Mathematical modeling and two metaheuristic algorithms for integrated process planning and group scheduling with sequence-dependent setup time

被引:0
作者
Mohammad Reza Hosseinzadeh
Mehdi Heydari
Mohammad Mahdavi Mazdeh
机构
[1] Iran University of Science and Technology,Department of Industrial Engineering
来源
Operational Research | 2022年 / 22卷
关键词
Process planning; Group scheduling; Sequence-dependent setup time; Mathematical modeling; Genetic algorithm; Water cycle algorithm;
D O I
暂无
中图分类号
学科分类号
摘要
The advancement of technology enables manufacturing companies to employ multifunction machines to increase the flexibility of a system in producing miscellaneous products in a short time. In this situation, goods can be usually produced through different process plans, and considering process planning and scheduling in an integrated framework would be essential. Furthermore, group processing is regarded to overcome the difficulty of long setup times and consequently increase the productivity of a manufacturing system. This paper deals with the integrated process planning and group scheduling problem with sequence-dependent setup time between each group of jobs. Two mixed-integer linear programming models with different approaches are presented. Moreover, two metaheuristic algorithms are proposed to solve the problems heuristically. The experiments show the high performance of the combination-based mathematical model for small-size problems as well as the proposed metaheuristic algorithms for medium-size and large-size instances.
引用
收藏
页码:5055 / 5105
页数:50
相关论文
共 197 条
[21]  
Chryssolouris G(1999)Scheduling in a production environment with multiple process plans per job Int J Prod Res 17 502-590
[22]  
Chan S(2003)A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling Comput Oper Res 11 586-180
[23]  
Cobb W(1993)Two-machine shop scheduling problems with batch processing Math Comput Model 18 166-95
[24]  
Eskandar H(2019)Sustainable integrated process planning and scheduling optimization using a genetic algorithm with an integrated chromosome representation Sustainability 59 80-180
[25]  
Sadollah A(2001)Integration of process planning and scheduling using simulation based genetic algorithms Int J Adv Manuf Technol 20 161-298
[26]  
Bahreininejad A(2010)Integrated process planning and scheduling by an agent-based ant colony optimization Comput Ind Eng 5 289-1264
[27]  
Hamdi M(2007)A simulated annealing-based optimization approach for integrated process planning and scheduling Int J Comput Integr Manuf 126 1256-6691
[28]  
Guo YW(2010)Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling Comput Oper Res 37 6683-1945
[29]  
Li W(2010)A review on integrated process planning and scheduling Int J Manuf Res 39 1933-1046
[30]  
Mileham AR(2010)An effective hybrid algorithm for integrated process planning and scheduling Int J Prod Econ 49 1036-4343