Integration of Process Planning and Scheduling: An Approach Based on Ant Lion Optimisation Algorithm
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Аутори
Petrović, MilicaMiljković, Zoran
Остала ауторства
Kumar Phanden, RakeshJain, Ajai
Davim, J. Paulo
Поглавље у монографији (Објављена верзија)
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In traditional approaches, process planning and scheduling are usually carried out separately and sequentially. Since these manufacturing functions are complementary and interrelated, the development of effective models for integrated process planning and scheduling (IPPS) is essential for improving the performance of the whole manufacturing system. This paper presents a methodology based on the nature-inspired Ant Lion Optimization (ALO) algorithm for solving this NP-hard combinatorial optimization problem effectively. As the ALO algorithm mimics the intelligent behaviour of antlions during the hunting process, this paper provides a mathematical modelling of the main steps of hunting prey as well as the optimization procedure for the integration of process planning and scheduling functions. Optimal scheduling plans are obtained using three objective functions: (i) makespan, (ii) balanced level of machine utilization, and (iii) mean flow time. The ALO algorithm is implemented in the MA...TLAB® software package, experimentally tested on 25 benchmark problems through two experimental studies, and its results are compared with the results obtained by GA and PSO algorithms. The experimental results demonstrate the applicability of the proposed approach in solving IPPS problem.
Кључне речи:
Integrated process planning and scheduling (IPPS) / Optimal scheduling plans / Ant Lion Optimization (ALO) algorithm / NP-hard combinatorial optimization problemИзвор:
Integration of Process Planning and Scheduling: Approaches and Algorithms, 2019, 185/9-Издавач:
- CRC Press, Taylor & Francis Group
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)
Колекције
Институција/група
Mašinski fakultetTY - CHAP AU - Petrović, Milica AU - Miljković, Zoran PY - 2019 UR - https://machinery.mas.bg.ac.rs/handle/123456789/3959 AB - In traditional approaches, process planning and scheduling are usually carried out separately and sequentially. Since these manufacturing functions are complementary and interrelated, the development of effective models for integrated process planning and scheduling (IPPS) is essential for improving the performance of the whole manufacturing system. This paper presents a methodology based on the nature-inspired Ant Lion Optimization (ALO) algorithm for solving this NP-hard combinatorial optimization problem effectively. As the ALO algorithm mimics the intelligent behaviour of antlions during the hunting process, this paper provides a mathematical modelling of the main steps of hunting prey as well as the optimization procedure for the integration of process planning and scheduling functions. Optimal scheduling plans are obtained using three objective functions: (i) makespan, (ii) balanced level of machine utilization, and (iii) mean flow time. The ALO algorithm is implemented in the MATLAB® software package, experimentally tested on 25 benchmark problems through two experimental studies, and its results are compared with the results obtained by GA and PSO algorithms. The experimental results demonstrate the applicability of the proposed approach in solving IPPS problem. PB - CRC Press, Taylor & Francis Group T2 - Integration of Process Planning and Scheduling: Approaches and Algorithms T1 - Integration of Process Planning and Scheduling: An Approach Based on Ant Lion Optimisation Algorithm SP - 185/9 DO - 10.1201/9780429021305 ER -
@inbook{ author = "Petrović, Milica and Miljković, Zoran", year = "2019", abstract = "In traditional approaches, process planning and scheduling are usually carried out separately and sequentially. Since these manufacturing functions are complementary and interrelated, the development of effective models for integrated process planning and scheduling (IPPS) is essential for improving the performance of the whole manufacturing system. This paper presents a methodology based on the nature-inspired Ant Lion Optimization (ALO) algorithm for solving this NP-hard combinatorial optimization problem effectively. As the ALO algorithm mimics the intelligent behaviour of antlions during the hunting process, this paper provides a mathematical modelling of the main steps of hunting prey as well as the optimization procedure for the integration of process planning and scheduling functions. Optimal scheduling plans are obtained using three objective functions: (i) makespan, (ii) balanced level of machine utilization, and (iii) mean flow time. The ALO algorithm is implemented in the MATLAB® software package, experimentally tested on 25 benchmark problems through two experimental studies, and its results are compared with the results obtained by GA and PSO algorithms. The experimental results demonstrate the applicability of the proposed approach in solving IPPS problem.", publisher = "CRC Press, Taylor & Francis Group", journal = "Integration of Process Planning and Scheduling: Approaches and Algorithms", booktitle = "Integration of Process Planning and Scheduling: An Approach Based on Ant Lion Optimisation Algorithm", pages = "185/9", doi = "10.1201/9780429021305" }
Petrović, M.,& Miljković, Z.. (2019). Integration of Process Planning and Scheduling: An Approach Based on Ant Lion Optimisation Algorithm. in Integration of Process Planning and Scheduling: Approaches and Algorithms CRC Press, Taylor & Francis Group., 185/9. https://doi.org/10.1201/9780429021305
Petrović M, Miljković Z. Integration of Process Planning and Scheduling: An Approach Based on Ant Lion Optimisation Algorithm. in Integration of Process Planning and Scheduling: Approaches and Algorithms. 2019;:185/9. doi:10.1201/9780429021305 .
Petrović, Milica, Miljković, Zoran, "Integration of Process Planning and Scheduling: An Approach Based on Ant Lion Optimisation Algorithm" in Integration of Process Planning and Scheduling: Approaches and Algorithms (2019):185/9, https://doi.org/10.1201/9780429021305 . .