Review
BibTex RIS Cite

Review on Optimization Techniques for AGV’s Optimization in Flexible Manufacturing System

Year 2023, Volume: 36 Issue: 1, 399 - 412, 01.03.2023
https://doi.org/10.35378/gujs.994588

Abstract

Production systems have been growing exponentially in size in recent decades. One of the problems in the manufacturing system is that a need of effective and efficient materials transportation between the workstations. Auto-mated guided vehicles (AGV’s) can provide a satisfactory solution to this issue. Because of this, AGV’s is a one of the most important material handling tool used in flexible manufacturing system (FMS). The designing, scheduling and routing of the AGV’s are the key issues for the reason that performance of AVG’s in flexible manufacturing system depends on these parameters. The failure of AGVs may significantly impact the operation and efficiency of the entire system, and it is a critical issue. In this paper the aims to answer How the reliability of individual AGVs in the system assessed? , and How AGVs affects the performance of the whole FMS system?. It was also discussed that optimizing techniques to improve the performance of AGV in FMS. This paper discussed the details studies of approaches and optimization techniques used for optimizing AGV’s in FMS including design and their control.

References

  • [1] Fazlollahtabar, H., and Saidi-Mehrabad, M., “Methodologies to optimize automated guided vehicle scheduling and routing problems: a review study”, Journal of Intelligent & Robotic Systems, 77(3): 525-545, (2015).
  • [2] Duinkerken, M.B., Ottjes, J.A., and Lodewijks, G., “Comparison of routing strategies for AGV systems using simulation”, In Proceedings of the 2006 winter simulation conference, IEEE, 1523-1530, (2006).
  • [3] Fazlollahtabar, H., Rezaie, B., and Kalantari, H., “Mathematical programming approach to optimize material flow in an AGV-based flexible job shop manufacturing system with performance analysis”, The International Journal of Advanced Manufacturing Technology, 51(9): 1149-1158, (2010).
  • [4] Badakhshian, M., Sulaiman, S.B., and Ariffin, M.K.A.B.M., “Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm”, International Journal of Physical Sciences, 7(9): 1461-1471, (2012).
  • [5] Udhayakumar, P., and Kumanan, S., “Task scheduling of AGV in FMS using non-traditional optimization techniques”, International Journal of Simulation Modelling, 9(1): 28-39, (2010).
  • [6] Gondran, M., Huguet, M. J., Lacomme, P., and Tchernev, N., “Comparison between two approaches to solve the Job Shop Scheduling Problem with Routing”, IFAC-PapersOnLine, 52(13): 2513-2518, (2019).
  • [7] Gutjahr, M., Kellerer, H., and Parragh, S.N., “Heuristic approaches for scheduling jobs and vehicles in a cyclic flexible manufacturing system”, Procedia Computer Science, 180: 825-832, (2021).
  • [8] Zhang, L., Hu, Y., and Guan, Y., “Research on hybrid-load AGV dispatching problem for mixed-model automobile assembly line”, Procedia CIRP, 81: 1059-1064, (2019).
  • [9] Heger, J., and Voss, T., “Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules”, Procedia CIRP, 81: 1136-1141, (2019).
  • [10] Bocewicz, G., Nielsen, I., and Banaszak, Z., “Automated guided vehicles fleet match-up scheduling with production flow constraints”, Engineering Applications of Artificial Intelligence, 30: 49-62, (2014).
  • [11] Scholz, M., Steinkamp, J., Zwingel, M., Hefner, F., and Franke, J., “Distributed Camera Architecture for Seamless Detection and Tracking of Dynamic Obstacles”, Procedia CIRP, 91: 342-347, (2020).
  • [12] Herrero-Perez, D., and Matinez-Barbera, H., “Decentralized coordination of autonomous AGV’s in flexible manufacturing systems”, International Conference on Intelligent Robots and Systems, 3674-3679, (2008).
  • [13] Gonzalez, S.R., Zambrano, G.M., and Mondragon, I.F., “Semi-heterarchical architecture to AGV adjustable autonomy within FMSs”, IFAC-Papers OnLine, 52(10): (2019).
  • [14] Caruntu, C.F., Pascal, C.M., Maxim, A., and Pauca, O., “Bio-inspired Coordination and Control of Autonomous Vehicles in Future Manufacturing and Goods Transportation”, IFAC-Papers OnLine, 53(2): 10861-10866, (2020).
