Application of modified multi-objective particle swarm optimisation algorithm for flexible process planning problem
Само за регистроване кориснике
2017
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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 typ...es 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.
Кључне речи:
simulated annealing / particle swarm optimisation / OR network / genetic algorithms / flexible process planning / ANDИзвор:
International Journal of Computer Integrated Manufacturing, 2017, 30, 2-3, 271-291Издавач:
- Taylor & Francis Ltd, Abingdon
Финансирање / пројекти:
- Иновативни приступ у примени интелигентних технолошких система за производњу делова од лима заснован на еколошким принципима (RS-MESTD-Technological Development (TD or TR)-35004)
DOI: 10.1080/0951192X.2016.1145804
ISSN: 0951-192X
WoS: 000390883300004
Scopus: 2-s2.0-84958543185
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Miljković, Zoran AU - Petrović, Milica PY - 2017 UR - https://machinery.mas.bg.ac.rs/handle/123456789/2625 AB - 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. PB - Taylor & Francis Ltd, Abingdon T2 - International Journal of Computer Integrated Manufacturing T1 - Application of modified multi-objective particle swarm optimisation algorithm for flexible process planning problem EP - 291 IS - 2-3 SP - 271 VL - 30 DO - 10.1080/0951192X.2016.1145804 ER -
@article{ author = "Miljković, Zoran and Petrović, Milica", year = "2017", 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.", publisher = "Taylor & Francis Ltd, Abingdon", journal = "International Journal of Computer Integrated Manufacturing", title = "Application of modified multi-objective particle swarm optimisation algorithm for flexible process planning problem", pages = "291-271", number = "2-3", volume = "30", doi = "10.1080/0951192X.2016.1145804" }
Miljković, Z.,& Petrović, M.. (2017). Application of modified multi-objective particle swarm optimisation algorithm for flexible process planning problem. in International Journal of Computer Integrated Manufacturing Taylor & Francis Ltd, Abingdon., 30(2-3), 271-291. https://doi.org/10.1080/0951192X.2016.1145804
Miljković Z, Petrović M. Application of modified multi-objective particle swarm optimisation algorithm for flexible process planning problem. in International Journal of Computer Integrated Manufacturing. 2017;30(2-3):271-291. doi:10.1080/0951192X.2016.1145804 .
Miljković, Zoran, Petrović, Milica, "Application of modified multi-objective particle swarm optimisation algorithm for flexible process planning problem" in International Journal of Computer Integrated Manufacturing, 30, no. 2-3 (2017):271-291, https://doi.org/10.1080/0951192X.2016.1145804 . .