Application of modified multi-objective particle swarm optimisation algorithm for flexible process planning problem

Process planning belongs to one of the most essential functions of the modern manufacturing system. Moreover, flexible process planning implies the ability of a system to adapt to changing requirements and thereby provide alternative ways of performing manufacturing operations on a part. Variety of...

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Published in:International journal of computer integrated manufacturing Vol. 30; no. 2-3; pp. 271 - 291
Main Authors: Miljković, Zoran, Petrović, Milica
Format: Journal Article
Language:English
Published: Taylor & Francis 04.03.2017
Subjects:
AND/OR network
flexible process planning
genetic algorithms
particle swarm optimisation
simulated annealing
ISSN:0951-192X, 1362-3052
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Abstract Process planning belongs to one of the most essential functions of the modern manufacturing system. Moreover, flexible process planning implies the ability of a system to adapt to changing requirements and thereby provide alternative ways of performing manufacturing operations on a part. Variety of manufacturing resources including variety of alternative machines, alternative tools, as well as tool access direction (TAD) leads to the fact that most of the parts in modern manufacturing systems have various flexible process plans. Therefore, obtaining optimal process plan from all available alternatives has become a very important task in the domain of flexible process planning research. In this article, a method based on modified particle swarm optimisation (mPSO) has been developed to solve this nondeterministic polynomial-hard combinatorial optimisation problem, and the following issues have been addressed: (i) the AND/OR network representation has been adopted to describe various types of flexibility, i.e. machine flexibility, tool flexibility, TAD flexibility, process flexibility and sequence flexibility; (ii) the particle encoding/decoding scheme has been proposed and traditional PSO algorithm has been modified with crossover, mutation and shift operator and (iii) optimal operation sequence has been found by performing multi-objective optimisation procedure concerning minimisation of the production time and production cost. In order to verify the performance of the proposed mPSO algorithm, five independent experiments have been carried out and comparisons with other meta-heuristic algorithms have been made. The experimental results show that the proposed algorithm has achieved satisfactory improvement in terms of efficiency and effectiveness.
AbstractList Process planning belongs to one of the most essential functions of the modern manufacturing system. Moreover, flexible process planning implies the ability of a system to adapt to changing requirements and thereby provide alternative ways of performing manufacturing operations on a part. Variety of manufacturing resources including variety of alternative machines, alternative tools, as well as tool access direction (TAD) leads to the fact that most of the parts in modern manufacturing systems have various flexible process plans. Therefore, obtaining optimal process plan from all available alternatives has become a very important task in the domain of flexible process planning research. In this article, a method based on modified particle swarm optimisation (mPSO) has been developed to solve this nondeterministic polynomial-hard combinatorial optimisation problem, and the following issues have been addressed: (i) the AND/OR network representation has been adopted to describe various types of flexibility, i.e. machine flexibility, tool flexibility, TAD flexibility, process flexibility and sequence flexibility; (ii) the particle encoding/decoding scheme has been proposed and traditional PSO algorithm has been modified with crossover, mutation and shift operator and (iii) optimal operation sequence has been found by performing multi-objective optimisation procedure concerning minimisation of the production time and production cost. In order to verify the performance of the proposed mPSO algorithm, five independent experiments have been carried out and comparisons with other meta-heuristic algorithms have been made. The experimental results show that the proposed algorithm has achieved satisfactory improvement in terms of efficiency and effectiveness.
Author Miljković, Zoran
Petrović, Milica
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  surname: Petrović
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  organization: Faculty of Mechanical Engineering, Production Engineering Department, University of Belgrade
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SubjectTerms AND/OR network
flexible process planning
genetic algorithms
particle swarm optimisation
simulated annealing
Title Application of modified multi-objective particle swarm optimisation algorithm for flexible process planning problem
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