  • [15] Salehipour, A., and Sepehri, M.M., “Optimal location of workstations in tandem automated-guided vehicle systems”, The International Journal of Advanced Manufacturing Technology, 72(9-12): 1429-1438, (2014).
  • [16] Tuma, C.C., Morandin, O., and Caridá, V. F., “Minimizing the makespan for the problem of reactive production scheduling in a FMS with AGVs using a new structure of chromosome in a hybrid GA with TS”, IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA), 1-6, (2013).
  • [17] Sedehi, M.S., and Farahani, R.Z., “An integrated approach to determine the block layout, AGV flow path and the location of pick-up/delivery points in single-loop systems”, International Journal of Production Research, 47(11): 3041-3061.
  • [18] Gebennini, E., Dallari, S., Grassi, A., Perrica, G., Fantuzzi, C., and Gamberini, R., “A simulation based approach for supporting automated guided vehicles (AGVs) systems design”, Winter Simulation Conference, IEEE, 2156-2163, (2009).
  • [19] Erol, R., Sahin, C., Baykasoglu, A., and Kaplanoglu, V., “A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems”, Applied soft computing: 12(6), 1720-1732, (2012).
  • [20] Löfving, M., Almström, P., Jarebrant, C., Wadman, B., and Widfeldt, M., “Evaluation of flexible automation for small batch production”, Procedia Manufacturing, 25: 177-184, (2018).
  • [21] Mehami, J., Nawi, M., and Zhong, R.Y., “Smart automated guided vehicles for manufacturing in the context of Industry 4.0”, Procedia manufacturing, 26: 1077-1086, (2018).
  • [22] Morinaga, E., Yasuda, D., Imagawa, Y., Wakamatsu, H., Tsumaya, A., Inoue, T., and Fujii, S., “A study on highly-distributed manufacturing system simulation”, Procedia Manufacturing, 39: 50-57, (2019).
  • [23] Quadrini, W., Negri, E., and Fumagalli, L., “Open interfaces for connecting automated guided vehicles to a fleet management system”, Procedia Manufacturing, 42: 406-413, (2020).
  • [24] Gebennini, E., Dallari, S., Grassi, A., Perrica, G., Fantuzzi, C., and Gamberini, R., “A simulation based approach for supporting automated guided vehicles (AGVs) systems design”, In 2008 Winter Simulation Conference, IEEE, 2156-2163, (2008).
  • [25] Viharos, A. B., and Németh, I. “Simulation and scheduling of AGV based robotic assembly systems”, IFAC-PapersOnLine, 51(11): 1415-1420, (2018).
  • [26] Li, B., and Zhang, Y., “Fault-tolerant cooperative motion planning of connected and automated vehicles at a signal-free and lane-free intersection”, IFAC-PapersOnLine, 51(24): 60-67, (2018).
  • [27] Beji, L., and Bestaoui, Y., “Motion generation and adaptive control method of automated guided vehicles in road following”, IEEE, Transactions on intelligent transportation systems, 6(1): 113-123, (2005).
  • [28] Gawrilow, E., Köhler, E., Möhring, R.H., and Stenzel, B., “Dynamic routing of automated guided vehicles in real-time”, In Mathematics–Key Technology for the Future, Springer, Berlin, Heidelberg, 165-177, (2008).
  • [29] Bilge, Ü., Esenduran, G., Varol, N., Öztürk, Z., Aydın, B., and Alp, A., “Multi-attribute responsive dispatching strategies for automated guided vehicles”, International Journal of Production Economics, 100(1): 65-75, (2006).
  • [30] Das, S. K., and Pasan, M. K., “Design and methodology of automated guided vehicle-a review”, IOSR, Journal of Mechanical and Civil Engineering, 29-35, (2016).
  • [31] Umar, U.A., Ariffin, M.K.A., Ismail, N., and Tang, S.H., “Conflict-free automated guided vehicles routing using multi-objective genetic algorithm Research Journal of Applied Sciences, Engineering and Technology, 6: 2681-2684, (2013).
  • [32] Kumar, K.K., Krishna, M.S., Ravitej, D., and Bhavana, D., Design of Automatic Guided Vehicles. International Journal of Mechanical Engineering and Technology (IJMET), 3: 24-32, (2012).
  • [33] Khedkar, A., Kajani, K., Ipkal, P., Banthia, S., Jagdale, B. N., and Kulkarni, M., “Automated Guided Vehicle System with Collision Avoidance and Navigation in Warehouse Environments”, Sensors, 7(05): (2020).
