TY  - CONF
AU  - Petronijević, Jelena
AU  - Petrović, Milica
AU  - Vuković, Najdan
AU  - Mitić, Marko
AU  - Babić, Bojan
AU  - Miljković, Zoran
PY  - 2016
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4561
AB  - Projektovanje tehnoloških procesa predstavlja određivanje postupka proizvodnje uz zadovoljenje prethodno definisanih ciljeva i ograničenja. Terminiranjem proizvodnje se na osnovu proizvodnog plana i prethodno određenih tehnoloških postupaka dodeljuju optimalni proizvodni resursi za odgovarajući vremenski period. Uvođenjem koncepta masovne kastomizacije, već ranije ključne funkcije, projektovanje i terminiranje proizvodnje, sada imaju krucijalnu ulogu u tehnološkom sistemu zbog sve većih zahteva koje se pred ove funkcije postavljaju. Rad se bavi uvođenjem koncepta multiagentnih i holon tehnoloških sistema uz pregled stanja u oblasti projektovanja tehnoloških procesa i terminiranja proizvodnje. Radom je obuhvaćen tradicionalni, sledstveni, pristup projektovanju i terminiranju, ali i integrisan prilaz problematici.
AB  - Process planning can be defined as determination of manufacturing processes by achieving its goals and constraints. Scheduling process assigns optimal manufacturing resources over time based on production plan and previously determined process plans. With the mass customization concept, previously key functions in the production, process planning and scheduling, now become crucial for satisfaction of more demanding requirements. The paper introduces the concepts of multi-agent and holonic manufacturing systems and presents state of the process planning and scheduling area of research. It gives an overview on both, sequential and integrated, process planning and scheduling.
PB  - JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet
C3  - 40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016
T1  - Мултиагентни и холон технолошки системи у пројектовању технолошких процеса и терминирању производње
T1  - Multi-agent and Holonic Manufacturing Systems for Process Plannong and Scheduling
EP  - 3.68
SP  - 3.63
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4561
ER  - 

@conference{
author = "Petronijević, Jelena and Petrović, Milica and Vuković, Najdan and Mitić, Marko and Babić, Bojan and Miljković, Zoran",
year = "2016",
abstract = "Projektovanje tehnoloških procesa predstavlja određivanje postupka proizvodnje uz zadovoljenje prethodno definisanih ciljeva i ograničenja. Terminiranjem proizvodnje se na osnovu proizvodnog plana i prethodno određenih tehnoloških postupaka dodeljuju optimalni proizvodni resursi za odgovarajući vremenski period. Uvođenjem koncepta masovne kastomizacije, već ranije ključne funkcije, projektovanje i terminiranje proizvodnje, sada imaju krucijalnu ulogu u tehnološkom sistemu zbog sve većih zahteva koje se pred ove funkcije postavljaju. Rad se bavi uvođenjem koncepta multiagentnih i holon tehnoloških sistema uz pregled stanja u oblasti projektovanja tehnoloških procesa i terminiranja proizvodnje. Radom je obuhvaćen tradicionalni, sledstveni, pristup projektovanju i terminiranju, ali i integrisan prilaz problematici., Process planning can be defined as determination of manufacturing processes by achieving its goals and constraints. Scheduling process assigns optimal manufacturing resources over time based on production plan and previously determined process plans. With the mass customization concept, previously key functions in the production, process planning and scheduling, now become crucial for satisfaction of more demanding requirements. The paper introduces the concepts of multi-agent and holonic manufacturing systems and presents state of the process planning and scheduling area of research. It gives an overview on both, sequential and integrated, process planning and scheduling.",
publisher = "JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet",
journal = "40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016",
title = "Мултиагентни и холон технолошки системи у пројектовању технолошких процеса и терминирању производње, Multi-agent and Holonic Manufacturing Systems for Process Plannong and Scheduling",
pages = "3.68-3.63",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4561"
}

Petronijević, J., Petrović, M., Vuković, N., Mitić, M., Babić, B.,& Miljković, Z.. (2016). Мултиагентни и холон технолошки системи у пројектовању технолошких процеса и терминирању производње. in 40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016
JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet., 3.63-3.68.
https://hdl.handle.net/21.15107/rcub_machinery_4561

Petronijević J, Petrović M, Vuković N, Mitić M, Babić B, Miljković Z. Мултиагентни и холон технолошки системи у пројектовању технолошких процеса и терминирању производње. in 40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016. 2016;:3.63-3.68.
https://hdl.handle.net/21.15107/rcub_machinery_4561 .

Petronijević, Jelena, Petrović, Milica, Vuković, Najdan, Mitić, Marko, Babić, Bojan, Miljković, Zoran, "Мултиагентни и холон технолошки системи у пројектовању технолошких процеса и терминирању производње" in 40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016 (2016):3.63-3.68,
https://hdl.handle.net/21.15107/rcub_machinery_4561 .

TY  - CONF
AU  - Petrović, Milica
AU  - Petronijević, Jelena
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Miljković, Zoran
AU  - Babić, Bojan
PY  - 2016
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4560
AB  - U radu je prikazan pristup za integrisano projektovanje i terminiranje fleksibilnih teholoških procesa obrade delova primenom algoritma baziranog na inteligenciji roja čestica i teoriji haosa (cPSO algoritam). Pored metoda kodiranja/dekodiranja parametara planova terminiranja u jedinke cPSO algoritma, u radu je predložen matematički model za minimizaciju ukupnog vremena za obradu svih delova čije se terminiranje vrši, maksimizaciju uravnoteženog iskorišćenja mašina alatki i minimizaciju transportnih tokova materijala. Takođe, u cilju prevazilaženja nedostataka vezanih za brzu konvergenciju algoritma u ranim fazama optimizacije, predložena je implementacija haotičnih mapa u PSO algoritam. Predloženi pristup je eksperimentalno verifikovan na primeru dobijanja optimalnih planova terminiranja realnih delova.
AB  - This paper presents an approach for integration of process planning and scheduling based on the particle swarm optimization algorithm and chaos theory (cPSO). Besides scheduling plans representation and particle encoding/decoding scheme, mathematical model for the minimization of makespan, maximization of balanced level of machine utilization and minimization of mean flow time was presented. Also, we proposed implementation of chaotic maps in PSO algorithm in order to prevent algorithm from converging prematurely. Experimental verification of the proposed algorithm was done through the optimal scheduling of real parts.
PB  - JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet
C3  - 40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016
T1  - Интелигенција роја честица и теорија хаоса у интегрисаном пројектовању и терминирању флексибилних технолошких процеса
T1  - Particle Swarm Optimization Algorithm and Chaos Theory for Integration of Process Planning and Scheduling
EP  - 3.32
SP  - 3.22
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4560
ER  - 

