Skip to main content

Advertisement

SpringerLink
Log in

Scheduling a single mobile robot for part-feeding tasks of production lines

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

This study deals with the problem of sequencing feeding tasks of a single mobile robot which is able to provide parts for feeders of machines on production lines. The mobile robot has to be scheduled in order to stoppage from lack of parts in the production line. A method based on the characteristics of feeders and inspired by the (\(s,Q\)) inventory system, is thus applied to define time windows for the feeding tasks of the robot. The capacity of the robot is also taken into consideration. The performance criterion is to minimize total traveling time of the robot for a given planning horizon. A genetic algorithm-based heuristics is presented which results in a significant increase in the speed of finding near-optimal solutions. To evaluate the performance of the genetic algorithm-based heuristic, a mixed-integer programming model has been developed for the problem. A case study is implemented at an impeller production line in a real factory and computational experiments are also conducted to demonstrate the effectiveness of the proposed approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Ascheuer, N., Escudero, L. F., Grotschel, 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(1), 25–42.

    Article  Google Scholar 

  • Askin, R. G., & Stanridge, C. R. (1993). Modeling and analysis of manufacturing systems. New York: Wiley.

    Google Scholar 

  • Carlton, W. B., & Barnes, J. W. (1996). Solving the traveling salesman problem with time windows using tabu search. IIE Transactions, 28(8), 617–629.

    Google Scholar 

  • Carpaneto, G., & Toth, P. (1980). Some new branching and bounding criteria for the asymmetric travelling salesman problem. Management Science, 26(7), 736–743.

    Article  Google Scholar 

  • Carpaneto, G., Dell’Amico, M., & Toth, P. (1995). Exact solution of large-scale, asymmetric traveling salesman problems. ACM Transactions on Mathematical Software, 21(4), 394–409.

    Article  Google Scholar 

  • Chatterjee, S., Carrera, C., & Lynch, L. A. (1996). Genetic algorithms and traveling salesman problems. European Journal of Operational Research, 9, 490–510.

    Article  Google Scholar 

  • Chen, S. M., & Chien, C. Y. (2011). Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques. Expert Systems with Applications, 38(12), 14439–14450.

    Article  Google Scholar 

  • Chen, C., & Tseng, C. (1996). The path and location planning of workpieces by genetic algorithms. Journal of Intelligent Manufacturing, 7(1), 69–76.

    Article  Google Scholar 

  • 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(5), 773–786.

    Article  Google Scholar 

  • Crama, Y., Kats, V., van de Klundert, J., & Levner, E. (2000). Cyclic scheduling in robotic flow shops. Annals of Operations Research, 96(1–4), 97–124.

    Article  Google Scholar 

  • Dang, Q. V., Nielsen, I., & Steger-Jensen, K. (2011). Scheduling a single mobile robot for feeding tasks in a manufacturing cell. In Proceedings of international conference advances in production management systems, Stavanger, Norway

  • Dang, Q. V., Nielsen, I., & Bocewicz, G. (2012a). Trends in PAAMS, AISC 157. In J. M. C. Rodríguez (Ed.), A genetic algorithm-based heuristic for part-feeding mobile robot scheduling problem (pp. 85–92). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Dang, Q. V., Nielsen, I., & Steger-Jensen, K. (2012b). Ecoproduction & logistics—new trends and business practices. In P. Golinska (Ed.), Scheduling a single mobile robot incorporated into production environment (pp. 185–201). Berlin, Heidelberg: Springer.

    Google Scholar 

  • 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(1), 48–55.

    Article  Google Scholar 

  • 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(4), 3680–3689.

    Article  Google Scholar 

  • Germs, R., Goldengorin, B., & Turkensteen, M. (2012). Lower tolerance-based branch and bound algorithms for the ATSP. Computer and Operations Research, 39(2), 291–298.

    Article  Google Scholar 

  • Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. New York: Addison-Wesley.

    Google Scholar 

  • Han, M. H., McGinnis, L. F., Shieh, J. S., & White, J. A. (1987). On sequencing retrievals in an automated storage/retrieval system. IIE Transactions, 19(1), 56–66.

