Particle swarm optimization hybridized with genetic algorithm for uncertain integrated process planning and scheduling with interval processing time

2019 ◽  
Vol 135 ◽  
pp. 1036-1046 ◽  
Author(s):  
Xinyu Li ◽  
Liang Gao ◽  
Wenwen Wang ◽  
Cuiyu Wang ◽  
Long Wen
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Process Planning ◽  
Processing Time ◽  
Particle Swarm ◽  
Integrated Process ◽  
Swarm Optimization ◽  
Planning And Scheduling

scholarly journals A Mathematical Model for Multiworkshop IPPS Problem in Batch Production

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Li Ba ◽  
Yan Li ◽  
Mingshun Yang ◽  
Xueliang Wu ◽  
Yong Liu ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Particle Swarm Optimization ◽  
Process Planning ◽  
Production Scheduling ◽  
Complex Problem ◽  
Integrated Process ◽  
Batch Production ◽  
Swarm Optimization ◽  
Planning And Scheduling ◽  
Factors Affecting

Integrated Process Planning and Scheduling (IPPS) problem is an important issue in production scheduling. Actually, there exit many factors affecting scheduling results. Many types of workpieces are commonly manufactured in batch production. Moreover, due to differences among process methods, all processes of a workpiece may not be performed in the same workshop or even in the same factory. For making IPPS problem more in line with practical manufacturing, this paper addresses an IPPS problem with batches and limited vehicles (BV-IPPS). An equal batch splitting strategy is adopted. A model for BV-IPPS problem is established. Makespan is the objective to be minimized. For solving the complex problem, a particle swarm optimization (PSO) with a multilayer encoding structure is proposed. Each module of the algorithm is designed. Finally, case studies have been conducted to validate the model and algorithm.

General Particle Swarm Optimization Algorithm for Integration of Process Planning and Scheduling

2010 ◽  
Vol 118-120 ◽  
pp. 409-413
Author(s):  
Shao Tan Xu ◽  
Xin Yu Li ◽  
Liang Gao ◽  
Yi Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Process Planning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Time Varying ◽  
Step Size ◽  
Swarm Optimization ◽  
Planning And Scheduling ◽  
System A

To realize the integration of process planning and scheduling (IPPS) in the manufacturing system, a particle swarm optimization (PSO) algorithm is utilized. Based on the general PSO (GPSO) model, one GPSO algorithm is projected to solve IPPS. In GPSO, crossover and mutation operations of genetic algorithm are respectively used for particles to exchange information and search randomly, and tabu search (TS) is used for particles’ local search. And time varying crossover probability and time varying maximum step size of tabu search are introduced. Experimental results show that IPPS can be solved by GPSO effectively. The feasibility of the proposed GPSO model and the significance of the research on IPPS are also demonstrated.

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