Application of Fuzzy Logic and Genetic Algorithms in Automated Works Transport Organization

Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 620)

Abstract

The paper deals with the problem of works transport organization and control by artificial intelligence with respect to path routing for an automated guided vehicle (AGV). The presented approach is based on non-changeable path during travel along a given loop. The ordered set of stations requesting transport service was determined by fuzzy logic, while the sequence of stations in a loop was optimized by genetic algorithms. A solution for both AGV’s and semi-autonomous transport vehicles wherein the control system informs the driver about optimal route was presented. The obtained solution was verified by a computer simulation.

Keywords

works transport control tandem loop AGV path optimization artificial intelligence fuzzy logic genetic algorithms 
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References

  1. 1. Burduk, A., Musiał K.: Optimization of chosen transport task by using generic algorithms. Lecture Notes in Computer Science. 9842, 197–205 (2016)Google Scholar
  2. 2. Kowalski, A., Marut, T.: Hybrid Methods Aiding Organisational and Technological Production Preparation Using Simulation Models of Nonlinear Production Systems. Lecture Notes in Computer Science. 7209, 259–266 (2012)Google Scholar
  3. 3. Gola, A.: Economic Aspects of Manufacturing Systems Design, Actual Probl. Econ. 6 (156), 205–212 (2014)Google Scholar
  4. 4. Rudawska, A., Čuboňova, N., Pomarańska, K., Stanečková, D, Gola, A.: Technical and Organizational Improvements of Packaging Production Processes. Advances in Science and Technology. Research Journal. 10 (30), 182–192 (2016)Google Scholar
  5. 5. Juszczyński, M., Kowalski, A.: Achieving Desired Cycle Times by Modelling Production Systems. Lecture Notes in Computer Sciences. 8104, 476–486, (2013)Google Scholar
  6. 6. Relich, M.: Portfolio selection of new product projects: a product reliability perspective. Eksploatacja i Niezawodnosc-Maintenance and Reliability. 18(4), 613–620 (2016)Google Scholar
  7. 7. Relich, M., Świć, A., Gola A.: A Knowledge-Based Approach to Product Concept Screening. Advances in Intelligent Systems and Computing. 373, 341–348 (2015)Google Scholar
  8. 8. Kłosowski, G., Gola, A., Świć, A.: Application of Fuzzy Logic Controller for Machine Load Balancing in Discrete Manufacturing System. Lecture Notes in Computer Science. 9375, 256–263 (2015)Google Scholar
  9. 9. Jasiulewicz-Kaczmarek, M., Saniuk, A., Nowicki, T.: The maintenance management in the macro-ergonomics context. Advances in Intelligent Systems and Computing. 487, 35–46 (2016)Google Scholar
  10. 10. Bocewicz, G.: Robustness of Multimodal Transportation Networks. Eksploatacja i Niezawodnosc – Maintenance and Reliability. 16 (2), 259–269 (2014)Google Scholar
  11. 11. Kłosowski, G., Gola A., Świć, A.: Application of Fuzzy Logic in Assigning Workers to Production Tasks. Advances in Intelligent Systems and Computing. 474, 505–513 (2016)Google Scholar
  12. 12. Fazlollahtabar, H., Saidi-Mehrabad, M., Balakrishnan, J.: Mathematical optimization for earliness/tardiness minimization in a multiple automated guided vehicle manufacturing system via integrated heuristic algorithms. Robotics and Autonomous Systems, 72, 131–138 (2015)Google Scholar
  13. 13. Qiu, L., Hsu, W.-J., Huang, S.-Y, Wang, H.: Scheduling and routing algorithms for AGVs: a survey. International Journal of Production Research. 40 (3), 745–760 (2002)Google Scholar
  14. 14. Sitek, P.: A hybrid approach to the two-echelon capacitated vehicle routing problem (2ECVRP), Advances in Intelligent Systems and Computing. 267, 251–263 (2014)Google Scholar
  15. 15. Fan, X., He, Q., Zhang, Y.: Zone Design of Tandem Loop AGVs Path with Hybrid Algorithm. IFAC-PapersOnLine ,48 (3), 869–874 (2015)Google Scholar
  16. 16. Saidi-Mehrabad, M., Dehnavi-Arani, S., Evazabadian, F., Mahmoodian, V.: An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflictfree routing of AGVs. Computers and Industrial Engineering. 86, 2–13 (2015)Google Scholar
  17. 17. Asef-Vaziri, A., Laporte, G., Sriskandarajah, C.: The block layout shortest loop design problem. IIE Transactions 32: 727–734 (2000)Google Scholar
  18. 18. Kłosowski, G., Gola, A.: Risk-based estimation of manufacturing order costs with artificial intelligence, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), 729–732 (2016)Google Scholar
  19. 19. Aized, T.: Modelling and performance maximization of an integrated automated guided vehicle system using coloured Petri net and response surface methods. Computers and Industrial Engineering. 57. 822–831 (2009)Google Scholar
  20. 20. Nishi, T., Ando, M., Konishi, M.: Experimental studies on a local rescheduling procedure for dynamic routing of autonomous decentralized AGV systems. Robotics and Computer-Integrated Manufacturing. 22, 154–165 (2006)Google Scholar
  21. 21. Fazlollahtabar, H., Saidi-Mehrabad M., Balakrishnan, J.: Mathematical optimization for earliness/tardiness minimization in a multiple automated guided vehicle manufacturing system via integrated heuristic algorithms. Robotics and Autonomous Systems 72: 131–138 (2015).Google Scholar
  22. 22. Rexing, B., Barnhart, C., Kniker, T., Jarrach, A., Krishnamurthy N.: Airline fleet assignment with time windows. Transportation Science. 34 (1), 1–20 (2000)Google Scholar
  23. 23. Pillac, V., Gendreau, M., Gueret, C., Medaglia A.L.: A review of dynamic vehicle routing problems. European Journal of Operational Research. 225 (1), 1–11 (2013).Google Scholar
  24. 24. Sitek, P., Wikarek, J.: A Hybrid Programming Framework for Modeling and Solving Constraint Satisfaction and Optimization Problems, Scientific Programming vol. 2016, Article ID 5102616 (2016)Google Scholar
  25. 25. Taniguchi, E., Shimamoto, H.: Intelligent transportation system based dynamic vehicle routing and scheduling with variable travel times. Transp. Research Part C. 12, 235–250 (2004).Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Institute of Technological Systems of Information, Faculty of Mechanical EngineeringLublin University of TechnologyLublinPoland
  2. 2.Department of Enterprise Organization, Faculty of ManagementLublin University of TechnologyLublinPoland

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