    Article  Google Scholar 

  • Hasegawa, M., & Ikeguchi, T. (2002). Solving large scale traveling salesman problems by chaotic neurodynamics. Neural Networks, 15(2), 271–283.

    Article  Google Scholar 

  • Ho, W., & Ji, P. (2004). A hybrid genetic algorithm for component sequencing and feeder arrangement. Journal of Intelligent Manufacturing, 15(3), 307–315.

    Article  Google Scholar 

  • Hurink, J., & Knust, S. (2002). A tabu search algorithm for scheduling a single robot in a job-shop environment. Discrete Applied Mathematics, 119(1–2), 181–203.

    Article  Google Scholar 

  • Landrieu, A., Mati, Y., & Binder, Z. (2001). A tabu search heuristic for the single vehicle pickup and delivery problem with time windows. Journal of Intelligent Manufacturing, 12(5–6), 497–508.

    Article  Google Scholar 

  • 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(4), 465–477.

    Article  Google Scholar 

  • Liu, F., & Zeng, G. (2009). Study of genetic algorithm with reinforcement learning to solve the TSP. Expert Systems with Applications, 36(3), 6995–7001.

    Article  Google Scholar 

  • López-Ibáñez, M., & Blum, C. (2010). Beam-ACO for the travelling salesman problem with time windows. Computers and Operations Research, 37(9), 1570–1583.

    Article  Google Scholar 

  • Maimon, O., Braha, D., & Seth, V. (2000). A neural network approach for a robot task sequencing problem. Artificial Intelligence in Engineering, 14(2), 175–189.

    Article  Google Scholar 

  • Miller, D. L., & Pekny, J. F. (1991). Exact solution of large asymmetric traveling salesman problems. Science, 251(4995), 754–761.

    Article  Google Scholar 

  • 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(3), 606–617.

    Article  Google Scholar 

  • Moon, C., Seo, Y., Yun, Y., & Gen, M. (2006). Adaptive genetic algorithm for advanced planning in manufacturing supply chain. Journal of Intelligent Manufacturing, 17(4), 509–522.

    Article  Google Scholar 

  • Ohlmann, J. W., & Thomas, B. W. (2007). A compressed-annealing heuristic for the traveling salesman problem with time windows. INFORMS Journal on Computing, 19(1), 80–90.

    Article  Google Scholar 

  • Potvin, J. Y. (1996). Genetic algorithms for the traveling salesman problem. Annals of Operation Research, 63(3), 339–370.

    Google Scholar 

  • Ratliff, H. D., & Rosenthal, A. S. (1983). Order-picking in a rectangular warehouse: A solvable case of the traveling salesman problem. Operations Research, 31(3), 507–521.

    Google Scholar 

  • Reuther, H., Simon, B. & Mads, H. (2010). 3D simulation used for evaluating the use and implementation of the mobile manipulator ”Little Helper” at Grundfos A/S. In H. Hvolby (Ed.), Proceedings of the 12th international modern information technology in the innovation processes of the industrial enterprises ( MITIP) conference, 29–31 August 2010 (pp. 9–18). Aalborg: Centre for Logistics, Aalborg University.

  • Silver, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory management and production planning and scheduling. New York: Wiley.

    Google Scholar 

  • Snyder, L. V., & Daskin, M. S. (2006). A random-key genetic algorithm for the generalized traveling salesman problem. European Journal of Operational Research, 174(1), 38–53.

    Article  Google Scholar 

  • Suárez, R., & Rosell, J. (2005). Feeding sequence selection in a manufacturing cell with four parallel machines. Robotics and Computer-Integrated Manufacturing, 21(3), 185–195.

    Article  Google Scholar 

  • Syslo, M. M., Deo, N., & Kowalik, J. S. (1983). Discrete optimization algorithms: With Pascal programs. New Jersey: Prentice-Hall.

    Google Scholar 

  • Toth, P., & Vigo, D. (2002). The vehicle routing problem. Philadelphia: SIAM.

