Skip to main content

Advertisement

SpringerLink
Log in

Efficient metaheuristics for pick and place robotic systems optimization

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

Abstract

This paper deals with a pick and place robotic system design problem. The objective is to present an efficient method which is able to optimize the performances of the robotic system. By defining the suitable combination of scheduling rules, our method allows each robot to perform the assigned pick and place operations in real time in order to maximize the throughput rate. For that, we have developed different resolution methods which define the scheduling rule for each robot in order to seize the products from the first side of the system and to place them on the second side. We suggest three metaheuristics which are the ant colony optimization, the particle swarm optimization and the genetic algorithm. Then, we try to select the best algorithm which is able to get the best solutions with the lowest execution times. This is the main advantage of our methods compared to exact methods. This fact represents a great interest taking in consideration that our methods must respect a strong industrial constraint regarding the functioning of a real industrial robotic system. This constraint states that the answer time to manage the seizing strategies of the robots must be less than 1 second. Numerical results show that the different algorithms perform optimally for the tested instances in a reasonable computational time.

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

Similar content being viewed by others

References

  • Ahmadi R. H., Mame J. W. (1999) Routing heuristics for automated pick and place machines. European Journal of Operation Research 117(3): 533–552

    Article  Google Scholar 

  • Atyabi A., Phon-Amnuaisuk S., Ho C. K. (2010) Navigating a robotic swarm in an uncharted 2D landscape. Applied Soft Computing 10: 149–169

    Article  Google Scholar 

  • Ayob, M., & Kendall, G. (2002). A new dynamic point specification approach to optimise surface mount placement machine in printed circuit board assembly. In IEEE ICIT ’02 (Vol. 1, pp. 486–491).

  • Ayob M., Kendall G. (2005) A triple objective function with a Chebychev dynamic pick-and-place point specification approach to optimise the surface mount placement machine. European Journal of Operational Research 164(3): 609–626

    Article  Google Scholar 

  • Belmecheri, F., Prins, C., Yalaoui, F., & Amodeo, L. (2012). Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows. Journal of Intelligent Manufacturing. doi:10.1007/s10845-012-0627-8.

  • Bianchi, R. A. C., Costa, & A. H. R. (2002). Comparing distributed reinforcement learning approaches to learn agent coordination, advances in artificial intelligence—IBERAMIA 2002, 2527/2002, 575–584. doi:10.1007/3-540-36131-6-59.

  • Brauner N., Finke G., Lehoux-Lebacque V., Potts C., Whitehead J. (2009) Scheduling of coupled tasks and one-machine no-wait robotic cells. Computers and Operations Research 36(2): 301–307

    Article  Google Scholar 

  • Chatterjee A., Pulansinghe H., Watanabee K., Izumik K. (2005) A particle swarm optimized fuzzy-neural network for voice- controlled robot systems. IEEE Transactions on Industrial Electronics 52(6): 1478–1489

    Article  Google Scholar 

  • Chehade H., Amodeo L., Yalaoui F. (2009) A new efficient hybrid method for selecting machines and sizing buffers in assembly lines. Journal of Operations and Logistics 16(2): III.1–III.22

    Google Scholar 

  • Daoud, S., Yalaoui, F., Amodeo, L., & Chehade, H. (2010). Ant colony algorithms for robotic systems optimization. In International conference on industrial engineering and manufacturing, Algeria.

  • Dorigo, M. (1992). Optimization learning and natural algorithms, PHD Thesis, Politecnice Di Milano.

  • Eberhart, E. C., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceeding 6th international symposium, micromachine human science (pp. 39–43), Japan.

  • Gecks, T., & Henrich, D. (2005). Human-robot cooperation: Safe pick-and-place operations. In Proceedings of the IEEE International workshop on robots and human interative communication (pp. 549–554). Nashville, USA.

  • Hani Y., Amodeo L., Yalaoui F., Chen H. (2007) Ant colony optimization for solving an industrial layout problem. European Journal of Operational Research 183(2): 633–642

    Article  Google Scholar 

  • Ho W., Ping J. (2010) An integrated scheduling problem of PCB components on sequential pick-and-place machines: Mathematical models and heuristic solutions. Expert Systems with Applications: An International Journal 36: 7002–7010

    Article  Google Scholar 

  • Holland J. H. (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Hong D. S., Cho H. S. (1999) A genetic-algorithm-based approach to the generation of robotic assembly sequences. Control Engineering Practice 7(2): 151–159

    Article  Google Scholar 

  • Kambayashi, Y., Tsujimura, Y., Yamachi, H., Takimoto, M., & Yamamoto, H. (2009). Design of a multi-robot system using mobile agents with ant colony clustering. In International conference on system sciences (pp. 1–10), Hawaii.

  • Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceeding of IEEE international conference neural network (Vol. 4, pp. 1942–1948), Australia.

  • Kennedy, J., Eberhart, R. C., & Shi, Y. H. (2001). Swarm intelligence. San Mateo, CA: Morgann Kaufmann Publishers.

  • Klemmt A., Horn S., Weigert G., Wolter K. J. (2009) Simulation-based optimization vs. mathematical programming: A hybrid approach for optimizing scheduling problems. Robotics and Computer-Integrated Manufacturing 25(6): 917–925

    Article  Google Scholar 

  • Kwok N. M., Liu D. K., Dissanayake G. (2006) Evolutionary computing based mobile robot localization. Engineering Applications of Artificial Intelligence 19: 857–868

    Article  Google Scholar 

  • Levitin G., Rubinovitz J., Shnits B. (2006) A genetic algorithm for robotic assembly line balancing. European Journal of Operational Research 168(3): 811–825

    Article  Google Scholar 

  • Li, S., Hu, C., & Tian, F. (2008). Enhancing optimal feeder assignment of the multi-head surface mounting machine using genetic algorithms. Applied Soft Computing Archive, 8, 522–529. ISSN: 1568-4946.