@conference{
author = "Petrović, Milica and Petronijević, Jelena and Mitić, Marko and Vuković, Najdan and Miljković, Zoran and Babić, Bojan",
year = "2016",
abstract = "U radu je prikazan pristup za integrisano projektovanje i terminiranje fleksibilnih teholoških procesa obrade delova primenom algoritma baziranog na inteligenciji roja čestica i teoriji haosa (cPSO algoritam). Pored metoda kodiranja/dekodiranja parametara planova terminiranja u jedinke cPSO algoritma, u radu je predložen matematički model za minimizaciju ukupnog vremena za obradu svih delova čije se terminiranje vrši, maksimizaciju uravnoteženog iskorišćenja mašina alatki i minimizaciju transportnih tokova materijala. Takođe, u cilju prevazilaženja nedostataka vezanih za brzu konvergenciju algoritma u ranim fazama optimizacije, predložena je implementacija haotičnih mapa u PSO algoritam. Predloženi pristup je eksperimentalno verifikovan na primeru dobijanja optimalnih planova terminiranja realnih delova., This paper presents an approach for integration of process planning and scheduling based on the particle swarm optimization algorithm and chaos theory (cPSO). Besides scheduling plans representation and particle encoding/decoding scheme, mathematical model for the minimization of makespan, maximization of balanced level of machine utilization and minimization of mean flow time was presented. Also, we proposed implementation of chaotic maps in PSO algorithm in order to prevent algorithm from converging prematurely. Experimental verification of the proposed algorithm was done through the optimal scheduling of real parts.",
publisher = "JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet",
journal = "40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016",
title = "Интелигенција роја честица и теорија хаоса у интегрисаном пројектовању и терминирању флексибилних технолошких процеса, Particle Swarm Optimization Algorithm and Chaos Theory for Integration of Process Planning and Scheduling",
pages = "3.32-3.22",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4560"
}

Petrović, M., Petronijević, J., Mitić, M., Vuković, N., Miljković, Z.,& Babić, B.. (2016). Интелигенција роја честица и теорија хаоса у интегрисаном пројектовању и терминирању флексибилних технолошких процеса. in 40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016
JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet., 3.22-3.32.
https://hdl.handle.net/21.15107/rcub_machinery_4560

Petrović M, Petronijević J, Mitić M, Vuković N, Miljković Z, Babić B. Интелигенција роја честица и теорија хаоса у интегрисаном пројектовању и терминирању флексибилних технолошких процеса. in 40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016. 2016;:3.22-3.32.
https://hdl.handle.net/21.15107/rcub_machinery_4560 .

Petrović, Milica, Petronijević, Jelena, Mitić, Marko, Vuković, Najdan, Miljković, Zoran, Babić, Bojan, "Интелигенција роја честица и теорија хаоса у интегрисаном пројектовању и терминирању флексибилних технолошких процеса" in 40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016 (2016):3.22-3.32,
https://hdl.handle.net/21.15107/rcub_machinery_4560 .

TY  - JOUR
AU  - Petrović, Milica
AU  - Petronijević, Jelena
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Miljković, Zoran
AU  - Babić, Bojan
PY  - 2016
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3964
AB  - Process planning and scheduling are two of the most important manufacturing functions which are usually performed sequentially in traditional approaches. Considering the fact that these functions are usually complementary, it is necessary to integrate them so as to improve performance of a manufacturing system. This paper presents implementation of novel nature-inspired Ant Lion Optimization (ALO) algorithm for solving this combinatorial optimization problem effectively. As the ALO algorithm mimics the intelligent behavior of antlions in hunting ants, the main steps of hunting prey, its mathematical modeling, and optimization procedure for integration of process planning and scheduling is proposed. The algorithm is implemented in Matlab environment and run on the 3.10 GHz processor with 2 GBs of RAM memory. Experimental results show applicability of the proposed approach in solving integrated process planning and scheduling problem.
T2  - Applied Mechanics and Materials
T1  - The Ant Lion Optimization Algorithm for Integrated Process Planning and Scheduling
SP  - 187-192
VL  - 834
DO  - 10.4028/www.scientific.net/AMM.834.187
ER  - 

@article{
author = "Petrović, Milica and Petronijević, Jelena and Mitić, Marko and Vuković, Najdan and Miljković, Zoran and Babić, Bojan",
year = "2016",
abstract = "Process planning and scheduling are two of the most important manufacturing functions which are usually performed sequentially in traditional approaches. Considering the fact that these functions are usually complementary, it is necessary to integrate them so as to improve performance of a manufacturing system. This paper presents implementation of novel nature-inspired Ant Lion Optimization (ALO) algorithm for solving this combinatorial optimization problem effectively. As the ALO algorithm mimics the intelligent behavior of antlions in hunting ants, the main steps of hunting prey, its mathematical modeling, and optimization procedure for integration of process planning and scheduling is proposed. The algorithm is implemented in Matlab environment and run on the 3.10 GHz processor with 2 GBs of RAM memory. Experimental results show applicability of the proposed approach in solving integrated process planning and scheduling problem.",
journal = "Applied Mechanics and Materials",
title = "The Ant Lion Optimization Algorithm for Integrated Process Planning and Scheduling",
pages = "187-192",
volume = "834",
doi = "10.4028/www.scientific.net/AMM.834.187"
}

Petrović, M., Petronijević, J., Mitić, M., Vuković, N., Miljković, Z.,& Babić, B.. (2016). The Ant Lion Optimization Algorithm for Integrated Process Planning and Scheduling. in Applied Mechanics and Materials, 834, 187-192.
https://doi.org/10.4028/www.scientific.net/AMM.834.187

Petrović M, Petronijević J, Mitić M, Vuković N, Miljković Z, Babić B. The Ant Lion Optimization Algorithm for Integrated Process Planning and Scheduling. in Applied Mechanics and Materials. 2016;834:187-192.
doi:10.4028/www.scientific.net/AMM.834.187 .

Petrović, Milica, Petronijević, Jelena, Mitić, Marko, Vuković, Najdan, Miljković, Zoran, Babić, Bojan, "The Ant Lion Optimization Algorithm for Integrated Process Planning and Scheduling" in Applied Mechanics and Materials, 834 (2016):187-192,
https://doi.org/10.4028/www.scientific.net/AMM.834.187 . .

