014). Solving distributed and flexible job-shop scheduling problems for a real-world fastener manufacturer. IEEE Access, 2, 1598-1606.
Lu, P. H., Wu, M. C., Tan, H., Peng, Y. H., & Chen, C. F. (2018). A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems. Journal of Intelligent Manufacturing, 29, 19-34.
Luo, Q., Deng, Q., Gong, G., Zhang, L., Han, W., & Li, K. (2020). An efficient memetic algorithm for distributed flexible job shop scheduling problem with transfers. Expert Systems with Applications, 160, 113721.
Luo, Q., Deng, Q., Gong, G., Guo, X., & Liu, X. (2022). A distributed flexible job shop scheduling problem considering worker arrangement using an improved memetic algorithm. Expert Systems with Applications, 207, 117984.
Marzouki, B., Driss, O. B., & Ghédira, K. (2018). Solving distributed and flexible job shop scheduling problem using a chemical reaction optimization metaheuristic. Procedia Computer Science, 126, 1424-1433.
Meng, L., Lu, C., Zhang, B., Ren, Y., Lv, C., Sang, H., ... & Zhang, C. (2021). Constraint programing for solving four complex flexible shop scheduling problems. IET Collaborative Intelligent Manufacturing, 3(2), 147-160.
Meng, L., Zhang, C., Zhang, B., & Ren, Y. (2019a). Mathematical modeling and optimization of energy-conscious flexible job shop scheduling problem with worker flexibility. IEEE Access, 7, 68043-68059.
Meng, L., Zhang, C., Shao, X., Ren, Y., & Ren, C. (2019b). 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.
Meng, L., Zhang, C., Shao, X., & Ren, Y. (2019c). MILP models for energy-aware flexible job shop scheduling problem. Journal of cleaner production, 210, 710-723.
Meng, L., Zhang, C., Ren, Y., Zhang, B., & Lv, C. (2020a). Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem. Computers & industrial engineering, 142, 106347.
Meng, L., Zhang, C., Shao, X., Zhang, B., Ren, Y., & Lin, W. (2020b). More MILP models for hybrid flow shop scheduling problem and its extended problems. International journal of production research, 58(13), 3905-3930.
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.
Meng, L., Gao, K., Ren, Y., Zhang, B., Sang, H., & Chaoyong, Z. (2022). Novel MILP and CP models for distributed hybrid flowshop scheduling problem with sequence-dependent setup times. Swarm and Evolutionary Computation, 71, 101058.
Meng, L., Duan, P., Gao, K., Zhang, B., Zou, W., Han, Y., & Zhang, C. (2024). MIP modeling of energy-conscious FJSP and its extended problems: From simplicity to complexity. Expert Systems with Applications, 241, 122594.
Meng, L., Cheng, W., Zhang, B., Zou, W., Fang, W., & Duan, P. (2023). An Improved Genetic Algorithm for Solving the Multi-AGV Flexible Job Shop Scheduling Problem. Sensors, 23(8), 3815.
Meng, L., Ren, Y., Zhang, B., Li, J. Q., Sang, H., & Zhang, C. (2020). MILP modeling and optimization of energy-efficient distributed flexible job shop scheduling problem. IEEE Access, 8, 191191-191203.
Naderi, B., & Azab, A. (2014). Modeling and heuristics for scheduling of distributed job shops. Expert Systems with Applications, 41(17), 7754-7763.
Şahman, M. A. (2021). A discrete spotted hyena optimizer for solving distributed job shop scheduling problems. Applied Soft Computing, 106, 107349.
Sang, Y., & Tan, J. (2022). Intelligent factory many-objective distributed flexible job shop collaborative scheduling method. Computers & Industrial Engineering, 164, 107884.
Tang, H., Fang, B., Liu, R., Li, Y., & Guo, S. (2022). A hybrid teaching and learning-based optimization algorithm for distributed sand casting job-shop scheduling problem. Applied Soft Computing, 120, 108694.
