An improved genetic algorithm with rolling window technology for dynamic integrated process planning and scheduling problem

2017 ◽  
Author(s):  
Lvjiang Yin ◽  
Liang Gao ◽  
Xinyu Li ◽  
Hao Xia
Keyword(s):  
Genetic Algorithm ◽  
Process Planning ◽  
Scheduling Problem ◽  
Integrated Process ◽  
Improved Genetic Algorithm ◽  
Planning And Scheduling ◽  
Rolling Window

scholarly journals Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Shuai Zhang ◽  
Yangbing Xu ◽  
Zhinan Yu ◽  
Wenyu Zhang ◽  
Dejian Yu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Process Planning ◽  
Imperialist Competitive Algorithm ◽  
Scheduling Problem ◽  
Integrated Process ◽  
Planning And Scheduling ◽  
Machine Breakdown ◽  
Competitive Algorithm ◽  
Effectiveness And Efficiency

Distributed integration of process planning and scheduling (DIPPS) extends traditional integrated process planning and scheduling (IPPS) by considering the distributed features of manufacturing. In this study, we first establish a mathematical model which contains all constraints for the DIPPS problem. Then, the imperialist competitive algorithm (ICA) is extended to effectively solve the DIPPS problem by improving country structure, assimilation strategy, and adding resistance procedure. Next, the genetic algorithm (GA) is adapted to maintain the robustness of the plan and schedule after machine breakdown. Finally, we perform a two-stage experiment to prove the effectiveness and efficiency of extended ICA and GA in solving DIPPS problem with machine breakdown.

Integrated Process Planning and Scheduling Based on Genetic Algorithms

2011 ◽  
Vol 291-294 ◽  
pp. 331-334
Author(s):  
Jin Feng Wang ◽  
Shi Jie Li ◽  
Shun Cheng Fan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Process Planning ◽  
Manufacturing System ◽  
Simulation Studies ◽  
Integrated Process ◽  
Improved Genetic Algorithm ◽  
Planning And Scheduling ◽  
Mutation Operators ◽  
Crossover And Mutation

Process planning and scheduling are two important manufacturing activities in the manufacturing system. In this paper, an improved genetic algorithm(GA) has been developed to facilitate the integration and optimization of process planning and scheduling. To improve the optimization performance, an efficient genetic representation has been developed. Selection, crossover, and mutation operators have been described. Simulation studies have been established to evaluate the performance of the algorithm. The results show that the algorithm is a promising and effective method for the integration of process planning and scheduling(IPPS).

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