Advertisement

Resource-Constrained Scheduling with Non-constant Capacity and Non-regular Activities

Chapter
  • 1.2k Downloads
Part of the Springer Optimization and Its Applications book series (SOIA, volume 114)

Abstract

This work is inspired by very challenging issues arising in space logistics. The problem of scheduling a number of activities, in a given time elapse, optimizing the resource exploitation is discussed. The available resources are not constant, as well as the request, relative to each job. The mathematical aspects are illustrated, providing a time-indexed MILP model. The case of a single resource is analysed first. Extensions, including the multi-resource case and the presence of additional conditions are considered. Possible applications are suggested and an in-depth experimental analysis is reported.

Keywords

Resource constrained project scheduling problem Non-constant resource capacity Non-constant resource request Irregular job/activity/cycle profile Multi-resource Time-indexed scheduling Mixed integer linear programming Global optimization 

Notes

Acknowledgements

The author is very grateful to the two referees whose suggestions contributed to the improvement of the original version of this chapter, significantly. Thanks are also due to Jane Evans for her very valuable support in revising the whole manuscript.

References

  1. 1.
    Agnetis, A., Billaut, J.C., Gawiejnowicz, S., Pacciarelli, D., Soukhal, A.: Multiagent Scheduling. Springer, Berlin (2014)CrossRefGoogle Scholar
  2. 2.
    Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)CrossRefGoogle Scholar
  3. 3.
    Błażewicz, J., Ecker, K.H., Pesch, E., Schmidt, G., Weglarz, J.: Handbook on Scheduling, International Handbooks on Information Systems. Springer, Berlin (2007)zbMATHGoogle Scholar
  4. 4.
    Brucker, P., Knust, S.: Complex Scheduling. Springer, Berlin (2012)CrossRefGoogle Scholar
  5. 5.
    Chen, Z., Chyu, C.: An evolutionary algorithm with multi–local search for the resource-constrained project scheduling problem. Intell. Inf. Manag. 2, 220–226 (2010)Google Scholar
  6. 6.
    Coelho, J., Vanhoucke, M.: Multi-mode resource-constrained project scheduling using RCPSP and SAT solvers. Eur. J. Oper. Res. 213(1), 73–82 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Damak, N., Jarboui, B., Siarry, P., Loukil, T.: Differential evolution for solving multi-mode resource-constrained project scheduling problems. Comput. Oper. Res. 36(9), 2653–2659 (2009)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Ha, D.L., Ploix, S., Zamai, E., Jacomino, M.: Control of energy consumption in home automation by resource constraint scheduling. The 15th International Conference on Control System and Computer Science, Bucharest, Romania, May 25–27 (2005)Google Scholar
  9. 9.
    Fasano, G.: Solving Non-standard Packing Problems by Global Optimization and Heuristics. SpringerBriefs in Optimization. Springer Science+Business Media, New York (2014)CrossRefGoogle Scholar
  10. 10.
    Gonzalez, F., Ramies, R.D.: Multi-objective optimization of the resource constrained project scheduling problem (RCPSP) a heuristic approach based on the mathematical model. Int. J. Comput. Sci. Appl. 2(2), 1–13 (2013)Google Scholar
  11. 11.
    Hartmann, S.: Packing problems and project scheduling models: an integrating perspective. J. Oper. Res. Soc. 51, 1083–1092 (2000)CrossRefGoogle Scholar
  12. 12.
    Hartmann, S.: Project Scheduling Under Limited Resources: Models, Methods, and Applications. Lecture Notes in Economics and Mathematical Systems, vol. 478. Springer, Berlin (2013)zbMATHGoogle Scholar
  13. 13.
    IBM Corporation: ILOG CPLEX Optimizer: High Performance Mathematical Optimization Engines. IBM Corporation Software Group, New York (2010). WSD14044-USEN-01Google Scholar
