Integration of process planning and scheduling using chaotic particle swarm optimization algorithm
Само за регистроване кориснике
2016
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are present...ed in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.
Кључне речи:
Scheduling / Process planning / Particle swarm optimization / Mobile robot / Integrated process planning and scheduling / Chaos theoryИзвор:
Expert Systems With Applications, 2016, 64, 569-588Издавач:
- Pergamon-Elsevier Science Ltd, Oxford
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: 10.1016/j.eswa.2016.08.019
ISSN: 0957-4174
WoS: 000383810800046
Scopus: 2-s2.0-84981285542
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Petrović, Milica AU - Vuković, Najdan AU - Mitić, Marko AU - Miljković, Zoran PY - 2016 UR - https://machinery.mas.bg.ac.rs/handle/123456789/2462 AB - Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment. PB - Pergamon-Elsevier Science Ltd, Oxford T2 - Expert Systems With Applications T1 - Integration of process planning and scheduling using chaotic particle swarm optimization algorithm EP - 588 SP - 569 VL - 64 DO - 10.1016/j.eswa.2016.08.019 ER -
@article{ author = "Petrović, Milica and Vuković, Najdan and Mitić, Marko and Miljković, Zoran", year = "2016", abstract = "Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.", publisher = "Pergamon-Elsevier Science Ltd, Oxford", journal = "Expert Systems With Applications", title = "Integration of process planning and scheduling using chaotic particle swarm optimization algorithm", pages = "588-569", volume = "64", doi = "10.1016/j.eswa.2016.08.019" }
Petrović, M., Vuković, N., Mitić, M.,& Miljković, Z.. (2016). Integration of process planning and scheduling using chaotic particle swarm optimization algorithm. in Expert Systems With Applications Pergamon-Elsevier Science Ltd, Oxford., 64, 569-588. https://doi.org/10.1016/j.eswa.2016.08.019
Petrović M, Vuković N, Mitić M, Miljković Z. Integration of process planning and scheduling using chaotic particle swarm optimization algorithm. in Expert Systems With Applications. 2016;64:569-588. doi:10.1016/j.eswa.2016.08.019 .
Petrović, Milica, Vuković, Najdan, Mitić, Marko, Miljković, Zoran, "Integration of process planning and scheduling using chaotic particle swarm optimization algorithm" in Expert Systems With Applications, 64 (2016):569-588, https://doi.org/10.1016/j.eswa.2016.08.019 . .