  • [34] Fu, J., “Jian Zhang, Guofu Ding, Shengfeng Qin and Haifan Jiang, “Determination of vehicle requirements of AGV system based on discrete event simulation and response surface methodology”, Proc IMechE Part B: J Engineering Manufacture, 235(9): 1425–1436, (2021).
  • [35] Yokota, T., “Heuristic Algorithm for the Co-Operative Picking with AGVs in Warehouse”, SICE Journal of Control, Measurement, and System Integration, 13(3): 057–065, (2020).
  • [36] Berman, S., Edna S., Yael E., “Evaluation of automatic guided vehicle systems”, Robotics and Computer-Integrated Manufacturing, (2008). DOI: 10.1016/j.rcim.2008.02.009
  • [37] Choobineh, F., Asef-Vaziri, A., and Huang, X., “Fleet sizing of automated guided vehicles: A linear programming approach based on closed queuing networks”, International Journal of Production Research, 50(12): 3222-3235, (2012).
  • [38] Kabir, Q.S., Suzuki, Y., “Comparative analysis of different routing heuristics for the battery management of automated guided vehicles”, International Journal of Production Research, 57(2): 624-641, (2019).
  • [39] Corréa, A.I., Langevin, A. and Rousseau, L.M., “Scheduling and Routing of Automated Guided Vehicles: A Hybrid Approach”, Computers & Operations Research, 34: 1688–1707, (2007).
  • [40] Barberá, H.M. and Herrero-Perez, D., “Development of a flexible AGV for flexible manufacturing systems”, Industrial Robot, 37(5): 459-468, (2010).
  • [41] Thurston, T. and Hsu, H., “Distributed agent architecture for port automation”, Proceedings of the 26th annual international computer software and applications conference (COMPSAC’02), Oxford, August 26–29. IEEE Computer Society, Los Alamitos, 81–87, (2002).
  • [42] Chaudhry, A.C., Mahmood, S. and Shami, M., “Simultaneous scheduling of machines and automated guided vehicles in flexible manufacturing systems using genetic algorithms”, Journal of Central South University of Technology, 18(5): 1473-1486, (2011).
  • [43] Cheng, Y., Sen, H., Natarajan, K., Teo, C. and Tan, K.,“Dispatching Automated Guided Vehicles in a Container Terminal”, Technical Report, National University of Singapore, (2003).
  • [44] Nageswararao, M., Narayanaraobd, K. and Ranagajanardhana, G., “Simultaneous Scheduling of Machines and AGVs in Flexible Manufacturing System with Minimization of Tardiness Criterion”, Procedia Materials Science, 5: 1492-1501, (2014).
  • [45] Chawla, V.K., Chandab, A.k. Angra, S., “Scheduling of Multi-Load AGVs In FMS By Modified Memetic Particle Swarm Optimization Algorithm”, Journal of Project Management, 3: 39–54, (2018).
  • [46] Rashidi, H., Kabir,A., “Scheduling Single-Load and Multi-Load AGVs in Container Terminals”, Journal of Science and Technology, 42(2): 1–10, (2010).
  • [47] Rashidi, H. and Tsang, E.P.K., “Vehicle Scheduling in Port Automation: Advanced Algorithms for Minimum Cost Flow Problems”, Second Edition, CRC Press, New York, (2016).
  • [48] Rahman, H. and Nielsen, I., “Scheduling Automated Transport Vehicles for Material Distribution Systems”, Applied Soft Computing, 82: 1-17, (2019)
  • [49] Demesure, G., Defoort, M., Bekrar, A., Trentesaux, D. and Djemaï M., ‘‘Decentralized motion planning and scheduling of AGVs in an FMS’’, IEEE Trans. Ind. Informat., 14(4): 1744–1752, (2018).
  • [50] Rahimikelarijani, B., Saidi-Mehrabad, M. and Barzinpour, F., “A Mathematical Model for Multiple-Load AGVs in Tandem Layout”, Journal of Optimization in Industrial Engineering, 13(1): 67-80, (2019).
  • [51] Maoudj, A., Bouzouia, B., Hentout, A., Kouider, A. and Toumi, R., “Distributed multi-agent scheduling and control system for robotic flexible assembly cells”, Journal of Intelligent Manufacturing, 30(4): 1629-1644, (2019).
  • [52] Yoshitake, H., Kamoshida, R. and Nagashima, Y., “New Automated Guided Vehicle System Using Real-Time Holonic Scheduling for Warehouse Picking”, IEEE Robotics and Automation Letters, 4(2): 1045-1052, (2019).
  • [53] Gu, W., Li, Y., Zheng, K., and Yuan, M., “A bio-inspired scheduling approach for machines and automated guided vehicles in flexible manufacturing system using hormone secretion principle”, Advances in Mechanical Engineering, 12(2): 1-17, (2020).
Year 2023, Volume: 36 Issue: 1, 399 - 412, 01.03.2023
https://doi.org/10.35378/gujs.994588

