A multi-objective memetic algorithm for integrated process planning and scheduling

Process planning and scheduling are two crucial components in a manufacturing system. The integration of the two functions has an important significance on improving the performance of the manufacturing system. However, integrated process planning and scheduling is an intractable non-deterministic p...

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Veröffentlicht in:International journal of advanced manufacturing technology Jg. 85; H. 5-8; S. 1513 - 1528
Hauptverfasser: Jin, Liangliang, Zhang, Chaoyong, Shao, Xinyu, Yang, Xudong, Tian, Guangdong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.07.2016
Springer Nature B.V
Schlagworte:
Algorithms
CAE) and Design
Computer-Aided Engineering (CAD
Engineering
Industrial and Production Engineering
Mathematical analysis
Mechanical Engineering
Media Management
Multiple objective analysis
Original Article
Polynomials
Process planning
Production scheduling
Schedules
Scheduling
Search methods
Workload
Workloads
Multi-objective optimization
Variable neighborhood search
Integrated process planning and scheduling (IPPS)
Multi-objective memetic algorithm
Intensification search
ISSN:0268-3768, 1433-3015
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Zusammenfassung:Process planning and scheduling are two crucial components in a manufacturing system. The integration of the two functions has an important significance on improving the performance of the manufacturing system. However, integrated process planning and scheduling is an intractable non-deterministic polynomial-time (NP)-hard problem, and the multiple objectives requirement widely exists in real-world production situations. In this paper, a multi-objective mathematical model of integrated process planning and scheduling is set up with three different objectives: the overall finishing time (makespan), the maximum machine workload (MMW), and the total workload of machines (TWM). A multi-objective memetic algorithm (MOMA) is proposed to solve this problem. In MOMA, all the possible schedules are improved by a problem-specific multi-objective local search method, which combines a variable neighborhood search (VNS) procedure and an effective objective-specific intensification search method. Moreover, we adopt the TOPSIS method to select a satisfactory schedule scheme from the optimal Pareto front. The proposed MOMA is tested on typical benchmark instances and the experimental results are compared with those obtained by the well-known NSGA-II. Computational results show that MOMA is a promising and very effective method for the multi-objective IPPS problem.
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ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-015-8037-7