A methodology for implementation of mobile robot in adaptive manufacturing environments

Journal of Intelligent Manufacturing

Volumn 28, Issue 5, 2017, Pages 1171-1188

  Nielsen, Izabela ^a   Dang, Quang Vinh ^b   Bocewicz, Grzegorz ^c   Banaszak, Zbigniew ^d  

^a AALBORG UNIVERSITY   (Denmark)

^b Vietnamese German University   (Viet Nam)

^c KOSZALIN UNIVERSITY OF TECHNOLOGY   (Poland)

^d WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Cloud manufacturing; Heuristics; Robotics; Scheduling

Indexed keywords

COMPUTER AIDED MANUFACTURING; GENETIC ALGORITHMS; INFORMATION MANAGEMENT; MACHINE DESIGN; MANUFACTURE; MOBILE ROBOTS; ROBOTICS; ROBOTS; SCHEDULING;

ADAPTIVE MANUFACTURING; CLOUD MANUFACTURING; COMPUTATIONAL EXPERIMENT; HEURISTICS; MANUFACTURING ENVIRONMENTS; MOBILE ROBOT SYSTEMS; PLANNING AND SCHEDULING; PRODUCTION SYSTEM;

ROBOT PROGRAMMING;

EID: 84927549098     PISSN: 09565515     EISSN: 15728145     Source Type: Journal    
DOI: 10.1007/s10845-015-1072-2     Document Type: Article

References (45)