Wang, L., & Peng, Z. P. (2020). Solving energy-efficient distributed job shop scheduling via multi-objective evolutionary algorithm with decomposition. Swarm and Evolutionary Computation, 58, 100745.
Wu, M. C., Lin, C. S., Lin, C. H., & Chen, C. F. (2017). Effects of different chromosome representations in developing genetic algorithms to solve DFJS scheduling problems. Computers & Operations Research, 80, 101-112.
Wu, X., Liu, X., & Zhao, N. (2019). An improved differential evolution algorithm for solving a distributed assembly flexible job shop scheduling problem. Memetic Computing, 11, 335-355.
Xie, J., Gao, L., Pan, Q. K., & Tasgetiren, M. F. (2019). An effective multi-objective artificial bee colony algorithm for energy efficient distributed job shop scheduling. Procedia Manufacturing, 39, 1194-1203.
Xu, W., Hu, Y., Luo, W., Wang, L., & Wu, R. (2021). A multi-objective scheduling method for distributed and flexible job shop based on hybrid genetic algorithm and tabu search considering operation outsourcing and carbon emission. Computers & Industrial Engineering, 157, 107318.
Zhang, L., Yu, C., & Wong, T. N. (2021). A graph-based constraint programming approach for the integrated process planning and scheduling problem. Computers & Operations Research, 131, 105282.
Zhu, K., Gong, G., Peng, N., Zhang, L., Huang, D., Luo, Q., & Li, X. (2023). Dynamic distributed flexible job-shop scheduling problem considering operation inspection. Expert Systems with Applications, 224, 119840.
Ziaee, M. (2014). A heuristic algorithm for the distributed and flexible job-shop scheduling problem. The Journal of Supercomputing, 67, 69-83.
Bukchin, Y., & Raviv, T. (2018). Constraint programming for solving various assembly line balancing problems. Omega, 78, 57-68.
Chan, F. T., Chung, S. H., Chan, L. Y., Finke, G., & Tiwari, M. K. (2006). Solving distributed FMS scheduling problems subject to maintenance: Genetic algorithms approach. Robotics and Computer-Integrated Manufacturing, 22(5-6), 493-504.
Chan, F. T., Chung, S. H., & Chan, P. L. Y. (2005). An adaptive genetic algorithm with dominated genes for distributed scheduling problems. Expert Systems with Applications, 29(2), 364-371.
Chang, H. C., & Liu, T. K. (2017). Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms. Journal of Intelligent Manufacturing, 28, 1973-1986.
Chung, S. H., Chan, F. T., & Chan, H. K. (2009). A modified genetic algorithm approach for scheduling of perfect maintenance in distributed production scheduling. Engineering Applications of Artificial Intelligence, 22(7), 1005-1014.
Dai, L. L., Pan, Q. K., Miao, Z. H., Suganthan, P. N., & Gao, K. Z. (2023). Multi-Objective Multi-Picking-Robot Task Allocation: Mathematical Model and Discrete Artificial Bee Colony Algorithm. IEEE Transactions on Intelligent Transportation Systems.
De Abreu, L. R., Araújo, K. A. G., de Athayde Prata, B., Nagano, M. S., & Moccellin, J. V. (2022). A new variable neighbourhood search with a constraint programming search strategy for the open shop scheduling problem with operation repetitions. Engineering Optimization, 54(9), 1563-1582.
De Giovanni, L., & Pezzella, F. (2010). An improved genetic algorithm for the distributed and flexible job-shop scheduling problem. European journal of operational research, 200(2), 395-408.
Du, Y., Li, J. Q., Luo, C., & Meng, L. L. (2021). A hybrid estimation of distribution algorithm for distributed flexible job shop scheduling with crane transportations. Swarm and Evolutionary Computation, 62, 100861.
Gedik, R., Kalathia, D., Egilmez, G., & Kirac, E. (2018). A constraint programming approach for solving unrelated parallel machine scheduling problem. Computers & Industrial Engineering, 121, 139-149.
