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 . .

TY  - CONF
AU  - Petrović, Milica
AU  - Jokić, Aleksandar
AU  - Miljković, Zoran
AU  - Kulesza, Zbigniew
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3968
AB  - The contemporary manufacturing systems face a challenging and uncertain future due to frequent customer demands for customized products. A promising direction that can enable manufacturing systems to fulfill the market requirements is the adaptation of a reconfigurable manufacturing system paradigm. Physical reconfigurability can be achieved by developing systems that can satisfy conflicting production priorities such as minimal production time and maximal profit. Having that in mind, in this paper, the authors present a comprehensive analysis of population-based multi-objective optimization algorithms utilized for scheduling manufacturing entities. The output of multi-objective optimization is a set of Pareto optimal solutions in the form of production scheduling plans with transportation constraints. Three state-of-the-art population-based algorithms i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), are employed for optimization, while the experimental results show the effectiveness and superiority of the WOA algorithm.
C3  - Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022)
T1  - Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities
SP  - 403-407
DO  - 10.1109/MMAR55195.2022.9874301
ER  - 

@conference{
author = "Petrović, Milica and Jokić, Aleksandar and Miljković, Zoran and Kulesza, Zbigniew",
year = "2022",
abstract = "The contemporary manufacturing systems face a challenging and uncertain future due to frequent customer demands for customized products. A promising direction that can enable manufacturing systems to fulfill the market requirements is the adaptation of a reconfigurable manufacturing system paradigm. Physical reconfigurability can be achieved by developing systems that can satisfy conflicting production priorities such as minimal production time and maximal profit. Having that in mind, in this paper, the authors present a comprehensive analysis of population-based multi-objective optimization algorithms utilized for scheduling manufacturing entities. The output of multi-objective optimization is a set of Pareto optimal solutions in the form of production scheduling plans with transportation constraints. Three state-of-the-art population-based algorithms i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), are employed for optimization, while the experimental results show the effectiveness and superiority of the WOA algorithm.",
journal = "Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022)",
title = "Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities",
pages = "403-407",
doi = "10.1109/MMAR55195.2022.9874301"
}

Petrović, M., Jokić, A., Miljković, Z.,& Kulesza, Z.. (2022). Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities. in Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022), 403-407.
https://doi.org/10.1109/MMAR55195.2022.9874301

Petrović M, Jokić A, Miljković Z, Kulesza Z. Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities. in Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022). 2022;:403-407.
doi:10.1109/MMAR55195.2022.9874301 .

Petrović, Milica, Jokić, Aleksandar, Miljković, Zoran, Kulesza, Zbigniew, "Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities" in Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022) (2022):403-407,
https://doi.org/10.1109/MMAR55195.2022.9874301 . .

TY  - JOUR
AU  - Pawlowski, Adam
AU  - Romaniuk, Slawomir
AU  - Kulesza, Zbigniew
AU  - Petrović, Milica
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4920
AB  - Nowadays, most robotic systems perform their tasks in an environment that is generally known. Thus, robot’s trajectory can be planned in advance depending on a given task. However, as a part of modern manufacturing systems which are faced with the requirements to produce high product variety, mobile robots should be flexible to adapt to changing and diverse environments and needs. In such scenarios, a modification of the task or a change in the environment, forces the operator to modify robot’s trajectory. Such modification is usually expensive and time-consuming, as experienced engineers must be involved to program robot’s movements. The current paper presents a solution to this problem by simplifying the process of teaching the robot a new trajectory. The proposed method generates a trajectory based on an initial raw demonstration of its shape. The new trajectory is generated in such a way that the errors between the actual and target end positions and orientations of the robot are minimized. To minimize those errors, the grey wolf optimization (GWO) algorithm is applied. The proposed approach is demonstrated for a two-wheeled mobile robot. Simulation and experimental results confirm high accuracy of generated trajectories.
PB  - Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU)
T2  - IAES International Journal of Robotics and Automation (IJRA)
T1  - Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm
EP  - 277
IS  - 4
SP  - 263
VL  - 11
DO  - 10.11591/ijra.v11i4.pp263-277
ER  - 

@article{
author = "Pawlowski, Adam and Romaniuk, Slawomir and Kulesza, Zbigniew and Petrović, Milica",
year = "2022",
abstract = "Nowadays, most robotic systems perform their tasks in an environment that is generally known. Thus, robot’s trajectory can be planned in advance depending on a given task. However, as a part of modern manufacturing systems which are faced with the requirements to produce high product variety, mobile robots should be flexible to adapt to changing and diverse environments and needs. In such scenarios, a modification of the task or a change in the environment, forces the operator to modify robot’s trajectory. Such modification is usually expensive and time-consuming, as experienced engineers must be involved to program robot’s movements. The current paper presents a solution to this problem by simplifying the process of teaching the robot a new trajectory. The proposed method generates a trajectory based on an initial raw demonstration of its shape. The new trajectory is generated in such a way that the errors between the actual and target end positions and orientations of the robot are minimized. To minimize those errors, the grey wolf optimization (GWO) algorithm is applied. The proposed approach is demonstrated for a two-wheeled mobile robot. Simulation and experimental results confirm high accuracy of generated trajectories.",
publisher = "Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU)",
journal = "IAES International Journal of Robotics and Automation (IJRA)",
title = "Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm",
pages = "277-263",
number = "4",
volume = "11",
doi = "10.11591/ijra.v11i4.pp263-277"
}

Pawlowski, A., Romaniuk, S., Kulesza, Z.,& Petrović, M.. (2022). Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm. in IAES International Journal of Robotics and Automation (IJRA)
Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU)., 11(4), 263-277.
https://doi.org/10.11591/ijra.v11i4.pp263-277

Pawlowski A, Romaniuk S, Kulesza Z, Petrović M. Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm. in IAES International Journal of Robotics and Automation (IJRA). 2022;11(4):263-277.
doi:10.11591/ijra.v11i4.pp263-277 .

Pawlowski, Adam, Romaniuk, Slawomir, Kulesza, Zbigniew, Petrović, Milica, "Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm" in IAES International Journal of Robotics and Automation (IJRA), 11, no. 4 (2022):263-277,
https://doi.org/10.11591/ijra.v11i4.pp263-277 . .