1. Agarwal, A., Shankar, R., & Tiwari, M. K. (2006). Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach. European Journal of Operational Research, 173, 211–225.
2. Ascheuer, N., Escudero, L. F., Grötschel, M., & Stoer, M. A. (1993). Cutting plane approach to the sequential ordering problem (with application to job scheduling in manufacturing). SIAM Journal of Optimization, 3, 25–42.
3. Bocewicz, G., Nielsen, P., Banaszak, Z. A., & Dang, V. Q. (2012). Cyclic steady state refinement: Multimodal processes perspective IFIP. Advances in Information and Communication Technology, 384, 18–26.
4. Carpaneto, G., Dell’Amico, M., & Toth, P. (1995). Exact solution of large-scale, asymmetric traveling salesman problems. ACM Transactions on Mathematical Software, 21, 394–409.
5. Choi, I. C., Kim, S. I., & Kim, H. S. (2003). A genetic algorithm with a mixed region search for the asymmetric traveling salesman problem. Computers and Operations Research, 30, 773–786.
6. Dang, Q. V., Nielsen, I., & Steger-Jensen, K. (2012). Mathematical formulation for mobile robot scheduling problem in a manufacturing cell. In J. Frick & B. T. Laugen (Eds.), Advances in production management systems value networks: Innovation, technologies, and management, IFIP AICT (Vol. 384, pp. 37–44). Heidelberg: Springer.
7. Dang, Q. V., & Nielsen, I. (2013). Simultaneous scheduling of machines and mobile robots. In J. M. Corchado, et al. (Eds.), PAAMS 2013 workshops, CCIS (Vol. 365, pp. 118–128). Heidelberg: Springer.
8. Dang, Q. V., Nielsen, I., Steger-Jensen, K., & Madsen, O. (2014). Scheduling a single mobile robot for part-feeding tasks of production lines. Journal of Intelligent Manufacturing, 25, 1271–1287.
9. Datta, S., Ray, R., & Banerji, D. (2008). Development of autonomous mobile robot with manipulator for manufacturing environment. International Journal of Advanced Manufacturing Technology, 38, 536–542.
10. Do, N. A. D., Nielsen, I. E., Chen, G. & Nielsen, P. (2014). A simulation-based genetic algorithm approach for reducing emissions from import container pick-up operation at container terminal. Annals of Operations Research. doi:10.1007/s10479-014-1636-0.
11. Edan, Y., Flash, T., Peiper, U. M., Shmullevich, I., & Sarig, Y. (1991). Near-minimum-time task planning for fruit-picking robots. IEEE Transactions on Robotics and Automation, 7, 48–55.
12. Gen, M., & Lin, L. (2014). Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey. Journal of Intelligent Manufacturing, 25, 849–866.
13. Geng, X., Chen, Z., Yang, W., Shi, D., & Zhao, K. (2011). Solving the travelling salesman problem based on an adaptive simulated annealing algorithm with greedy search. Applied Soft Computing, 11, 3680–3689.
14. Germs, R., Goldengorin, B., & Turkensteen, M. (2012). Lower tolerance-based branch and bound algorithms for the ATSP. Computer and Operations Research, 39, 291–298.
15. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. New York: Addison-Wesley.
16. Hentout, A., Bouzouia, B., Akli, I., & Toumi, R. (2010). Mobile manipulation: A case study. In A. Lazinica & K. Hiroyuki (Eds.), Robot manipulators (pp. 145–167). Rijeka: New Achievements, INTECH.
17. High Level Group. (2006). Manufuture: Strategic research agenda. Luxembourg: High Level Group.
18. Hurink, J., & Knust, S. (2002). A tabu search algorithm for scheduling a single robot in a job-shop environment. Discrete Applied Mathematics, 119, 181–203.
19. Hvilshøj, M., Bøgh, S. (2011).‘Little Helper’ —An autonomous industrial mobile manipulator concept. International Journal of Advanced Robotic Systems. doi:10.5772/10579.
20. Hvilshøj, M., Bøgh, S., Nielsen, O. S., & Madsen, O. S. (2012). Multiple part feeding-real-world application for mobile manipulators. Assembly Automation, 32, 62–71.
21. Kastalli, I. V., & Van Looy, B. (2013). Servitization: Disentangling the impact of service business model innovation on manufacturing firm performance. Journal of Operations Management, 31, 169–180.
22. Katz, D., Kenney, J., Brock, O. (2008). How can robots succeed in unstructured environments?. In Workshop on robot manipulation: Intelligence in human environments at robotics: Science and Systems. Zurich, Switzerland.
23. Khalfallah, M., Figay, N., Ferreira Da Silva, C., & Ghodous, P. (2014). A cloud-based platform to ensure interoperability in aerospace industry. Journal of Intelligent Manufacturing. doi:10.1007/s10845-014-0897-4.
24. Li, D. X. (2011). Enterprise systems: State-of-the-art and future trends. IEEE Transactions on Industrial Informatics, 7, 630–640.
25. Lian, D., & Li, D. X. (2012). Business intelligence for enterprise systems: A survey. IEEE Transactions on Industrial Informatics, 8, 679–687.
26. Lin, L., Hao, X.-C., Gen, M., & Jo, J.-B. (2012). Network modeling and evolutionary optimization for scheduling in manufacturing. Journal of Intelligent Manufacturing, 23, 2237–2253.
27. 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, 17, 465–477.
28. Liu, F., & Zeng, G. (2009). Study of genetic algorithm with reinforcement learning to solve the TSP. Expert Systems with Applications, 36, 6995–7001.
29. Maimon, O., Braha, D., & Seth, V. (2000). A neural network approach for a robot task sequencing problem. Artificial Intelligence in Engineering, 14, 175–189.
30. Mekid, S., Schlegel, T., Aspragathos, N., & Teti, R. (2007). Foresight formulation in innovative production, automation and control systems. Foresight, 9, 35–47.
31. Moon, C., Kim, J., Choi, G., & Seo, Y. (2002). An efficient genetic algorithm for the traveling salesman problem with precedence constraints. European Journal of Operational Research, 140, 606–617.
32. Ohlmann, J. W., & Thomas, B. W. (2007). A compressed-annealing heuristic for the traveling salesman problem with time windows. INFORMS Journal on Computing, 19, 80–90.
33. Potvin, J. Y. (1996). Genetic algorithms for the traveling salesman problem. Annals of Operations Research, 63, 339–390.
34. Rusinko, C. A. (2007). Green manufacturing: An evaluation of environmentally sustainable manufacturing practices and their impact on competitive outcomes. IEEE Transactions on Engineering Management, 54, 445–454.
35. Silver, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory management and production planning and scheduling. New York: John Wiley and Sons.
36. Sitek, P. & Wikarek, J. (2013). A hybrid approach to modeling and optimization for supply chain management with multimodal transport, IEEE Conference: 18th International Conference on Methods and Models in Automation and Robotics (MMAR). pp. 777–782.
37. Snyder, L. V., & Daskin, M. S. (2006). A random-key genetic algorithm for the generalized traveling salesman problem. European Journal of Operational Research, 174, 38–53.
38. Stopp, A., Horstmann, S., Kristensen, S., & Lohnert, F. (2003). Towards interactive learning for manufacturing assistants. IEEE Transactions on Industrial Electronics, 50, 705–707.
39. Tao, F., Zhang, L., Venkatesh, V. C., Luo, Y., & Cheng, Y. (2011). Cloud manufacturing: a computing and service-oriented manufacturing model. Proceedings of the Institution of Mechanical Engineers. Part B: Journal of Engineering Manufacture, 225, 1969–1976.
40. Turkensteen, M., Ghosh, D., Goldengorin, B., & Sierksma, G. (2008). Tolerance-based branch and bound algorithms for the ATSP. European Journal of Operational Research, 189, 775–788.
41. Wang, S., Liu, Z., Sun, Q., Zou, H., & Yang, F. (2014). Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing. Journal of Intelligent Manufacturing, 25, 283–291.
42. Wu, D., Greer, M. J., Rosen, D. W., & Schaefer, D. (2013). Cloud manufacturing: Strategic vision and state-of-the-art. Journal of Manufacturing Systems, 32, 564–579.
43. Xing, L. N., Chen, Y. W., Yang, K. W., Hou, F., Shen, X. S., & Cai, H. P. (2008). A hybrid approach combining an improved genetic algorithm and optimization strategies for the asymmetric traveling salesman problem. Engineering Applications of Artificial Intelligence, 21, 1370–1380.
44. Zacharia, P Th, & Aspragathos, N. A. (2005). Optimal robot task scheduling based on genetic algorithms. Robotics and Computer-Integrated Manufacturing, 21, 67–79.
45. Zhang, Y., Zhang, G., Liu, Y., & Hu, D. (2015). Research on services encapsulation and virtualization access model of machine for cloud manufacturing. Journal of Intelligent Manufacturing. doi:10.1007/s10845-015-1064-2.