TY  - JOUR
AU  - Petrović, Milica
AU  - Jokić, Aleksandar
AU  - Miljković, Zoran
AU  - Kulesza, Zbigniew
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3946
AB  - During the last decades, intelligent mobile robots have been recognized as one of the most promising
and emerging solutions used for fulfilling material transport demands in intelligent manufacturing
systems. One of the most significant characteristics of those demands is their multi-objectivity, where
identified objectives might usually conflict. Therefore, obtaining the optimally scheduled robotic-
based material transport system that is simultaneously facing several conflicting objectives is a highly
challenging task. To address such a challenge, this paper proposes a novel multi-objective Grey Wolf
Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an
intelligent single mobile robot. The proposed optimization methodology includes the comprehensive
analysis and the mathematical formulation of 13 novel fitness functions combined to form a Pareto
front of the multi-objective optimization problem and a novel strategy for optimal exploration of multi-
objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational
Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and
compare the effectiveness of the proposed enhanced MOGWO algorithm with three state-of-the-
art metaheuristic methods (MOGA, MOAOA, and MOPSO) on 25 benchmark problems. The results
achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outper-
forms other algorithms in terms of convergence, coverage, and the robust optimal Pareto solution.
Finally, transportation paths based on obtained scheduling plans are experimentally corroborated
by the mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical
model of the intelligent manufacturing environment. The achieved experimental results successfully
demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of
material transport tasks based on a single mobile robotic system.
PB  - Elsevier
T2  - Applied Soft Computing
T1  - Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm
SP  - 109784
VL  - 131
DO  - 10.1016/j.asoc.2022.109784
ER  - 

@article{
author = "Petrović, Milica and Jokić, Aleksandar and Miljković, Zoran and Kulesza, Zbigniew",
year = "2022",
abstract = "During the last decades, intelligent mobile robots have been recognized as one of the most promising
and emerging solutions used for fulfilling material transport demands in intelligent manufacturing
systems. One of the most significant characteristics of those demands is their multi-objectivity, where
identified objectives might usually conflict. Therefore, obtaining the optimally scheduled robotic-
based material transport system that is simultaneously facing several conflicting objectives is a highly
challenging task. To address such a challenge, this paper proposes a novel multi-objective Grey Wolf
Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an
intelligent single mobile robot. The proposed optimization methodology includes the comprehensive
analysis and the mathematical formulation of 13 novel fitness functions combined to form a Pareto
front of the multi-objective optimization problem and a novel strategy for optimal exploration of multi-
objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational
Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and
compare the effectiveness of the proposed enhanced MOGWO algorithm with three state-of-the-
art metaheuristic methods (MOGA, MOAOA, and MOPSO) on 25 benchmark problems. The results
achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outper-
forms other algorithms in terms of convergence, coverage, and the robust optimal Pareto solution.
Finally, transportation paths based on obtained scheduling plans are experimentally corroborated
by the mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical
model of the intelligent manufacturing environment. The achieved experimental results successfully
demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of
material transport tasks based on a single mobile robotic system.",
publisher = "Elsevier",
journal = "Applied Soft Computing",
title = "Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm",
pages = "109784",
volume = "131",
doi = "10.1016/j.asoc.2022.109784"
}

Petrović, M., Jokić, A., Miljković, Z.,& Kulesza, Z.. (2022). Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm. in Applied Soft Computing
Elsevier., 131, 109784.
https://doi.org/10.1016/j.asoc.2022.109784

Petrović M, Jokić A, Miljković Z, Kulesza Z. Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm. in Applied Soft Computing. 2022;131:109784.
doi:10.1016/j.asoc.2022.109784 .

Petrović, Milica, Jokić, Aleksandar, Miljković, Zoran, Kulesza, Zbigniew, "Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm" in Applied Soft Computing, 131 (2022):109784,
https://doi.org/10.1016/j.asoc.2022.109784 . .