Araştırma Makalesi
Yıl 2020,
Cilt: 28 Sayı: 2, 117 - 127, 31.08.2020
Öz
Endüstri 4.0 kavramının ortaya çıkışıyla iç lojistikte akıllı araçların kullanımı yaygınlaşmaya başlamıştır. İç lojistikte, hammadde ya da işlenecek parçaların iş merkezlerine taşınması ve işlenmiş parçaların depoya taşınması görevlerinde kullanılan otomatik yönlendirmeli araçların yerini otonom transfer araçları almaktadır. Dolayısıyla sabit tek bir rota yerine esnek rotalara ihtiyaç doğmuştur. Çalışmada, her bir taşıma görev listesi için ayrı bir rota planlaması yapan bir model sunulmuştur. Model için Hibrid Tavlama Benzetimi Algoritması önerilmiş ve ilgili algoritma Yasaklı Arama Algoritması ile karşılaştırılmıştır. Test problemleri üzerinde yapılan kıyaslamalarda Hibrid Tavlama Benzetimi Algoritmasının daha iyi sonuçlar verdiği görülmüştür.
Anahtar Kelimeler
Otonom Transfer Araçları dağıtım ve toplama görev çizelgeleme hibrid benzetilmiş tavlama algoritması tabu arama algoritması
Destekleyen Kurum
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK).
Proje Numarası
116E731
Teşekkür
Bu proje, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından desteklenmektedir, Sözleşme-No: 116E731, Proje Başlığı: Akıllı Fabrikalar İçin Otonom Taşıyıcılar Ve Gerekli İnsan-Makine Ve Makine-Makine Arayüzlerinin Geliştirilmesi.
Kaynakça
- Alcácer, V., & Cruz-Machado, V. (2019). Scanning the industry 4.0: a literature review on technologies for manufacturing systems. Engineering Science and Technology, an International Journal.
- Winkelhaus, S., & Grosse, E. H. (2019). Logistics 4.0: a systematic review towards a new logistics system. International Journal of Production Research, 1(26).
- Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering, https://doi.org/10.1016/j.eng.2017.05.015.
- Kalinovcic, L., Petrovic, T., Bogdan, S. & Bobanac, V. (2011). Modified Banker’s algorithm for scheduling in multi-agv systems. IEEE - CASE, pp. 351 - 356.
- Vivaldini, K. C. T., Rocha, L. F., Becker, M. & Moreira, A. P. (2015). Comprehensive Review of the Dispatching, Scheduling and Routing of AGVs, Proc. of the 11th Port. Conf. on CONTROLO’2014 , 505 - 514.
- Kumar, A. (2014). Development of an Automated Guided Vehicles in Industrial Environment. International Journal of Mechanical Engineering and Robotics Research (IJMERR), 3(1), 377–391.
- King, R. E. &Wilson, C. (1991). A Review of Automated Guided Vehicle Systems Design and Sheduling, Production Planning and Control, 2 (1), 44-51.
- Maxwell, W. L. (1981). Solving Material Handling Design Problems With OR, Industrial Engineering, 13, 58-69.
- Maxwell, W. L. & Muckstadt, J. A. (1982). Design of Automatic Guided Vehicle Systems, I.I.E. Transactions, 14, 114-124.
- Kuhn, A. & Schmidt, F. (1985). General EDP Aided Planning and Realization of AGV Systems. Proceedings of the 3rd International Conference on AGVs, 247-257.
- Xie, M. (1995). Trinocular Vision for AGV Guidance: Path Locationzation and Obstacle Detection, Computer and Electrical Engineering, 21 (6), 441-452.
- Majety, S. V. &Wang, M. H. (1995). Terminal Location and Guided Path Design in Terminal Based AGV System, International Journal of Production.Research, 33 (7), 1925-1938.
- Hwang, H., Cim, S. V. and Moon, S. W. (1996). Determination of Optimum Unit Load Size.of the AGV in Ana Electronics Assembly Production System, International Journal of Production Research, 34 (5), 1293-1306.
- Hsieh, L. F. & Sha, D.Y. (1997). Heuristic Algorithm for the Design of Facilities Layout and AGV Routes in Tandem AGV Systems, Industrial Journal of Industrial Engineering, 4(1), 52-61.
- Gourgand, M., Sun, X. C. & Tcheinew, N. (1995). Choice of the Guide Path Layout for an AGV Based Material Handling Systems, IEEE, 2, 475-483.
- Ting, J. H., & Tanchoco, J. M. A. 1997. Comparision of Single and Mixed-Model AGV Systems, American Society of Mechanical Engineers, 119 (4), 849-.854.
- Fazlollahtabar, H., & Saidi-Mehrabad, M. (2015). Methodologies to optimize automated guided vehicle scheduling and routing problems: a review study. Journal of Intelligent & Robotic Systems, 77(3-4), 525-545.
- Psaraftis, H. N. (1980). A Dynamic Programming Solution to the Single Vehicle Many-to-many Immediate Request Dial-a-ride Problem, Transportation Science, 14, 130-154.