Ham, A. M., & Cakici, E. (2016). Flexible job shop scheduling problem with parallel batch processing machines: MIP and CP approaches. Computers & Industrial Engineering, 102, 160-165.
Ham, A., Park, M. J., & Kim, K. M. (2021). Energy-aware flexible job shop scheduling using mixed integer programming and constraint programming. Mathematical Problems in Engineering, 2021, 1-12.
He, X., Pan, Q. K., Gao, L., Neufeld, J. S., & Gupta, J. N. (2024). Historical information based iterated greedy algorithm for distributed flowshop group scheduling problem with sequence-dependent setup times. Omega, 123, 102997.
Li, J., Gu, X., Zhang, Y., & Zhou, X. (2022). Distributed flexible job-shop scheduling problem based on hybrid chemical reaction optimization algorithm. Complex System Modeling and Simulation, 2(2), 156-173.
Jia, H. Z., Nee, A. Y., Fuh, J. Y., & Zhang, Y. F. (2003). A modified genetic algorithm for distributed scheduling problems. Journal of Intelligent Manufacturing, 14, 351-362.
Jia, H. Z., Fuh, J. Y., Nee, A. Y., & Zhang, Y. F. (2002). Web-based multi-functional scheduling system for a distributed manufacturing environment. Concurrent Engineering, 10(1), 27-39.
Karimi, H., Rahmati, S. H. A., & Zandieh, M. (2012). An efficient knowledge-based algorithm for the flexible job shop scheduling problem. Knowledge-Based Systems, 36, 236-244.
Li, J. Q., Duan, P., Cao, J., Lin, X. P., & Han, Y. Y. (2018). A hybrid Pareto-based tabu search for the distributed flexible job shop scheduling problem with E/T criteria. IEEE Access, 6, 58883-58897.
Li, X., Xie, J., Ma, Q., Gao, L., & Li, P. (2022). Improved gray wolf optimizer for distributed flexible job shop scheduling problem. Science China Technological Sciences, 65(9), 2105-2115.
Lin, C. S., Lee, I. L., & Wu, M. C. (2019). Merits of using chromosome representations and shadow chromosomes in genetic algorithms for solving scheduling problems. Robotics and computer-integrated manufacturing, 58, 196-207.
Liu, T. K., Chen, Y. P., & Chou, J. H. (2014). Solving distributed and flexible job-shop scheduling problems for a real-world fastener manufacturer. IEEE Access, 2, 1598-1606.
Lu, P. H., Wu, M. C., Tan, H., Peng, Y. H., & Chen, C. F. (2018). A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems. Journal of Intelligent Manufacturing, 29, 19-34.
Luo, Q., Deng, Q., Gong, G., Zhang, L., Han, W., & Li, K. (2020). An efficient memetic algorithm for distributed flexible job shop scheduling problem with transfers. Expert Systems with Applications, 160, 113721.
Luo, Q., Deng, Q., Gong, G., Guo, X., & Liu, X. (2022). A distributed flexible job shop scheduling problem considering worker arrangement using an improved memetic algorithm. Expert Systems with Applications, 207, 117984.
Marzouki, B., Driss, O. B., & Ghédira, K. (2018). Solving distributed and flexible job shop scheduling problem using a chemical reaction optimization metaheuristic. Procedia Computer Science, 126, 1424-1433.
Meng, L., Lu, C., Zhang, B., Ren, Y., Lv, C., Sang, H., ... & Zhang, C. (2021). Constraint programing for solving four complex flexible shop scheduling problems. IET Collaborative Intelligent Manufacturing, 3(2), 147-160.
Meng, L., Zhang, C., Zhang, B., & Ren, Y. (2019a). Mathematical modeling and optimization of energy-conscious flexible job shop scheduling problem with worker flexibility. IEEE Access, 7, 68043-68059.
Meng, L., Zhang, C., Shao, X., Ren, Y., & Ren, C. (2019b). 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.