TY  - CONF
AU  - Petronijević, Jelena
AU  - Petrović, Milica
AU  - Vuković, Najdan
AU  - Mitić, Marko
AU  - Babić, Bojan
AU  - Miljković, Zoran
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4623
AB  - Multi-agent systems have been used for modelling various problems in the social, biological and technical domain. When comes to technical systems, especially manufacturing systems, agents are most often applied in optimization and scheduling problems. Traditionally, scheduling is done after creation of process plans. In this paper, agent methodology is used for integration of these two functions. The proposed multi-agent architecture provides simultaneous performance of process planning and scheduling and it consists of four intelligent agents: part and job agents, machine agent, and optimization agent. Verification and feasibility of a proposed approach is
conducted using agent based simulation in AnyLogic software.
PB  - Novi Sad : Faculty of Technical Sciences
C3  - Proceedings of the 12th International Scientific Conference MMA 2015 – Flexible Technologies, Novi Sad, 25-26 September 2015
T1  - Multi-Agent Modeling for Integrated Process Planning and Scheduling
EP  - 124
SP  - 121
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4623
ER  - 

@conference{
author = "Petronijević, Jelena and Petrović, Milica and Vuković, Najdan and Mitić, Marko and Babić, Bojan and Miljković, Zoran",
year = "2015",
abstract = "Multi-agent systems have been used for modelling various problems in the social, biological and technical domain. When comes to technical systems, especially manufacturing systems, agents are most often applied in optimization and scheduling problems. Traditionally, scheduling is done after creation of process plans. In this paper, agent methodology is used for integration of these two functions. The proposed multi-agent architecture provides simultaneous performance of process planning and scheduling and it consists of four intelligent agents: part and job agents, machine agent, and optimization agent. Verification and feasibility of a proposed approach is
conducted using agent based simulation in AnyLogic software.",
publisher = "Novi Sad : Faculty of Technical Sciences",
journal = "Proceedings of the 12th International Scientific Conference MMA 2015 – Flexible Technologies, Novi Sad, 25-26 September 2015",
title = "Multi-Agent Modeling for Integrated Process Planning and Scheduling",
pages = "124-121",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4623"
}

Petronijević, J., Petrović, M., Vuković, N., Mitić, M., Babić, B.,& Miljković, Z.. (2015). Multi-Agent Modeling for Integrated Process Planning and Scheduling. in Proceedings of the 12th International Scientific Conference MMA 2015 – Flexible Technologies, Novi Sad, 25-26 September 2015
Novi Sad : Faculty of Technical Sciences., 121-124.
https://hdl.handle.net/21.15107/rcub_machinery_4623

Petronijević J, Petrović M, Vuković N, Mitić M, Babić B, Miljković Z. Multi-Agent Modeling for Integrated Process Planning and Scheduling. in Proceedings of the 12th International Scientific Conference MMA 2015 – Flexible Technologies, Novi Sad, 25-26 September 2015. 2015;:121-124.
https://hdl.handle.net/21.15107/rcub_machinery_4623 .

Petronijević, Jelena, Petrović, Milica, Vuković, Najdan, Mitić, Marko, Babić, Bojan, Miljković, Zoran, "Multi-Agent Modeling for Integrated Process Planning and Scheduling" in Proceedings of the 12th International Scientific Conference MMA 2015 – Flexible Technologies, Novi Sad, 25-26 September 2015 (2015):121-124,
https://hdl.handle.net/21.15107/rcub_machinery_4623 .

TY  - CONF
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Petrović, Milica
AU  - Petronijević, Jelena
AU  - Diryag, Ali
AU  - Miljković, Zoran
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4469
AB  - This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions.
PB  - Society for Information Systems and Computer Networks
C3  - Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015
T1  - Bioinspired metaheuristic algorithms for global optimization
EP  - 42
SP  - 38
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4469
ER  - 

@conference{
author = "Mitić, Marko and Vuković, Najdan and Petrović, Milica and Petronijević, Jelena and Diryag, Ali and Miljković, Zoran",
year = "2015",
abstract = "This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions.",
publisher = "Society for Information Systems and Computer Networks",
journal = "Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015",
title = "Bioinspired metaheuristic algorithms for global optimization",
pages = "42-38",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4469"
}

Mitić, M., Vuković, N., Petrović, M., Petronijević, J., Diryag, A.,& Miljković, Z.. (2015). Bioinspired metaheuristic algorithms for global optimization. in Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015
Society for Information Systems and Computer Networks., 38-42.
https://hdl.handle.net/21.15107/rcub_machinery_4469

Mitić M, Vuković N, Petrović M, Petronijević J, Diryag A, Miljković Z. Bioinspired metaheuristic algorithms for global optimization. in Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015. 2015;:38-42.
https://hdl.handle.net/21.15107/rcub_machinery_4469 .

Mitić, Marko, Vuković, Najdan, Petrović, Milica, Petronijević, Jelena, Diryag, Ali, Miljković, Zoran, "Bioinspired metaheuristic algorithms for global optimization" in Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015 (2015):38-42,
https://hdl.handle.net/21.15107/rcub_machinery_4469 .

TY  - CONF
AU  - Vuković, Najdan
AU  - Mitić, Marko
AU  - Petrović, Milica
AU  - Petronijević, Jelena
AU  - Miljković, Zoran
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4468
AB  - In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF.
PB  - Society for Information Systems and Computer Networks
C3  - Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015
T1  - Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter
EP  - 94
SP  - 89
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4468
ER  - 

@conference{
author = "Vuković, Najdan and Mitić, Marko and Petrović, Milica and Petronijević, Jelena and Miljković, Zoran",
year = "2015",
abstract = "In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF.",
publisher = "Society for Information Systems and Computer Networks",
journal = "Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015",
title = "Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter",
pages = "94-89",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4468"
}

Vuković, N., Mitić, M., Petrović, M., Petronijević, J.,& Miljković, Z.. (2015). Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter. in Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015
Society for Information Systems and Computer Networks., 89-94.
https://hdl.handle.net/21.15107/rcub_machinery_4468

Vuković N, Mitić M, Petrović M, Petronijević J, Miljković Z. Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter. in Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015. 2015;:89-94.
https://hdl.handle.net/21.15107/rcub_machinery_4468 .

Vuković, Najdan, Mitić, Marko, Petrović, Milica, Petronijević, Jelena, Miljković, Zoran, "Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter" in Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015 (2015):89-94,
https://hdl.handle.net/21.15107/rcub_machinery_4468 .