Abstract

References

  • [1] Fazlollahtabar, H., and Saidi-Mehrabad, M., “Methodologies to optimize automated guided vehicle scheduling and routing problems: a review study”, Journal of Intelligent & Robotic Systems, 77(3): 525-545, (2015).
  • [2] Duinkerken, M.B., Ottjes, J.A., and Lodewijks, G., “Comparison of routing strategies for AGV systems using simulation”, In Proceedings of the 2006 winter simulation conference, IEEE, 1523-1530, (2006).
  • [3] Fazlollahtabar, H., Rezaie, B., and Kalantari, H., “Mathematical programming approach to optimize material flow in an AGV-based flexible job shop manufacturing system with performance analysis”, The International Journal of Advanced Manufacturing Technology, 51(9): 1149-1158, (2010).
  • [4] Badakhshian, M., Sulaiman, S.B., and Ariffin, M.K.A.B.M., “Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm”, International Journal of Physical Sciences, 7(9): 1461-1471, (2012).
  • [5] Udhayakumar, P., and Kumanan, S., “Task scheduling of AGV in FMS using non-traditional optimization techniques”, International Journal of Simulation Modelling, 9(1): 28-39, (2010).
  • [6] Gondran, M., Huguet, M. J., Lacomme, P., and Tchernev, N., “Comparison between two approaches to solve the Job Shop Scheduling Problem with Routing”, IFAC-PapersOnLine, 52(13): 2513-2518, (2019).
  • [7] Gutjahr, M., Kellerer, H., and Parragh, S.N., “Heuristic approaches for scheduling jobs and vehicles in a cyclic flexible manufacturing system”, Procedia Computer Science, 180: 825-832, (2021).
  • [8] Zhang, L., Hu, Y., and Guan, Y., “Research on hybrid-load AGV dispatching problem for mixed-model automobile assembly line”, Procedia CIRP, 81: 1059-1064, (2019).
  • [9] Heger, J., and Voss, T., “Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules”, Procedia CIRP, 81: 1136-1141, (2019).
  • [10] Bocewicz, G., Nielsen, I., and Banaszak, Z., “Automated guided vehicles fleet match-up scheduling with production flow constraints”, Engineering Applications of Artificial Intelligence, 30: 49-62, (2014).
  • [11] Scholz, M., Steinkamp, J., Zwingel, M., Hefner, F., and Franke, J., “Distributed Camera Architecture for Seamless Detection and Tracking of Dynamic Obstacles”, Procedia CIRP, 91: 342-347, (2020).
  • [12] Herrero-Perez, D., and Matinez-Barbera, H., “Decentralized coordination of autonomous AGV’s in flexible manufacturing systems”, International Conference on Intelligent Robots and Systems, 3674-3679, (2008).
  • [13] Gonzalez, S.R., Zambrano, G.M., and Mondragon, I.F., “Semi-heterarchical architecture to AGV adjustable autonomy within FMSs”, IFAC-Papers OnLine, 52(10): (2019).
  • [14] Caruntu, C.F., Pascal, C.M., Maxim, A., and Pauca, O., “Bio-inspired Coordination and Control of Autonomous Vehicles in Future Manufacturing and Goods Transportation”, IFAC-Papers OnLine, 53(2): 10861-10866, (2020).
  • [15] Salehipour, A., and Sepehri, M.M., “Optimal location of workstations in tandem automated-guided vehicle systems”, The International Journal of Advanced Manufacturing Technology, 72(9-12): 1429-1438, (2014).
  • [16] Tuma, C.C., Morandin, O., and Caridá, V. F., “Minimizing the makespan for the problem of reactive production scheduling in a FMS with AGVs using a new structure of chromosome in a hybrid GA with TS”, IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA), 1-6, (2013).
  • [17] Sedehi, M.S., and Farahani, R.Z., “An integrated approach to determine the block layout, AGV flow path and the location of pick-up/delivery points in single-loop systems”, International Journal of Production Research, 47(11): 3041-3061.