Meng, L., Zhang, C., Shao, X., & Ren, Y. (2019c). MILP models for energy-aware flexible job shop scheduling problem. Journal of cleaner production, 210, 710-723.
Meng, L., Zhang, C., Ren, Y., Zhang, B., & Lv, C. (2020a). Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem. Computers & industrial engineering, 142, 106347.
Meng, L., Zhang, C., Shao, X., Zhang, B., Ren, Y., & Lin, W. (2020b). More MILP models for hybrid flow shop scheduling problem and its extended problems. International journal of production research, 58(13), 3905-3930.
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.
Meng, L., Gao, K., Ren, Y., Zhang, B., Sang, H., & Chaoyong, Z. (2022). Novel MILP and CP models for distributed hybrid flowshop scheduling problem with sequence-dependent setup times. Swarm and Evolutionary Computation, 71, 101058.
Meng, L., Duan, P., Gao, K., Zhang, B., Zou, W., Han, Y., & Zhang, C. (2024). MIP modeling of energy-conscious FJSP and its extended problems: From simplicity to complexity. Expert Systems with Applications, 241, 122594.
Meng, L., Cheng, W., Zhang, B., Zou, W., Fang, W., & Duan, P. (2023). An Improved Genetic Algorithm for Solving the Multi-AGV Flexible Job Shop Scheduling Problem. Sensors, 23(8), 3815.
Meng, L., Ren, Y., Zhang, B., Li, J. Q., Sang, H., & Zhang, C. (2020). MILP modeling and optimization of energy-efficient distributed flexible job shop scheduling problem. IEEE Access, 8, 191191-191203.
Naderi, B., & Azab, A. (2014). Modeling and heuristics for scheduling of distributed job shops. Expert Systems with Applications, 41(17), 7754-7763.
Şahman, M. A. (2021). A discrete spotted hyena optimizer for solving distributed job shop scheduling problems. Applied Soft Computing, 106, 107349.
Sang, Y., & Tan, J. (2022). Intelligent factory many-objective distributed flexible job shop collaborative scheduling method. Computers & Industrial Engineering, 164, 107884.
Tang, H., Fang, B., Liu, R., Li, Y., & Guo, S. (2022). A hybrid teaching and learning-based optimization algorithm for distributed sand casting job-shop scheduling problem. Applied Soft Computing, 120, 108694.
Wang, L., & Peng, Z. P. (2020). Solving energy-efficient distributed job shop scheduling via multi-objective evolutionary algorithm with decomposition. Swarm and Evolutionary Computation, 58, 100745.
Wu, M. C., Lin, C. S., Lin, C. H., & Chen, C. F. (2017). Effects of different chromosome representations in developing genetic algorithms to solve DFJS scheduling problems. Computers & Operations Research, 80, 101-112.
Wu, X., Liu, X., & Zhao, N. (2019). An improved differential evolution algorithm for solving a distributed assembly flexible job shop scheduling problem. Memetic Computing, 11, 335-355.
Xie, J., Gao, L., Pan, Q. K., & Tasgetiren, M. F. (2019). An effective multi-objective artificial bee colony algorithm for energy efficient distributed job shop scheduling. Procedia Manufacturing, 39, 1194-1203.
Xu, W., Hu, Y., Luo, W., Wang, L., & Wu, R. (2021). A multi-objective scheduling method for distributed and flexible job shop based on hybrid genetic algorithm and tabu search considering operation outsourcing and carbon emission. Computers & Industrial Engineering, 157, 107318.
Zhang, L., Yu, C., & Wong, T. N. (2021). A graph-based constraint programming approach for the integrated process planning and scheduling problem. Computers & Operations Research, 131, 105282.
Zhu, K., Gong, G., Peng, N., Zhang, L., Huang, D., Luo, Q., & Li, X. (2023). Dynamic distributed flexible job-shop scheduling problem considering operation inspection. Expert Systems with Applications, 224, 119840.
Ziaee, M. (2014). A heuristic algorithm for the distributed and flexible job-shop scheduling problem. The Journal of Supercomputing, 67, 69-83.