TY  - GEN
AU  - Petronijević, Jelena
AU  - Petrović, Milica
AU  - Vuković, Najdan
AU  - Mitić, Marko
AU  - Babić, Bojan
AU  - Miljković, Zoran
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4815
AB  - Tehničko rešenje - nova metoda (M85), pripada oblasti mašinstva i odnosi se na domen dinamičkog integrisanog planiranja i terminiranja proizvodnje. Rešavanje problema izvodi se multiagentnom metodologijom. Predložena multiagentna arhitektura se sastoji iz pet agenata: agent za delove, agent za operacije, agent za mašine, agent za alate i agent za sinhronizaciju. Sinhronizovanim dejstvom svih agenata uz posedovanje informacije o alternativnim tehnološkim postupcima, a u zavisnosti od stanja okruženja vrši se dinamičko planiranje i terminiranje proizvodnje. Verifikacija predloženog rešenja izvedena je u AnyLogic softverskom paketu. Rezultati simulacije pokazuju da predložena arhitektura omogućuje promenu i prilagođavanje tehnoloških postupaka, kao i planova terminiranja, u zavisnosti od stanja simuliranog modela tehnološkog okruženja. Razvijana je kroz aktivnosti u okviru naučnog projekta pod oznakom TR-35004 MPNiTR Vlade Republike Srbije.
T2  - Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер
T1  - Multiagentni sistem za dinamičko integrisano planiranje i terminiranje proizvodnje
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4815
ER  - 

@misc{
author = "Petronijević, Jelena and Petrović, Milica and Vuković, Najdan and Mitić, Marko and Babić, Bojan and Miljković, Zoran",
year = "2015",
abstract = "Tehničko rešenje - nova metoda (M85), pripada oblasti mašinstva i odnosi se na domen dinamičkog integrisanog planiranja i terminiranja proizvodnje. Rešavanje problema izvodi se multiagentnom metodologijom. Predložena multiagentna arhitektura se sastoji iz pet agenata: agent za delove, agent za operacije, agent za mašine, agent za alate i agent za sinhronizaciju. Sinhronizovanim dejstvom svih agenata uz posedovanje informacije o alternativnim tehnološkim postupcima, a u zavisnosti od stanja okruženja vrši se dinamičko planiranje i terminiranje proizvodnje. Verifikacija predloženog rešenja izvedena je u AnyLogic softverskom paketu. Rezultati simulacije pokazuju da predložena arhitektura omogućuje promenu i prilagođavanje tehnoloških postupaka, kao i planova terminiranja, u zavisnosti od stanja simuliranog modela tehnološkog okruženja. Razvijana je kroz aktivnosti u okviru naučnog projekta pod oznakom TR-35004 MPNiTR Vlade Republike Srbije.",
journal = "Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер",
title = "Multiagentni sistem za dinamičko integrisano planiranje i terminiranje proizvodnje",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4815"
}

Petronijević, J., Petrović, M., Vuković, N., Mitić, M., Babić, B.,& Miljković, Z.. (2015). Multiagentni sistem za dinamičko integrisano planiranje i terminiranje proizvodnje. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер.
https://hdl.handle.net/21.15107/rcub_machinery_4815

Petronijević J, Petrović M, Vuković N, Mitić M, Babić B, Miljković Z. Multiagentni sistem za dinamičko integrisano planiranje i terminiranje proizvodnje. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер. 2015;.
https://hdl.handle.net/21.15107/rcub_machinery_4815 .

Petronijević, Jelena, Petrović, Milica, Vuković, Najdan, Mitić, Marko, Babić, Bojan, Miljković, Zoran, "Multiagentni sistem za dinamičko integrisano planiranje i terminiranje proizvodnje" in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер (2015),
https://hdl.handle.net/21.15107/rcub_machinery_4815 .

TY  - GEN
AU  - Petrović, Milica
AU  - Petronijević, Jelena
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Miljković, Zoran
AU  - Babić, Bojan
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4813
AB  - Tehničko rešenje - nova metoda (M85), pripada oblasti mašinstva i direktno se odnosi na domen integrisanog projektovanja i terminiranja fleksibilnih tehnoloških procesa. Shodno tome, metoda rešava problem generisanja optimalnih planova terminiranja primenom biološki inspirisanog algoritma na bazi inteligencije roja čestica (engl. PSO – Particle Swarm Optimization) i teorije haosa (engl. Chaos theory). Jedan od nedostataka tradicionalnog PSO algoritma je i konvergencija ka lokalnom optimalnom rešenju u ranim fazama optimizacije. U cilju prevazilaženja nedostatka vezanih za brzu konvergenciju algoritma i povećavanje prostora alternativnih rešenja, haotične mape su implementirane u PSO algoritam. Rezultati optimizacije planova terminiranja za odabrane „benchmark“ delove iz literature pokazuju opravdanost primene predloženog koncepta. Razvijana je u okviru aktivnosti naučnog projekta pod oznakomТР-35004 MPNiTR Vlade Republike Srbije.
T2  - Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер
T1  - Integrisano projektovanje i terminiranje tehnoloških procesa primenom inteligencije roja čestica i teorije haosa
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4813
ER  - 

@misc{
author = "Petrović, Milica and Petronijević, Jelena and Mitić, Marko and Vuković, Najdan and Miljković, Zoran and Babić, Bojan",
year = "2015",
abstract = "Tehničko rešenje - nova metoda (M85), pripada oblasti mašinstva i direktno se odnosi na domen integrisanog projektovanja i terminiranja fleksibilnih tehnoloških procesa. Shodno tome, metoda rešava problem generisanja optimalnih planova terminiranja primenom biološki inspirisanog algoritma na bazi inteligencije roja čestica (engl. PSO – Particle Swarm Optimization) i teorije haosa (engl. Chaos theory). Jedan od nedostataka tradicionalnog PSO algoritma je i konvergencija ka lokalnom optimalnom rešenju u ranim fazama optimizacije. U cilju prevazilaženja nedostatka vezanih za brzu konvergenciju algoritma i povećavanje prostora alternativnih rešenja, haotične mape su implementirane u PSO algoritam. Rezultati optimizacije planova terminiranja za odabrane „benchmark“ delove iz literature pokazuju opravdanost primene predloženog koncepta. Razvijana je u okviru aktivnosti naučnog projekta pod oznakomТР-35004 MPNiTR Vlade Republike Srbije.",
journal = "Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер",
title = "Integrisano projektovanje i terminiranje tehnoloških procesa primenom inteligencije roja čestica i teorije haosa",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4813"
}

Petrović, M., Petronijević, J., Mitić, M., Vuković, N., Miljković, Z.,& Babić, B.. (2015). Integrisano projektovanje i terminiranje tehnoloških procesa primenom inteligencije roja čestica i teorije haosa. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер.
https://hdl.handle.net/21.15107/rcub_machinery_4813

Petrović M, Petronijević J, Mitić M, Vuković N, Miljković Z, Babić B. Integrisano projektovanje i terminiranje tehnoloških procesa primenom inteligencije roja čestica i teorije haosa. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер. 2015;.
https://hdl.handle.net/21.15107/rcub_machinery_4813 .

Petrović, Milica, Petronijević, Jelena, Mitić, Marko, Vuković, Najdan, Miljković, Zoran, Babić, Bojan, "Integrisano projektovanje i terminiranje tehnoloških procesa primenom inteligencije roja čestica i teorije haosa" in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер (2015),
https://hdl.handle.net/21.15107/rcub_machinery_4813 .