  • [18] Gebennini, E., Dallari, S., Grassi, A., Perrica, G., Fantuzzi, C., and Gamberini, R., “A simulation based approach for supporting automated guided vehicles (AGVs) systems design”, Winter Simulation Conference, IEEE, 2156-2163, (2009).
  • [19] Erol, R., Sahin, C., Baykasoglu, A., and Kaplanoglu, V., “A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems”, Applied soft computing: 12(6), 1720-1732, (2012).
  • [20] Löfving, M., Almström, P., Jarebrant, C., Wadman, B., and Widfeldt, M., “Evaluation of flexible automation for small batch production”, Procedia Manufacturing, 25: 177-184, (2018).
  • [21] Mehami, J., Nawi, M., and Zhong, R.Y., “Smart automated guided vehicles for manufacturing in the context of Industry 4.0”, Procedia manufacturing, 26: 1077-1086, (2018).
  • [22] Morinaga, E., Yasuda, D., Imagawa, Y., Wakamatsu, H., Tsumaya, A., Inoue, T., and Fujii, S., “A study on highly-distributed manufacturing system simulation”, Procedia Manufacturing, 39: 50-57, (2019).
  • [23] Quadrini, W., Negri, E., and Fumagalli, L., “Open interfaces for connecting automated guided vehicles to a fleet management system”, Procedia Manufacturing, 42: 406-413, (2020).
  • [24] Gebennini, E., Dallari, S., Grassi, A., Perrica, G., Fantuzzi, C., and Gamberini, R., “A simulation based approach for supporting automated guided vehicles (AGVs) systems design”, In 2008 Winter Simulation Conference, IEEE, 2156-2163, (2008).
  • [25] Viharos, A. B., and Németh, I. “Simulation and scheduling of AGV based robotic assembly systems”, IFAC-PapersOnLine, 51(11): 1415-1420, (2018).
  • [26] Li, B., and Zhang, Y., “Fault-tolerant cooperative motion planning of connected and automated vehicles at a signal-free and lane-free intersection”, IFAC-PapersOnLine, 51(24): 60-67, (2018).
  • [27] Beji, L., and Bestaoui, Y., “Motion generation and adaptive control method of automated guided vehicles in road following”, IEEE, Transactions on intelligent transportation systems, 6(1): 113-123, (2005).
  • [28] Gawrilow, E., Köhler, E., Möhring, R.H., and Stenzel, B., “Dynamic routing of automated guided vehicles in real-time”, In Mathematics–Key Technology for the Future, Springer, Berlin, Heidelberg, 165-177, (2008).
  • [29] Bilge, Ü., Esenduran, G., Varol, N., Öztürk, Z., Aydın, B., and Alp, A., “Multi-attribute responsive dispatching strategies for automated guided vehicles”, International Journal of Production Economics, 100(1): 65-75, (2006).
  • [30] Das, S. K., and Pasan, M. K., “Design and methodology of automated guided vehicle-a review”, IOSR, Journal of Mechanical and Civil Engineering, 29-35, (2016).
  • [31] Umar, U.A., Ariffin, M.K.A., Ismail, N., and Tang, S.H., “Conflict-free automated guided vehicles routing using multi-objective genetic algorithm Research Journal of Applied Sciences, Engineering and Technology, 6: 2681-2684, (2013).
  • [32] Kumar, K.K., Krishna, M.S., Ravitej, D., and Bhavana, D., Design of Automatic Guided Vehicles. International Journal of Mechanical Engineering and Technology (IJMET), 3: 24-32, (2012).
  • [33] Khedkar, A., Kajani, K., Ipkal, P., Banthia, S., Jagdale, B. N., and Kulkarni, M., “Automated Guided Vehicle System with Collision Avoidance and Navigation in Warehouse Environments”, Sensors, 7(05): (2020).
  • [34] Fu, J., “Jian Zhang, Guofu Ding, Shengfeng Qin and Haifan Jiang, “Determination of vehicle requirements of AGV system based on discrete event simulation and response surface methodology”, Proc IMechE Part B: J Engineering Manufacture, 235(9): 1425–1436, (2021).
  • [35] Yokota, T., “Heuristic Algorithm for the Co-Operative Picking with AGVs in Warehouse”, SICE Journal of Control, Measurement, and System Integration, 13(3): 057–065, (2020).
  • [36] Berman, S., Edna S., Yael E., “Evaluation of automatic guided vehicle systems”, Robotics and Computer-Integrated Manufacturing, (2008). DOI: 10.1016/j.rcim.2008.02.009
