Biologically inspired optimization algorithms for flexible process planning
Само за регистроване кориснике
2017
Поглавље у монографији (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Flexible process planning belongs to one of the most essential functions of the modern manufacturing system. The aim of this function is to define detailed methods for manufacturing of a part in an economic and competitive manner starting from the initial phase (drawing of the target part) up to the final one (the desired shape of the target part). A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes flexible process planning problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. This paper presents a comparative analysis of biologically inspired optimization algorithms which are used to solve this problem. Four different optimization algorithms, namely genetic algorithms (GA), simulated annealing (SA), chaotic particle swarm optimization algorithm (cPSO), and ant lion optimization algorithm (ALO) are proposed and implemented in Matlab environment. Optimal process plans are ob...tained by multi-objective optimization of production time and production cost. The experimental verification is carried out by using real-world examples. The experimental results indicate that all aforementioned algorithms can be successfully used for optimization of flexible process plans, while the best performance shows ALO algorithm.
Кључне речи:
Simulated annealing / Genetic algorithms / Flexible process planning / Chaotic particle swarm optimization algorithm / Ant lion optimization algorithmИзвор:
Lecture Notes in Mechanical Engineering, 2017, 417-428Издавач:
- Springer Heidelberg
Финансирање / пројекти:
Колекције
Институција/група
Mašinski fakultetTY - CHAP AU - Petrović, Milica AU - Miljković, Zoran PY - 2017 UR - https://machinery.mas.bg.ac.rs/handle/123456789/2710 AB - Flexible process planning belongs to one of the most essential functions of the modern manufacturing system. The aim of this function is to define detailed methods for manufacturing of a part in an economic and competitive manner starting from the initial phase (drawing of the target part) up to the final one (the desired shape of the target part). A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes flexible process planning problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. This paper presents a comparative analysis of biologically inspired optimization algorithms which are used to solve this problem. Four different optimization algorithms, namely genetic algorithms (GA), simulated annealing (SA), chaotic particle swarm optimization algorithm (cPSO), and ant lion optimization algorithm (ALO) are proposed and implemented in Matlab environment. Optimal process plans are obtained by multi-objective optimization of production time and production cost. The experimental verification is carried out by using real-world examples. The experimental results indicate that all aforementioned algorithms can be successfully used for optimization of flexible process plans, while the best performance shows ALO algorithm. PB - Springer Heidelberg T2 - Lecture Notes in Mechanical Engineering T1 - Biologically inspired optimization algorithms for flexible process planning EP - 428 SP - 417 DO - 10.1007/978-3-319-56430-2_31 ER -
@inbook{ author = "Petrović, Milica and Miljković, Zoran", year = "2017", abstract = "Flexible process planning belongs to one of the most essential functions of the modern manufacturing system. The aim of this function is to define detailed methods for manufacturing of a part in an economic and competitive manner starting from the initial phase (drawing of the target part) up to the final one (the desired shape of the target part). A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes flexible process planning problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. This paper presents a comparative analysis of biologically inspired optimization algorithms which are used to solve this problem. Four different optimization algorithms, namely genetic algorithms (GA), simulated annealing (SA), chaotic particle swarm optimization algorithm (cPSO), and ant lion optimization algorithm (ALO) are proposed and implemented in Matlab environment. Optimal process plans are obtained by multi-objective optimization of production time and production cost. The experimental verification is carried out by using real-world examples. The experimental results indicate that all aforementioned algorithms can be successfully used for optimization of flexible process plans, while the best performance shows ALO algorithm.", publisher = "Springer Heidelberg", journal = "Lecture Notes in Mechanical Engineering", booktitle = "Biologically inspired optimization algorithms for flexible process planning", pages = "428-417", doi = "10.1007/978-3-319-56430-2_31" }
Petrović, M.,& Miljković, Z.. (2017). Biologically inspired optimization algorithms for flexible process planning. in Lecture Notes in Mechanical Engineering Springer Heidelberg., 417-428. https://doi.org/10.1007/978-3-319-56430-2_31
Petrović M, Miljković Z. Biologically inspired optimization algorithms for flexible process planning. in Lecture Notes in Mechanical Engineering. 2017;:417-428. doi:10.1007/978-3-319-56430-2_31 .
Petrović, Milica, Miljković, Zoran, "Biologically inspired optimization algorithms for flexible process planning" in Lecture Notes in Mechanical Engineering (2017):417-428, https://doi.org/10.1007/978-3-319-56430-2_31 . .