  14. 14.
    Jaberi, M.: A multi-objective resource-constrained project-scheduling problem using mean field annealing neural networks. J. Math. Comput. Sci. 9, 228–239 (2014)CrossRefGoogle Scholar
  15. 15.
    Kabra, S., Shaik, M.A., Rathore, A.S.: Multi-period scheduling of a multistage multiproduct bio-pharmaceutical process. Comput. Chem. Eng. 52, 95–103 (2013)CrossRefGoogle Scholar
  16. 16.
    Lee, Y.C., Zomaya, A.Y.: Rescheduling for reliable job completion with the support of clouds. Futur. Gener. Comput. Syst. 26, 1192–1199 (2010)CrossRefGoogle Scholar
  17. 17.
    Li, J., Misener, R., Floudas, C.A.: Continuous-time modeling and global optimization approach for scheduling of crude oil operations. AIChE J. 58(1), 205–226 (2012)CrossRefGoogle Scholar
  18. 18.
    Malakooti, B.: Operations and Production Systems with Multiple Objectives. Wiley, Chichester (2013). ISBN 978-1-118-58537-5Google Scholar
  19. 19.
    Pinedo, M.L.: Planning and Scheduling in Manufacturing and Services. Springer, New York (2005)zbMATHGoogle Scholar
  20. 20.
    Shaik, M.A., Floudas, C.A.: Novel unified modeling approach for short-term scheduling. Ind. Eng. Chem. Res. 48, 2947–2964 (2009)CrossRefGoogle Scholar
  21. 21.
    Zhang, Z., Chen, J.: Solving the spatial scheduling problem: a two-stage approach. Int. J. Prod. Res. 50(10), 2732–2743 (2012)CrossRefGoogle Scholar
  22. 22.
    Bidot, J., Vidal, T., Laborie, P., Beck, J.C.: A theoretic and practical framework for scheduling in a stochastic environment. J. Sched. 12(3), 315–344 (2009)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Mendes, J.J.M., Gonvalces, J.F., Resende, M.G.C.: A random key based genetic algorithm for the resource constrained project scheduling problem. Comput. Oper. Res. 36, 92–109 (2009)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Peteghem, V.V., Vanhoucke, M.: A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. Eur. J. Oper. Res. 201(2), 409–418 (2010)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Xu, J.P., Zeng, Z.Q., Han, B., Lei, X.: A dynamic programming-based particle swarm optimization algorithm for an inventory management problem under uncertainty. Eng. Optim. 45(7), 851–880 (2013)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Yannibelli, V., Amandi, A.: Hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem. Expert Syst. Appl. 40, 2421–2434 (2013)CrossRefGoogle Scholar
  27. 27.
    Ziarati, K., Akbari, R., Zeighami, V.: On the performance of bee algorithms for resource-constrained project scheduling problem. Appl. Soft Comput. J. 11(4), 3720–3733 (2011)CrossRefGoogle Scholar
  28. 28.
    van den Akker, J.M., Hurkens, C.A.J., Savelsbergh, M.W.P.: Time-indexed formulations for machine scheduling problems: column generation. Informs J. Comput. 12(2), 111–124 (2000)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Liu, Y., Dong, H., Lohse, N., Petrovic, S., Gindy, N.: An investigation into minimising total energy consumption and total weighted tardiness in job shops. J. Clean. Prod. 65, 87–96 (2014)CrossRefGoogle Scholar
  30. 30.
    Miyamoto, T., Mori, K., Izui, Y., Kitamura, S.: A study of resource constraint project scheduling problem for energy saving. International Conference on System Science and Engineering, ICSSE (2014). doi: 10.1109/ICSSE.2014.6887897
  31. 31.
    Pisinger, D., Sigurd, M.: The two-dimensional bin packing problem with variable bin sizes and costs. Discret. Optim. 2(2), 154–167 (2005)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Castro, P.M., Oliveira, J.F.: Scheduling inspired models for two-dimensional packing problems. Eur. J. Oper. Res. 215, 45–56 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.Exploration and Science, Thales Alenia SpaceTurinItaly

Personalised recommendations