    Book  Google Scholar 

  • Tsai, C. F., Tsai, C. W., & Tseng, C. C. (2004). A new hybrid heuristic approach for solving large traveling salesman problem. Information Sciences, 166(1–4), 67–81.

    Article  Google Scholar 

  • Tsujimura, Y., & Gen, M. (1999). Parts loading scheduling in a flexible forging machine using an advanced genetic algorithm. Journal of Intelligent Manufacturing, 10(2), 149–159.

    Article  Google Scholar 

  • Turkensteen, M., Ghosh, D., Goldengorin, B., & Sierksma, G. (2008). Tolerance-based branch and bound algorithms for the ATSP. European Journal of Operational Research, 189(3), 775–788.

    Article  Google Scholar 

  • 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(8), 1370–1380.

    Article  Google Scholar 

  • Zacharia, P Th, & Aspragathos, N. A. (2005). Optimal robot task scheduling based on genetic algorithms. Robotics and Computer-Integrated Manufacturing, 21(1), 67–79.

    Article  Google Scholar 

Download references

Acknowledgments

This work has partly been supported by the European Commission under grant agreement number FP7-260026-TAPAS.

Author information

Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, Aalborg University, Fibigerstræde 16, 9220 , Aalborg, Denmark

    Quang-Vinh Dang, Izabela Nielsen, Kenn Steger-Jensen & Ole Madsen

Authors
  1. Quang-Vinh Dang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Izabela Nielsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kenn Steger-Jensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ole Madsen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Izabela Nielsen.

Appendix

Appendix

  • Probability of crossover \(P_{c}\) (Tables 9, 10).

Table 9 ANOVA for \(P_{c}\)
Full size table
Table 10 Objective value and computation time at four levels of \(P_{c}\)
Full size table

The average and standard deviation of the objective value is the best when \(P_{c}\) is equal to 0.8 or 1.0. However, only slight improvement on the objective value is achieved with longer computation time when \(P_{c}\) increases from 0.8 to 1.0. The value of \(P_{c}\) is hence set to be 0.8.

  • Probability of mutation \(P_{m}\) (Tables 11, 12).

Table 11 ANOVA for \(P_{m}\)
Full size table
Table 12 Objective value and computation time at four levels of \(P_{m}\)
Full size table

There are improvements on the average of the objective value when \(P_{m}\) increases from 0.05 to 0.2. However, these improvements are minor in comparison with the increase of the computation time. Furthermore, the standard deviations of the objective value at \(P_{m}\) of 0.1 and 0.2 are better than the other values of \(P_{m}\). The value of \(P_{m}\) is hence set to be 0.1.

  • No-improvement consecutive generations \(G_{c}\) (Tables 13, 14).

Table 13 ANOVA for \(G_{c}\)
Full size table
Table 14 Objective value and computation time at four levels of \(G_{c}\)
Full size table

Similar to the case of \(N_{p}\), the value of \(G_{c}\) is set to be 100.

  • Maximum computation time \(CT_{m}\) (Tables 15, 16).

There are considerable improvements on the average of the objective value when \(CT_{m}\) increases from 15 to 60 s (the best average is achieved at \(CT_{m}\) of 60). Furthermore, the standard deviation of the objective value at \(CT_{m}\) of 60 is better than the other values of \(CT_{m}\) except the case of 30. Note that the value of \(CT_{m}\) is set based on the improvements on the objective value because the ANOVA of \(CT_{m}\) shows that this parameter strongly affects the objective value (\(F = 22.560 > 2.866\)). The value of \(CT_{m}\) is hence set to be 60 (a problem instance of larger size is randomly generated to test \(CT_{m}\)).

Table 15 ANOVA for \(CT_{m}\)
Full size table
Table 16 Objective value and computation time at four levels of \(CT_{m}\)
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dang, QV., Nielsen, I., Steger-Jensen, K. et al. Scheduling a single mobile robot for part-feeding tasks of production lines. J Intell Manuf 25, 1271–1287 (2014). https://doi.org/10.1007/s10845-013-0729-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-013-0729-y

Keywords

Search

Navigation