- Maxwell, W. L. & Muckstadt, J. A. (1982). Design of Automatic Guided Vehicle Systems, I.I.E. Transactions, 14, 114-124.
- Bodin, L., Colden, B., Assad, A. & Ball, M. (1983) Routing and Scheduling of.Vehicles and Crews, Computers and Operations Research, 10, 63-201.
- Ulusoy, G., Sivrikaya-Serifoglu, F., & Bilge, U. (1997). A genetic algorithm approach to the simultaneous scheduling of machines and automated guided vehicles. Computers & Operations Research, https://doi.org/10.1016/s0305-0548(96)00061-5.
- Lee, J. H., Lee, B. H., & Choi, M. H. (1998). A real-time traffic control scheme of multiple AGV systems for collision free minimum time motion: a routing table approach. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, https://doi.org/10.1109/3468.668966.
- Rajotia, S., Shanker, K., & Batra, J. (1998). A semi-dynamic time window constrained routeing strategy in an AGV system. International Journal of Production Research, https://doi.org/10.1080/002075498193921.
- Oboth, C., Batta, R., & Karwan, M. (1999). Dynamic conflict-free routing of automated guided vehicles. International Journal of Production Research, https://doi.org/10.1080/002075499190888.
- Desaulniers, G., Langevin, A., Riopel, D., & Villeneuve, B. (2003). Dispatching and Conflict-Free Routing of Automated Guided Vehicles: An Exact Approach. International Journal of Flexible Manufacturing Systems.
- Abdelmaguid, T. F., Nassef, A. O., Kamal, B. A., & Hassan, M. F. (2004). A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Research, https://doi.org/10.1080/0020754032000123579.
- Lin, L., Shinn, S. W., Gen, M., & Hwang, H. (2006). Network model and effective evolutionary approach for AGV dispatching in manufacturing system. Journal of Intelligent Manufacturing, https://doi.org/10.1007/s10845-005-0019-4.
- Deroussi, L., Gourgand, M., & Tchernev, N. (2008). A simple metaheuristic approach to the simultaneous scheduling of machines and automated guided vehicles. International Journal of Production .
- Lacomme, P., Larabi, M., & Tchernev, N. (2013). Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Economics, https://doi.org/10.1016/j.ijpe.2010.07.012.
- Shneier, M., & Bostelman, R. (2015). Literature Review of Mobile Robots for Manufacturing. NIST Internal Report, https://doi.org/10.6028/nist.ir.8022.
- Xidias E.K., Nearchou A.C. & Aspragathos N.A. (2009), Vehicle scheduling in 2D shop floor environments, Ind. Robot. 36 (2).
- Hussein, A., Mostafa, H., Badrel-Din, M., Sultan, O., & Khamis, A. (2012). Metaheuristic optimization approach to mobile robot path planning. 2012 International Conference on Engineering and Technology (ICET), https://doi.org/10.1109/icengtechnol.2012.6396150.
- Emilio F., Dahleh M.A., & Feron E., Real-time motion planning for agile autonomous vehicles, Journal of Guidance, Control, and Dynamics, 25(1), 116-129, 2002.
- Liu S., Linbo M., & Jinshou Y. (2006) Path planning based on ant colony algorithm and distributed local navigation for multi-robot systems, IEEE 2006 International Conference on Mechatronics and Automation, 1733-1738.
- Herrero-Pérez D. & Martínez-Barberá H. (2010) Modeling distributed transportation systems composed of flexible automated guided vehicles in flexible manufacturing systems. IEEE Transactions on Industrial Informatics. 6(2),166–180.
- Xidias E.K., Paraskevi Z., & Andreas N. (2016) Path Planning and scheduling for a fleet of autonomous vehicles. Robotica, 1- 17.
- Magdy Y., Shehata O. M., AbdelAziz M., Ghoneima M. & Tolbah F. (2017). Metaheuristic optimization in path planning of autonomous vehicles under the ATOM framework, 2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES), 32-37. https://doi.org/10.1109/ICVES.2017.7991897.
- Xidias E.K. (2018) On Designing Near-Optimum Paths on Weighted Regions for an Intelligent Vehicle, International Journal of Intelligent Transportation Systems Research, 1838-8659.
- Cura, T. (2008) Modern sezgisel teknikler ve uygulamaları, Papatya Yayıncılık, İstanbul, 173.
- Eglese, R.W. (1990) Simulated Annealing: A Tool for Operational Research, European Journal of Operational Research, 34, 600-612.
- Metropolis, N., Rosenbluth, A. W., & Teller, A. H. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 1087-1092.
- Glover, F. (1986) Future Paths for Integer Programming and Links to Artificial Intelligence, Computers and Operations Research, 13, 533-549.
Toplam 42 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Proje Numarası | 116E731 |
| Yayımlanma Tarihi | 31 Ağustos 2020 |
| Kabul Tarihi | 12 Mayıs 2020 |
| Yayımlandığı Sayı | Yıl 2020 Cilt: 28 Sayı: 2 |
