A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm
Само за регистроване кориснике
2019
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One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired Whale Optimization Algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level o...f robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm Optimization algorithm (PSO and cPSO), and hybrid GA-SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms.
Кључне речи:
Whale optimization algorithm / Scheduling / Mobile robot / Intelligent manufacturing systems / Biologically inspired algorithmsИзвор:
Applied Soft Computing, 2019, 81, 105520-Издавач:
- Elsevier, Amsterdam
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.asoc.2019.105520
ISSN: 1568-4946
WoS: 000476471800013
Scopus: 2-s2.0-85066235463
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Petrović, Milica AU - Miljković, Zoran AU - Jokić, Aleksandar PY - 2019 UR - https://machinery.mas.bg.ac.rs/handle/123456789/3015 AB - One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired Whale Optimization Algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level of robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm Optimization algorithm (PSO and cPSO), and hybrid GA-SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms. PB - Elsevier, Amsterdam T2 - Applied Soft Computing T1 - A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm SP - 105520 VL - 81 DO - 10.1016/j.asoc.2019.105520 ER -
@article{ author = "Petrović, Milica and Miljković, Zoran and Jokić, Aleksandar", year = "2019", abstract = "One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired Whale Optimization Algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level of robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm Optimization algorithm (PSO and cPSO), and hybrid GA-SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms.", publisher = "Elsevier, Amsterdam", journal = "Applied Soft Computing", title = "A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm", pages = "105520", volume = "81", doi = "10.1016/j.asoc.2019.105520" }
Petrović, M., Miljković, Z.,& Jokić, A.. (2019). A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm. in Applied Soft Computing Elsevier, Amsterdam., 81, 105520. https://doi.org/10.1016/j.asoc.2019.105520
Petrović M, Miljković Z, Jokić A. A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm. in Applied Soft Computing. 2019;81:105520. doi:10.1016/j.asoc.2019.105520 .
Petrović, Milica, Miljković, Zoran, Jokić, Aleksandar, "A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm" in Applied Soft Computing, 81 (2019):105520, https://doi.org/10.1016/j.asoc.2019.105520 . .