TY  - JOUR
AU  - Petrović, Milica
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Petronijević, Jelena
AU  - Miljković, Zoran
AU  - Babi, Bojan
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4557
AB  - The paper presents an approach based on the application of the Particle Swarm Intelligence algorithm for solving the combinatorial optimization problem of determining the order of execution of operations when processing parts on machines. The proposed approach considers the following types of flexibility: machine flexibility, tool flexibility, process flexibility, and operation sequence flexibility. To represent the flexibility of the machining process of part processing, the method of representing the manufacturing process through networks was chosen, while for the described mathematical model, the criteria for optimization are minimum production time and minimum costs. Experimental results show that the presented algorithm is more efficient, i.e. to give optimal orders of operations in less time and fewer iterations compared to single GA, SA and hybrid GA-SA algorithm.
PB  - Beograd : JUQS
T2  - International Journal Advanced Quality
T1  - Modified Chaotic Particle Swarm Optimization Algorithm for Flexible Process Planning
EP  - 32
IS  - 3
SP  - 25
VL  - 43
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4557
ER  - 

@article{
author = "Petrović, Milica and Mitić, Marko and Vuković, Najdan and Petronijević, Jelena and Miljković, Zoran and Babi, Bojan",
year = "2015",
abstract = "The paper presents an approach based on the application of the Particle Swarm Intelligence algorithm for solving the combinatorial optimization problem of determining the order of execution of operations when processing parts on machines. The proposed approach considers the following types of flexibility: machine flexibility, tool flexibility, process flexibility, and operation sequence flexibility. To represent the flexibility of the machining process of part processing, the method of representing the manufacturing process through networks was chosen, while for the described mathematical model, the criteria for optimization are minimum production time and minimum costs. Experimental results show that the presented algorithm is more efficient, i.e. to give optimal orders of operations in less time and fewer iterations compared to single GA, SA and hybrid GA-SA algorithm.",
publisher = "Beograd : JUQS",
journal = "International Journal Advanced Quality",
title = "Modified Chaotic Particle Swarm Optimization Algorithm for Flexible Process Planning",
pages = "32-25",
number = "3",
volume = "43",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4557"
}

Petrović, M., Mitić, M., Vuković, N., Petronijević, J., Miljković, Z.,& Babi, B.. (2015). Modified Chaotic Particle Swarm Optimization Algorithm for Flexible Process Planning. in International Journal Advanced Quality
Beograd : JUQS., 43(3), 25-32.
https://hdl.handle.net/21.15107/rcub_machinery_4557

Petrović M, Mitić M, Vuković N, Petronijević J, Miljković Z, Babi B. Modified Chaotic Particle Swarm Optimization Algorithm for Flexible Process Planning. in International Journal Advanced Quality. 2015;43(3):25-32.
https://hdl.handle.net/21.15107/rcub_machinery_4557 .

Petrović, Milica, Mitić, Marko, Vuković, Najdan, Petronijević, Jelena, Miljković, Zoran, Babi, Bojan, "Modified Chaotic Particle Swarm Optimization Algorithm for Flexible Process Planning" in International Journal Advanced Quality, 43, no. 3 (2015):25-32,
https://hdl.handle.net/21.15107/rcub_machinery_4557 .

TY  - JOUR
AU  - Petrović, Milica
AU  - Petronijević, Jelena
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Plemić, Aleksandar
AU  - Miljković, Zoran
AU  - Babić, Bojan
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4419
AB  - Obtaining an optimal process plan according to all alternative manufacturing resources has become very important task in flexible process planning problem research. In this paper, we use a novel nature-inspired algorithm called Ant Lion Optimizer (ALO) to solve this NP-hard combinatorial optimization problem. The network representation is adopted to describe flexibilities in process planning and mathematical model for the minimization of the total production time and cost is presented. The algorithm is implemented in Matlab environment and run on the 3.10 GHz processor with 2 GBs of RAM memory. The presented experimental results show that the proposed algorithm performs better in comparison with other bio-inspired optimization algorithms.
PB  - University of Novi Sad - Faculty of Technical Sciences
T2  - Journal of Production Engineering
T1  - The Ant Lion Optimization Algorithm for Flexible Process Planning
EP  - 68
IS  - 2
SP  - 65
VL  - 18
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4419
ER  - 

@article{
author = "Petrović, Milica and Petronijević, Jelena and Mitić, Marko and Vuković, Najdan and Plemić, Aleksandar and Miljković, Zoran and Babić, Bojan",
year = "2015",
abstract = "Obtaining an optimal process plan according to all alternative manufacturing resources has become very important task in flexible process planning problem research. In this paper, we use a novel nature-inspired algorithm called Ant Lion Optimizer (ALO) to solve this NP-hard combinatorial optimization problem. The network representation is adopted to describe flexibilities in process planning and mathematical model for the minimization of the total production time and cost is presented. The algorithm is implemented in Matlab environment and run on the 3.10 GHz processor with 2 GBs of RAM memory. The presented experimental results show that the proposed algorithm performs better in comparison with other bio-inspired optimization algorithms.",
publisher = "University of Novi Sad - Faculty of Technical Sciences",
journal = "Journal of Production Engineering",
title = "The Ant Lion Optimization Algorithm for Flexible Process Planning",
pages = "68-65",
number = "2",
volume = "18",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4419"
}

Petrović, M., Petronijević, J., Mitić, M., Vuković, N., Plemić, A., Miljković, Z.,& Babić, B.. (2015). The Ant Lion Optimization Algorithm for Flexible Process Planning. in Journal of Production Engineering
University of Novi Sad - Faculty of Technical Sciences., 18(2), 65-68.
https://hdl.handle.net/21.15107/rcub_machinery_4419

Petrović M, Petronijević J, Mitić M, Vuković N, Plemić A, Miljković Z, Babić B. The Ant Lion Optimization Algorithm for Flexible Process Planning. in Journal of Production Engineering. 2015;18(2):65-68.
https://hdl.handle.net/21.15107/rcub_machinery_4419 .

Petrović, Milica, Petronijević, Jelena, Mitić, Marko, Vuković, Najdan, Plemić, Aleksandar, Miljković, Zoran, Babić, Bojan, "The Ant Lion Optimization Algorithm for Flexible Process Planning" in Journal of Production Engineering, 18, no. 2 (2015):65-68,
https://hdl.handle.net/21.15107/rcub_machinery_4419 .

