A Constraint-Based Framework for Scheduling Problems

  • Jarosław Wikarek
  • Paweł Sitek
  • Tadeusz Stefański
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10751)


Scheduling and resource allocation problems are widespread in many areas of today’s technology and management. Their different forms and structures appear in production, logistics, software engineering, computer networks, etc. In practice, however, classical scheduling problems with fixed structures and only standard constraints (precedence, disjoint etc.) are rare. Practical scheduling problems include also logical and non-linear constraints and use non-standard criteria of schedule evaluations. In many cases, decision makers are interested in the feasibility and/or optimality of a given schedule for specified conditions formulated as questions, for example, Is it possible…?, What is the minimum/maximum…?, What if..? etc. Thus there is a need to develop a programming framework that will facilitate the modeling and solving a variety of diverse scheduling problems. This paper proposes such a constraint-based framework for modeling and solving scheduling problems. It was built with the CLP (Constraint Logic Programming) environment and supported with MP (Mathematical Programming).


Scheduling Constraint logic programming Mathematical programming optimization Hybrid methods Decision support systems 


  1. 1.
    Błażewicz, J., Ecker, K.H., Pesch, E., Schmidt, G., Weglarz, J.: Handbook on Scheduling From Theory to Applications. Springer, Heidelberg (2007). ISBN:978-3-540-28046-0zbMATHGoogle Scholar
  2. 2.
    Schrijver, A.: Theory of Linear and Integer Programming. Wiley, New York (1998)zbMATHGoogle Scholar
  3. 3.
    Milano, M., Wallace, M.: Integrating operations research. Constraint Program. Ann. Oper. Res. 175(1), 37–76 (2010)CrossRefzbMATHGoogle Scholar
  4. 4.
    Hooker, J.N.: Logic, optimization and constraint programming. INFORMS J. Comput. 14, 295–321 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Sitek, P., Wikarek, J.: A hybrid programming framework for modeling and solving constraint satisfaction and optimization problems. Sci. Program. 2016, 13 (2016). Article ID 5102616Google Scholar
  6. 6.
    Sitek, P., Wikarek, J., Nielsen, P.: A constraint-driven approach to food supply chain management. Ind. Manag. Data Syst. 117(9) (2017). Article ID 600090
  7. 7.
    Graham, R.L., Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G.: Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann. Discrete Math. 4, 287–326 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Coelho, J., Vanhoucke, M.: Multi-mode resource-constrained project scheduling using RCPSP and SAT solvers. Eur. J. Oper. Res. 213, 73–82 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Rossi, F., Van Beek, P., Walsh, T.: Handbook of Constraint Programming (Foundations of Artificial Intelligence). Elsevier Science Inc., New York (2006)zbMATHGoogle Scholar
  10. 10.
    Achterberg, T., Berthold, T., Koch, T., Wolter, K.: Constraint integer programming: a new approach to integrate CP and MIP. In: Perron, L., Trick, M.A. (eds.) CPAIOR 2008. LNCS, vol. 5015, pp. 6–20. Springer, Heidelberg (2008). CrossRefGoogle Scholar
  11. 11.
    Eclipse – home. Accessed 5 July 2017

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Management and Control SystemsKielce University of TechnologyKielcePoland

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