  • [37] Choobineh, F., Asef-Vaziri, A., and Huang, X., “Fleet sizing of automated guided vehicles: A linear programming approach based on closed queuing networks”, International Journal of Production Research, 50(12): 3222-3235, (2012).
  • [38] Kabir, Q.S., Suzuki, Y., “Comparative analysis of different routing heuristics for the battery management of automated guided vehicles”, International Journal of Production Research, 57(2): 624-641, (2019).
  • [39] Corréa, A.I., Langevin, A. and Rousseau, L.M., “Scheduling and Routing of Automated Guided Vehicles: A Hybrid Approach”, Computers & Operations Research, 34: 1688–1707, (2007).
  • [40] Barberá, H.M. and Herrero-Perez, D., “Development of a flexible AGV for flexible manufacturing systems”, Industrial Robot, 37(5): 459-468, (2010).
  • [41] Thurston, T. and Hsu, H., “Distributed agent architecture for port automation”, Proceedings of the 26th annual international computer software and applications conference (COMPSAC’02), Oxford, August 26–29. IEEE Computer Society, Los Alamitos, 81–87, (2002).
  • [42] Chaudhry, A.C., Mahmood, S. and Shami, M., “Simultaneous scheduling of machines and automated guided vehicles in flexible manufacturing systems using genetic algorithms”, Journal of Central South University of Technology, 18(5): 1473-1486, (2011).
  • [43] Cheng, Y., Sen, H., Natarajan, K., Teo, C. and Tan, K.,“Dispatching Automated Guided Vehicles in a Container Terminal”, Technical Report, National University of Singapore, (2003).
  • [44] Nageswararao, M., Narayanaraobd, K. and Ranagajanardhana, G., “Simultaneous Scheduling of Machines and AGVs in Flexible Manufacturing System with Minimization of Tardiness Criterion”, Procedia Materials Science, 5: 1492-1501, (2014).
  • [45] Chawla, V.K., Chandab, A.k. Angra, S., “Scheduling of Multi-Load AGVs In FMS By Modified Memetic Particle Swarm Optimization Algorithm”, Journal of Project Management, 3: 39–54, (2018).
  • [46] Rashidi, H., Kabir,A., “Scheduling Single-Load and Multi-Load AGVs in Container Terminals”, Journal of Science and Technology, 42(2): 1–10, (2010).
  • [47] Rashidi, H. and Tsang, E.P.K., “Vehicle Scheduling in Port Automation: Advanced Algorithms for Minimum Cost Flow Problems”, Second Edition, CRC Press, New York, (2016).
  • [48] Rahman, H. and Nielsen, I., “Scheduling Automated Transport Vehicles for Material Distribution Systems”, Applied Soft Computing, 82: 1-17, (2019)
  • [49] Demesure, G., Defoort, M., Bekrar, A., Trentesaux, D. and Djemaï M., ‘‘Decentralized motion planning and scheduling of AGVs in an FMS’’, IEEE Trans. Ind. Informat., 14(4): 1744–1752, (2018).
  • [50] Rahimikelarijani, B., Saidi-Mehrabad, M. and Barzinpour, F., “A Mathematical Model for Multiple-Load AGVs in Tandem Layout”, Journal of Optimization in Industrial Engineering, 13(1): 67-80, (2019).
  • [51] Maoudj, A., Bouzouia, B., Hentout, A., Kouider, A. and Toumi, R., “Distributed multi-agent scheduling and control system for robotic flexible assembly cells”, Journal of Intelligent Manufacturing, 30(4): 1629-1644, (2019).
  • [52] Yoshitake, H., Kamoshida, R. and Nagashima, Y., “New Automated Guided Vehicle System Using Real-Time Holonic Scheduling for Warehouse Picking”, IEEE Robotics and Automation Letters, 4(2): 1045-1052, (2019).
  • [53] Gu, W., Li, Y., Zheng, K., and Yuan, M., “A bio-inspired scheduling approach for machines and automated guided vehicles in flexible manufacturing system using hormone secretion principle”, Advances in Mechanical Engineering, 12(2): 1-17, (2020).
There are 53 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Mechanical Engineering
Authors