TY  - GEN
AU  - Petrović, Milica
AU  - Petronijević, Jelena
AU  - Vuković, Najdan
AU  - Mitić, Marko
AU  - Miljković, Zoran
AU  - Babić, Bojan
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4756
AB  - Tehničko rešenje - nova metoda (M85), odnosi se na problem generisanja optimalnih planova terminiranja primenom multiagentnih sistema i tehnika veštačke inteligencije, konkretno biološki inspirisanog algoritma na bazi inteligencije roja čestica (engl. PSO – Particle Swarm Optimization) i veštačkih neuronskih mreža (engl. ANN – Artificial Neural Networks). Ova nova metoda pripada oblasti mašinstva i odnosi se na domen integrisanog projektovanja i terminiranja optimalnih fleksibilnih tehnoloških procesa. Predložena multiagentna arhitehtura se sastoji od šest agenata: agent za optimizaciju, agent za učenje, agent za delove, agent za mašine, agent za alate i agent za transport. Agent za učenje zajedno sa agentom za optimizaciju vrši generisanje optimalnih fleksibilnih tehnoloških procesa, dok preostala četiri agenta učestvuju u njihovom terminiranju. Dakle, nakon generisanja optimalnih i približno optimalnih alternativnih tehnoloških procesa obrade delova, u AnyLogic softverskom paketu je izvršeno terminiranje primenom razvijenih agenata. Simulacioni rezultati optimizacije planova terminiranja za odabrane „benchmark“ delove iz literature pokazuju opravdanost primene predložene metodologije u simuliranom modelu tehnološkog okruženja. Razvijana je kroz opsežne aktivnosti u okviru naučnog projekta pod oznakom TR-35004 MPNiTR Vlade Republike Srbije.
T2  - Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер
T1  - Integrisano projektovanje i teriminiranje otimalnih fleksibilnih tehnoloških procesa bazirano na multiagentnim sistemima i tehnikama veštačke inteligencije
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4756
ER  - 

@misc{
author = "Petrović, Milica and Petronijević, Jelena and Vuković, Najdan and Mitić, Marko and Miljković, Zoran and Babić, Bojan",
year = "2014",
abstract = "Tehničko rešenje - nova metoda (M85), odnosi se na problem generisanja optimalnih planova terminiranja primenom multiagentnih sistema i tehnika veštačke inteligencije, konkretno biološki inspirisanog algoritma na bazi inteligencije roja čestica (engl. PSO – Particle Swarm Optimization) i veštačkih neuronskih mreža (engl. ANN – Artificial Neural Networks). Ova nova metoda pripada oblasti mašinstva i odnosi se na domen integrisanog projektovanja i terminiranja optimalnih fleksibilnih tehnoloških procesa. Predložena multiagentna arhitehtura se sastoji od šest agenata: agent za optimizaciju, agent za učenje, agent za delove, agent za mašine, agent za alate i agent za transport. Agent za učenje zajedno sa agentom za optimizaciju vrši generisanje optimalnih fleksibilnih tehnoloških procesa, dok preostala četiri agenta učestvuju u njihovom terminiranju. Dakle, nakon generisanja optimalnih i približno optimalnih alternativnih tehnoloških procesa obrade delova, u AnyLogic softverskom paketu je izvršeno terminiranje primenom razvijenih agenata. Simulacioni rezultati optimizacije planova terminiranja za odabrane „benchmark“ delove iz literature pokazuju opravdanost primene predložene metodologije u simuliranom modelu tehnološkog okruženja. Razvijana je kroz opsežne aktivnosti u okviru naučnog projekta pod oznakom TR-35004 MPNiTR Vlade Republike Srbije.",
journal = "Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер",
title = "Integrisano projektovanje i teriminiranje otimalnih fleksibilnih tehnoloških procesa bazirano na multiagentnim sistemima i tehnikama veštačke inteligencije",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4756"
}

Petrović, M., Petronijević, J., Vuković, N., Mitić, M., Miljković, Z.,& Babić, B.. (2014). Integrisano projektovanje i teriminiranje otimalnih fleksibilnih tehnoloških procesa bazirano na multiagentnim sistemima i tehnikama veštačke inteligencije. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер.
https://hdl.handle.net/21.15107/rcub_machinery_4756

Petrović M, Petronijević J, Vuković N, Mitić M, Miljković Z, Babić B. Integrisano projektovanje i teriminiranje otimalnih fleksibilnih tehnoloških procesa bazirano na multiagentnim sistemima i tehnikama veštačke inteligencije. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер. 2014;.
https://hdl.handle.net/21.15107/rcub_machinery_4756 .

Petrović, Milica, Petronijević, Jelena, Vuković, Najdan, Mitić, Marko, Miljković, Zoran, Babić, Bojan, "Integrisano projektovanje i teriminiranje otimalnih fleksibilnih tehnoloških procesa bazirano na multiagentnim sistemima i tehnikama veštačke inteligencije" in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер (2014),
https://hdl.handle.net/21.15107/rcub_machinery_4756 .

TY  - CONF
AU  - Petronijević, Jelena
AU  - Petrović, Milica
AU  - Babić, Bojan
AU  - Miljković, Zoran
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4562
AB  - Sistemi zasnovani na agentima primenju se za razvoj društvenih, bioloških i tehničkih sistema. U domenu tehničkih sistema, svoju primenu nalaze i u rešavanju problema optimizacije savremenih tehnoloških sistema. U radu je predstavljena razvijena multiagentna metodologija za optimalno projektovanje tehnoloških procesa obrade dela. Predložena multiagentna arhitehtura se sastoji od četiri agenta: agent za delove, agent za mašine, agent za transport i agent za optimizaciju. Nakon generisanja optimalnih tehnoloških procesa primenom biološki inspirisanog algoritma na bazi inteligencije roja čestica, u AnyLogic softverskom paketu je izvršena simulacija primenom razvijenih agenata. Eksperimentalni rezultati pokazuju opravdanost primene predložene metodologije u simuliranom modelu tehnološkog okruženja.
AB  - Agent based systems have been used for the development of social, biological, and technical systems. In the domain of technical systems, they are widely applied in optimization problems of modern manufacturing systems. This paper presents multi-agent methodology for optimal process planning. The proposed multi-agent architecture consists of four intelligent agents: job/part agent, machine agent, transport agent, and optimization agent. After generation of
optimal process plans, agent based simulation was performed using AnyLogic software. Use of applied method has been justified by experimental results in simulated model of manufacturing environment.
PB  - JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet
C3  - 39th JUPITER Conference : Proceedings, Beograd, oktobar 2014
T1  - Примена мултиагентних система и теорије ројева у оптимизацији флексибилних технолошких процеса
T1  - Application of Multi-Agent Systems and Particle Swarm Optimization Algorithm for Flexible Process Planning
EP  - 3.121
SP  - 3.114
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4562
ER  - 