    Google Scholar 

  • Lin, T.-W., & Wang, C.-H. (2010). A hybrid genetic algorithm to minimize the periodic preventive maintenance cost in series-parallel system. Journal of Intelligent Manufacturing. doi:10.1007/s10845-010-0406-3.

  • Liu, X.-J., Yi, H., & Ni, Z.-H. (2010). Application of ant colony optimization algorithm in process planning optimization. Journal of Intelligent Manufacturing. doi:10.1007/s10845-010-0407-2.

  • Mattone R., Divona M., Wolf A. (2000) Sorting of items on a moving conveyor belt Part 2. Performance evaluation and optimization of pick-and-place operations. Robotics and Computer Integrated Manufacturing 16(16): 81–90

    Article  Google Scholar 

  • Mohamad, M. M., Dunnigan, M. W., & Taylor, N. K. (2005). Ant colony robot motion planning. In Proceedings of EURCON conference (pp. 213–216). Belgrade, Serbia & Montenegro.

  • Nelson A. L., Barlow G. J., Doitsidis L. (2009) Fitness functions in evolutionary robotics: A survey and analysis. Robotics and Autonomous Systems 57(4): 345–370

    Article  Google Scholar 

  • Tuzkaya S., Tuzkaya U. R., Doğaç B. (2008) A particle swarm optimization algorithm for the multiple-level warehouse layout design problem. Computers and Industrial Engineering archive 54(4): 783–799

    Article  Google Scholar 

  • Poli, R. (2007). An analysis of publications on particle swarm optimisation applications, Technical Report CSM-469 (Department of Computer Science, University of Essex, UK).

  • Porta Garcia M. A., Montiel O., Castillo O., Sepúlveda R., Melin P. (2009) Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Applied Soft Computing archive 9(3): 1102–1110

    Article  Google Scholar 

  • Pugh, J., & Martinoli, A. (2008). Distributed adaptation in multi-robot search using particle swarm optimization. In Proceedings of the 10th international conference on the simulation of adaptive behavior, Lecture Notes in Computer Science (pp. 393–402).

  • Ray, R. N., Chatterjee, D., & Gowami, S. K. (2010). A PSO based optimal switching technique for voltage harmonic reduction of multilevel inverter. Expert Systems with Applications, 37(12), 7796–7801.

    Google Scholar 

  • Shi, Y., & Eberhart, R. C. (1998a). A modified particle swarm optimizer. In Proceedings of the IEEE international conference on evolutionary computation (pp. 69–73), Piscataway.

  • Shi, Y., & Eberhart, R. C. (1998b). Parameter selection in particle swarm optimization. In Evolutionary programming, 7, in proceeding EP98 (pp. 591–600). New York: Springer.

  • Son C. (2006) Comparison of optimal motion planning algorithms for intelligent control of robotic part micro-assembly task. International Journal of Machine Tools and Manufacture 46(5): 508–517

    Article  Google Scholar 

  • Su C. T., Fu H. P. (1998) A simulated annealing heuristic for robotics assembly using the dynamic pick-and-place model. Production Planning and Control 9(8): 795–802

    Article  Google Scholar 

  • Van Hop N., Tabucanon M. T. (2001) Extended dynamic point specification approach to sequencing robot moves for PCB assembly. International Journal of Production Research 39(8): 1671–1687

    Article  Google Scholar 

  • Vijay Chakaravarthy, G., & Marimuthu, S. (2011). Performance evaluation of proposed differential evolution and particle swarm optimization algorithms for scheduling m-machine flow shops with lot streaming. Journal of Intelligent Manufacturing. doi:10.1007/s10845-011-0552-2.

  • Wilhelm W. E., Choudhry N. D., Damodaran P. (2007) A model to optimize placement operations on dual-head placement machines. Discrete Optimization 4(2): 232–256

    Article  Google Scholar 

  • Yalaoui, N., Mahdi, H., Amodeo, L., & Yalaoui, F. (2011). A new approach for workshop design. Journal of Intelligent Manufacturing, 22(6), 933–951. doi:10.1007/s10845-009-0368-5.

    Google Scholar 

  • Ye W., Ma, D., & Fan, H. (2006). Path planning for space robot based on the self-adaptive ant colony algorithm. In Systems and control in aerospace and astronautics, 1st international symposium on volume (pp. 30–33).

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

    Article  Google Scholar 

  • Zhan Z. H., Zhang J., Li Y., Chung H. S.-H. (2009) Adaptive particle swarm optimization, systems. IEEE Systems, Man, and Cybernetics Society 39(6): 1362–1381

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institut Charles Delaunay—Laboratoire d’Optimisation des Systèmes Industriels, Université de Technologie de Troyes, UMR-STMR 6279, 12 Rue Marie Curie, BP 2060, 10000, Troyes Cedex, France

    Slim Daoud, Hicham Chehade, Farouk Yalaoui & Lionel Amodeo

  2. Aries Packaging, Technopole de l’Aube en Champagne, 10430, Rosières-Près-Troyes, France

    Slim Daoud

Authors
  1. Slim Daoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hicham Chehade
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Farouk Yalaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lionel Amodeo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hicham Chehade.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Daoud, S., Chehade, H., Yalaoui, F. et al. Efficient metaheuristics for pick and place robotic systems optimization. J Intell Manuf 25, 27–41 (2014). https://doi.org/10.1007/s10845-012-0668-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-012-0668-z

Keywords

Search

Navigation