Journal of Intelligent Manufacturing

, Volume 23, Issue 4, pp 1195–1205 | Cite as

A collaborative scheduling GA for products-packages service within extended selling chains environment

  • Pedro Gomez-Gasquet
  • Raul Rodriguez-Rodriguez
  • Ruben Dario Franco
  • Angel Ortiz-Bas


The theory of network coordination provides theoretical foundations to explain how companies can overcome organizational boundaries and constraints to jointly manage business processes across their selling chains. In particular, this work focuses on Collaborative Scheduling, a collaboration process whereby selling chain trading partners activate either on-line or off-line inter-firm coordination mechanisms to jointly plan production activities in order to deliver the final products to end customers each one of them, being the delivery date as close to the date desired as possible. The problem of collaborative scheduling is formally defined by means of a mathematical model. In the model, the defined objective function has the goal to minimize the total weighted tardiness of the package of products acquired by the clients to be delivered in a specific date. The delivery date of each Product-Package is conditioned by the latest date established by each supplier for each product that forms part of the same one. Besides, having different process times for each product and different penalties for each Product-Package, each supplier can offer a different mix of additional products with different due date. Due to the complexity of the problem a Genetic Algorithm has been the approach taken for its resolution. The GA elements and procedures are defined and the parameters are tuned. Although the major contribution of this work focuses on the algorithmic development of a proposal in the context of operations research that could help to solve the problem also is discussed the environment in which this occurs and that justifies our interest. In order to validate the proposed solutions diverse configurations are presented and the results obtained by means of the GA and some heuristics rules are compared.


Collaborative scheduling Selling chains Multi-supplier scheduling Genetic algorithm 