@conference{
author = "Petronijević, Jelena and Petrović, Milica and Babić, Bojan and Miljković, Zoran",
year = "2014",
abstract = "Sistemi zasnovani na agentima primenju se za razvoj društvenih, bioloških i tehničkih sistema. U domenu tehničkih sistema, svoju primenu nalaze i u rešavanju problema optimizacije savremenih tehnoloških sistema. U radu je predstavljena razvijena multiagentna metodologija za optimalno projektovanje tehnoloških procesa obrade dela. Predložena multiagentna arhitehtura se sastoji od četiri agenta: agent za delove, agent za mašine, agent za transport i agent za optimizaciju. Nakon generisanja optimalnih tehnoloških procesa primenom biološki inspirisanog algoritma na bazi inteligencije roja čestica, u AnyLogic softverskom paketu je izvršena simulacija primenom razvijenih agenata. Eksperimentalni rezultati pokazuju opravdanost primene predložene metodologije u simuliranom modelu tehnološkog okruženja., Agent based systems have been used for the development of social, biological, and technical systems. In the domain of technical systems, they are widely applied in optimization problems of modern manufacturing systems. This paper presents multi-agent methodology for optimal process planning. The proposed multi-agent architecture consists of four intelligent agents: job/part agent, machine agent, transport agent, and optimization agent. After generation of
optimal process plans, agent based simulation was performed using AnyLogic software. Use of applied method has been justified by experimental results in simulated model of manufacturing environment.",
publisher = "JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet",
journal = "39th JUPITER Conference : Proceedings, Beograd, oktobar 2014",
title = "Примена мултиагентних система и теорије ројева у оптимизацији флексибилних технолошких процеса, Application of Multi-Agent Systems and Particle Swarm Optimization Algorithm for Flexible Process Planning",
pages = "3.121-3.114",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4562"
}

Petronijević, J., Petrović, M., Babić, B.,& Miljković, Z.. (2014). Примена мултиагентних система и теорије ројева у оптимизацији флексибилних технолошких процеса. in 39th JUPITER Conference : Proceedings, Beograd, oktobar 2014
JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet., 3.114-3.121.
https://hdl.handle.net/21.15107/rcub_machinery_4562

Petronijević J, Petrović M, Babić B, Miljković Z. Примена мултиагентних система и теорије ројева у оптимизацији флексибилних технолошких процеса. in 39th JUPITER Conference : Proceedings, Beograd, oktobar 2014. 2014;:3.114-3.121.
https://hdl.handle.net/21.15107/rcub_machinery_4562 .

Petronijević, Jelena, Petrović, Milica, Babić, Bojan, Miljković, Zoran, "Примена мултиагентних система и теорије ројева у оптимизацији флексибилних технолошких процеса" in 39th JUPITER Conference : Proceedings, Beograd, oktobar 2014 (2014):3.114-3.121,
https://hdl.handle.net/21.15107/rcub_machinery_4562 .

TY  - GEN
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Petrović, Milica
AU  - Petronijević, Jelena
AU  - Miljković, Zoran
AU  - Lazarević, Ivan
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4753
AB  - Tehničko rešenje - nova metoda (M85), odnosi se na rešavanje kompleksnog problema upravljanja inteligentnog mobilnog robota primenom empirijske upravljačke teorije na bazi biološki inspirisanih algoritama optimizacije i mašinskog učenja demonstracijom,  i to tako da se upravljačke komande mobilnog robota koriste za reprodukciju više trajektorija željenog oblika u okviru modula za demonstraciju, dok se u modulu mašinskog učenja vrši implementacija metoda optimizacije u cilju  određivanja optimalne trajektorija robota. Ova metoda je razvijana u okviru naučnog  projekta pod oznakom TR-35004 MPNiTR Vlade Republike Srbije
T2  - Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер
T1  - Reprodukcija kompleksnih trajektorija mobilnog robota na bazi biološki inspirisanih algoritama
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4753
ER  - 

@misc{
author = "Mitić, Marko and Vuković, Najdan and Petrović, Milica and Petronijević, Jelena and Miljković, Zoran and Lazarević, Ivan",
year = "2014",
abstract = "Tehničko rešenje - nova metoda (M85), odnosi se na rešavanje kompleksnog problema upravljanja inteligentnog mobilnog robota primenom empirijske upravljačke teorije na bazi biološki inspirisanih algoritama optimizacije i mašinskog učenja demonstracijom,  i to tako da se upravljačke komande mobilnog robota koriste za reprodukciju više trajektorija željenog oblika u okviru modula za demonstraciju, dok se u modulu mašinskog učenja vrši implementacija metoda optimizacije u cilju  određivanja optimalne trajektorija robota. Ova metoda je razvijana u okviru naučnog  projekta pod oznakom TR-35004 MPNiTR Vlade Republike Srbije",
journal = "Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер",
title = "Reprodukcija kompleksnih trajektorija mobilnog robota na bazi biološki inspirisanih algoritama",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4753"
}

Mitić, M., Vuković, N., Petrović, M., Petronijević, J., Miljković, Z.,& Lazarević, I.. (2014). Reprodukcija kompleksnih trajektorija mobilnog robota na bazi biološki inspirisanih algoritama. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер.
https://hdl.handle.net/21.15107/rcub_machinery_4753

Mitić M, Vuković N, Petrović M, Petronijević J, Miljković Z, Lazarević I. Reprodukcija kompleksnih trajektorija mobilnog robota na bazi biološki inspirisanih algoritama. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер. 2014;.
https://hdl.handle.net/21.15107/rcub_machinery_4753 .

Mitić, Marko, Vuković, Najdan, Petrović, Milica, Petronijević, Jelena, Miljković, Zoran, Lazarević, Ivan, "Reprodukcija kompleksnih trajektorija mobilnog robota na bazi biološki inspirisanih algoritama" in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер (2014),
https://hdl.handle.net/21.15107/rcub_machinery_4753 .

TY  - CONF
AU  - Petrović, Milica
AU  - Miljković, Zoran
AU  - Vuković, Najdan
AU  - Babić, Bojan
AU  - Petronijević, Jelena
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4420
AB  - Process planning and scheduling are two of the most important manufacturing functions which are usually performed sequentially in traditional approaches. Considering the fact that these functions are usually complementary, it is necessary to integrate them so as to improve performance of a manufacturing system. This paper conceptualizes a multi-agent methodology by considering four intelligent agents (job, machine, tool, and optimization agent) and presents developed modified particle swarm optimization (mPSO) algorithm to solve this combinatorial
optimization problem effectively. In order to improve the search efficiency and increase ability to find global optimum, proposed mPSO algorithm has been enhanced with new crossover and mutation operators. Experimental results show applicability of the proposed approach in solving integrated process planning and scheduling problem.
PB  - The Aristotle University of Thessaloniki
C3  - Proceedings of the 5th International Conference on Manufacturing Engineering (ICMEN 2014)
T1  - Integration of Process Planning and Scheduling Using Modified Particle Swarm Optimization Algorithm
EP  - 118
SP  - 109
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4420
ER  - 