Manoj Gupta 0000-0002-3547-800X

Publication Date March 1, 2023
Published in Issue Year 2023 Volume: 36 Issue: 1

Cite

APA Gupta, M. (2023). Review on Optimization Techniques for AGV’s Optimization in Flexible Manufacturing System. Gazi University Journal of Science, 36(1), 399-412. https://doi.org/10.35378/gujs.994588
AMA Gupta M. Review on Optimization Techniques for AGV’s Optimization in Flexible Manufacturing System. Gazi University Journal of Science. March 2023;36(1):399-412. doi:10.35378/gujs.994588
Chicago Gupta, Manoj. “Review on Optimization Techniques for AGV’s Optimization in Flexible Manufacturing System”. Gazi University Journal of Science 36, no. 1 (March 2023): 399-412. https://doi.org/10.35378/gujs.994588.
EndNote Gupta M (March 1, 2023) Review on Optimization Techniques for AGV’s Optimization in Flexible Manufacturing System. Gazi University Journal of Science 36 1 399–412.
IEEE M. Gupta, “Review on Optimization Techniques for AGV’s Optimization in Flexible Manufacturing System”, Gazi University Journal of Science, vol. 36, no. 1, pp. 399–412, 2023, doi: 10.35378/gujs.994588.
ISNAD Gupta, Manoj. “Review on Optimization Techniques for AGV’s Optimization in Flexible Manufacturing System”. Gazi University Journal of Science 36/1 (March 2023), 399-412. https://doi.org/10.35378/gujs.994588.
JAMA Gupta M. Review on Optimization Techniques for AGV’s Optimization in Flexible Manufacturing System. Gazi University Journal of Science. 2023;36:399–412.
MLA Gupta, Manoj. “Review on Optimization Techniques for AGV’s Optimization in Flexible Manufacturing System”. Gazi University Journal of Science, vol. 36, no. 1, 2023, pp. 399-12, doi:10.35378/gujs.994588.
Vancouver Gupta M. Review on Optimization Techniques for AGV’s Optimization in Flexible Manufacturing System. Gazi University Journal of Science. 2023;36(1):399-412.