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  1. Azizoglu M., Kirca O. (1998) Tardiness minimization on parallel machines. International Journal of Production Economic 55: 163–168CrossRefGoogle Scholar
  2. Baker, K. (1995). Elements of sequencing and scheduling. In N. H. Hanover (Ed.), Amos tuck school of business administration dartmouth college.Google Scholar
  3. Brucker, P. (2007). Scheduling algorithms (5th ed., pp. 33–94). Berlin: Springer-VerlagGoogle Scholar
  4. Burton T. T., Boeder S. M. (2003) The lean extended enterprise: Moving beyond the four walls to value stream excellence. J Ross Publishing Inc, Florida, USAGoogle Scholar
  5. Byrne, J. A. (1993). The Virtual Corporation. BusinessWeek, 8th February 1993, New York, USA.Google Scholar
  6. Camarinha-Matos H., Afsarmanesh L. (2005) Collaborative networks: A new scientific discipline. Journal of Intelligent Manufacturing 16(4–5): 439–452CrossRefGoogle Scholar
  7. Camarinha-Matos, L., Afsarmanesh, H., Ollus, M. (eds) (2005) Virtual organizations: Systems and practices. Springer, UKGoogle Scholar
  8. Camarinha-Matos, L., Afsarmanesh, H., Ortiz, A. (eds) (2005) Collaborative networks and their breeding environments IFIP 186. Springer, BerlinGoogle Scholar
  9. Cheng T. C. E., Sin C. C. S. (1990) A state-of-the-art review of parallel-machine scheduling research. European Journal of Operational Research 77: 271–292CrossRefGoogle Scholar
  10. Christopher M. (2003) Logistics and supply chain management: Strategies for reducing cost and improving service (2nd ed.). Financial Times-Prentice Hall, New York, USAGoogle Scholar
  11. Davidow W., Malone M. (1992) The virtual corporation: Structuring and revitalizing the corporation for 21th Century. Harper Collins, New York, USAGoogle Scholar
  12. Dyer J. H. (2000) Collaborative advantage: Winning through extended enterprise supplier networks. Oxford University Press, New YorkGoogle Scholar
  13. Gomez-Gasquet, P., Franco, R. D., Rodriguez, R. & Ortiz, A. (2009). A scheduler for extended supply chains based on a combinatorial auction, Journal of Operations and Logistics, 2(1), V1–V12.Google Scholar
  14. Holland, J. H. (1975). Adaptation in natural and artificial systems. Report of the Systems Analysis Research Group SYS–1/92, University of Dortmund, Department of Computer Science. University Michigan Press, Ann Arbor.Google Scholar
  15. Hu X. F., Bao J. S., Jin Y. (2010) Minimising makespan on parallel machines with precedence constraints and machine eligibility restrictions. International Journal of Production Research 48(6): 1639–1651CrossRefGoogle Scholar
  16. Jackson, J. R. (1995). Scheduling a production line to minimize maximum tardiness. Research Report 43, Management Science Research Project, University of California, Los Angeles.Google Scholar
  17. Kim D. W., Na. D. G., Chen F. (2003) Unrelated parallel machine scheduling wit setup times and total weighted tardiness objective. Robotics and Computer Integrated Manufacturing 19: 173–181CrossRefGoogle Scholar
  18. Ko H. H., Baek J. K., Kang Y. H., Kim S. S. (2004) A scheduling scheme for restricted parallel machines with cycling process. Journal of Korean Institute Industrial Engineering 30: 107–119Google Scholar
  19. Kramer R. M., Tyler T. R. (1995) Trust in organizations: Frontiers of theory and research. Sage Publications, Berkeley, CAGoogle Scholar
  20. Lee Y. H., Pinedo M. (1997) Scheduling jobs on parallel machines with sequence-dependent setup times. European Journal Operational Research 100: 464–474CrossRefGoogle Scholar
  21. Liaw C. F., Lin Y. K., Cheng C. Y., Chen M. (2003) Scheduling unrelated parallel machines to minimize total weighted tardiness. Computer Operations Research 30: 1777–1789CrossRefGoogle Scholar
  22. Macbeth, D. K., Boddy, D., & Wagner, B. (1998). Partnering strategy implementation in the supply chain. In Bittici, U. S., & Carrie, A. S. (Eds.), IFIP Conference Proceedings 129, 291–304, Proceeding of the International Conference of the Manufacturing Value-Chain on Strategic Management of the Manufacturing Value Chain. Kluwer.Google Scholar
  23. Meier J. (1995) The importance of relationship management in establishing successful interorganizational systems. Journal of Strategic Information Systems 4(2): 135–148CrossRefGoogle Scholar
  24. Park M. W. (2000) A genetic algorithm for the parallel-machine total weighted tardiness problem. Journal of Korean Institute Industrial Engineering 26: 183–192Google Scholar
  25. Potts C. N., Kovalyov M. Y. (2000) Scheduling with batching: A review. European Journal of Operational Research 120: 228–249CrossRefGoogle Scholar
  26. Potts C. N., Van Wassenhove L. (1982) Decomposition Algorithm for the single machine total tardiness problem. Operational Research Letters 1: 177–181CrossRefGoogle Scholar
  27. Putnik, G. D., & Cunha, M. M. (2007). Knowledge and technology management in virtual organizations: Issues, Trends, Opportunities and Solutions. IGI Publishing.Google Scholar
  28. Ruiz R., Maroto C., Alcaraz J. (2006) Two new robust genetic algorithms for the flowshop scheduling problem. Omega 34(5): 461–476CrossRefGoogle Scholar
  29. Seuring S., Müller M., Goldbach M., Schneidewind U. (2003) Strategy and organization in supply chains. Physica-Verlag, New York, USAGoogle Scholar
  30. Sheen G. J., Liao L. W (2007) Scheduling machine-dependent jobs to minimize lateness on machines with identical speed under availability constraints. Computer & Operations Research 34(8): 2266–2278CrossRefGoogle Scholar
  31. Su L. H. (2009) Scheduling on identical parallel machines to minimize total completion time with deadline and machine eligibility constraints. International Journal of Advanced Manufacturing Technology 40(5–6): 572–581CrossRefGoogle Scholar
  32. Zhong W. C., Liu J., Xue M. Z., Jiao L. C. (2004) A multiagent genetic algorithm for global numerical optimization. Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics 34(2): 1128–1141CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Pedro Gomez-Gasquet
    • 1
  • Raul Rodriguez-Rodriguez
    • 1
  • Ruben Dario Franco
    • 1
  • Angel Ortiz-Bas
    • 1
  1. 1.Centro de Investigación de Gestión e Ingeniería de la Producción (CIGIP)Universidad Politécnica de ValenciaValenciaSpain

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