@conference{
author = "Petrović, Milica and Miljković, Zoran and Vuković, Najdan and Babić, Bojan and Petronijević, Jelena",
year = "2014",
abstract = "Process planning and scheduling are two of the most important manufacturing functions which are usually performed sequentially in traditional approaches. Considering the fact that these functions are usually complementary, it is necessary to integrate them so as to improve performance of a manufacturing system. This paper conceptualizes a multi-agent methodology by considering four intelligent agents (job, machine, tool, and optimization agent) and presents developed modified particle swarm optimization (mPSO) algorithm to solve this combinatorial
optimization problem effectively. In order to improve the search efficiency and increase ability to find global optimum, proposed mPSO algorithm has been enhanced with new crossover and mutation operators. Experimental results show applicability of the proposed approach in solving integrated process planning and scheduling problem.",
publisher = "The Aristotle University of Thessaloniki",
journal = "Proceedings of the 5th International Conference on Manufacturing Engineering (ICMEN 2014)",
title = "Integration of Process Planning and Scheduling Using Modified Particle Swarm Optimization Algorithm",
pages = "118-109",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4420"
}

Petrović, M., Miljković, Z., Vuković, N., Babić, B.,& Petronijević, J.. (2014). Integration of Process Planning and Scheduling Using Modified Particle Swarm Optimization Algorithm. in Proceedings of the 5th International Conference on Manufacturing Engineering (ICMEN 2014)
The Aristotle University of Thessaloniki., 109-118.
https://hdl.handle.net/21.15107/rcub_machinery_4420

Petrović M, Miljković Z, Vuković N, Babić B, Petronijević J. Integration of Process Planning and Scheduling Using Modified Particle Swarm Optimization Algorithm. in Proceedings of the 5th International Conference on Manufacturing Engineering (ICMEN 2014). 2014;:109-118.
https://hdl.handle.net/21.15107/rcub_machinery_4420 .

Petrović, Milica, Miljković, Zoran, Vuković, Najdan, Babić, Bojan, Petronijević, Jelena, "Integration of Process Planning and Scheduling Using Modified Particle Swarm Optimization Algorithm" in Proceedings of the 5th International Conference on Manufacturing Engineering (ICMEN 2014) (2014):109-118,
https://hdl.handle.net/21.15107/rcub_machinery_4420 .

TY  - GEN
AU  - Petrović, Milica
AU  - Miljković, Zoran
AU  - Vuković, Najdan
AU  - Babić, Bojan
AU  - Petronijević, Jelena
PY  - 2013
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4749
AB  - Tehničko rešenje - nova metoda (M85), odnosi se na domen projektovanja optimalnih tehnoloških procesa obrade dela, i to preko integracije genetičkog algoritma i algoritma simuliranog kaljenja pri rešavanju kompleksnog kombinatorno-optimizacionog problema. Razvijeni hibridni algoritam baziran je na integraciji genetičkog algoritma i algoritma simuliranog kaljenja i obuhvata dve faze u rešavanju razmatranog kombinatorno-optimizacionog problema. Prva faza podrazumeva primenu genetičkih algoritama u inicijalnom globalnom generisanju „dobrih“ tehnoloških procesa. Na bazi odabranih tehnoloških procesa, u drugoj fazi hibridnog metaheurističkog algoritma primenjen je algoritam simuliranog kaljenja, koji se koristi za lokalno pretraživanje „dobrih“ tehnoloških procesa i dobijanje optimalnih i/ili približno optimalnih fleksibilnih tehnoloških procesa mašinske obrade dela. Rezultati ostvareni primenom ove nove metode ukazuju na to da postoji evidentan doprinos postojećem stanju u oblasti optimizacije tehnoloških procesa, koji se ogleda kroz efikasnije generisanje optimalnog i/ili približno optimalnog tehnološkog procesa obrade dela, uzimajući u obzir alternativne mašine alatke i alternativne alate za svaku od operacija. Razvijana je u okviru aktivnosti na naučnom projektu pod oznakom TR-35004 MPNiTR Vlade Republike Srbije.
T2  - Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер
T1  - Optimizacija fleksibilnih tehnoloških procesa primenom hibridnog metaheurističkog algoritma
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4749
ER  - 

@misc{
author = "Petrović, Milica and Miljković, Zoran and Vuković, Najdan and Babić, Bojan and Petronijević, Jelena",
year = "2013",
abstract = "Tehničko rešenje - nova metoda (M85), odnosi se na domen projektovanja optimalnih tehnoloških procesa obrade dela, i to preko integracije genetičkog algoritma i algoritma simuliranog kaljenja pri rešavanju kompleksnog kombinatorno-optimizacionog problema. Razvijeni hibridni algoritam baziran je na integraciji genetičkog algoritma i algoritma simuliranog kaljenja i obuhvata dve faze u rešavanju razmatranog kombinatorno-optimizacionog problema. Prva faza podrazumeva primenu genetičkih algoritama u inicijalnom globalnom generisanju „dobrih“ tehnoloških procesa. Na bazi odabranih tehnoloških procesa, u drugoj fazi hibridnog metaheurističkog algoritma primenjen je algoritam simuliranog kaljenja, koji se koristi za lokalno pretraživanje „dobrih“ tehnoloških procesa i dobijanje optimalnih i/ili približno optimalnih fleksibilnih tehnoloških procesa mašinske obrade dela. Rezultati ostvareni primenom ove nove metode ukazuju na to da postoji evidentan doprinos postojećem stanju u oblasti optimizacije tehnoloških procesa, koji se ogleda kroz efikasnije generisanje optimalnog i/ili približno optimalnog tehnološkog procesa obrade dela, uzimajući u obzir alternativne mašine alatke i alternativne alate za svaku od operacija. Razvijana je u okviru aktivnosti na naučnom projektu pod oznakom TR-35004 MPNiTR Vlade Republike Srbije.",
journal = "Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер",
title = "Optimizacija fleksibilnih tehnoloških procesa primenom hibridnog metaheurističkog algoritma",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4749"
}

Petrović, M., Miljković, Z., Vuković, N., Babić, B.,& Petronijević, J.. (2013). Optimizacija fleksibilnih tehnoloških procesa primenom hibridnog metaheurističkog algoritma. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер.
https://hdl.handle.net/21.15107/rcub_machinery_4749

Petrović M, Miljković Z, Vuković N, Babić B, Petronijević J. Optimizacija fleksibilnih tehnoloških procesa primenom hibridnog metaheurističkog algoritma. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер. 2013;.
https://hdl.handle.net/21.15107/rcub_machinery_4749 .

Petrović, Milica, Miljković, Zoran, Vuković, Najdan, Babić, Bojan, Petronijević, Jelena, "Optimizacija fleksibilnih tehnoloških procesa primenom hibridnog metaheurističkog algoritma" in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер (2013),
https://hdl.handle.net/21.15107/rcub_machinery_4749 .