Abstract
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.
Highlights
Process planning and production scheduling are two indispensable subsystems in manufacturing systems
The makespan is taken as the objective to be minimized, and a mathematical model for the BV-Integrated Process Planning and Scheduling (IPPS) problem is established
The results show that the makespan obtained
Summary
Li Ba , Yan Li , Mingshun Yang , Xueliang Wu , Yong Liu , Xinqin Gao , and Zhihong Miao. Integrated Process Planning and Scheduling (IPPS) problem is an important issue in production scheduling. Many types of workpieces are commonly manufactured in batch production. 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. For solving the complex problem, a particle swarm optimization (PSO) with a multilayer encoding structure is proposed. Each module of the algorithm is designed. Case studies have been conducted to validate the model and algorithm
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