A Concept of Decision Support for Robust Resource—Constrained Scheduling Problems Using Hybrid Approach
Resource-constrained scheduling problems appear at different levels of decisions in logistics, manufacturing, computer networks, software engineering etc. They are usually characterized by many types of constraints and decision variables which often make them difficult to solve (NP-complete). In addition, these problems are often characterized by the uncertainty of resources, allocations and time. Opportunity to ask questions and get answers about the feasibility/optimality of a schedule in uncertain conditions (e.g. about available resources) is extremely important for decision-makers.
This paper presents a hybrid approach to modeling and solving robust constrained scheduling problems where two environments (mathematical programming and constraint logic programming) were integrated. This integration, hybridization as well as a transformation of the problem helped reduce the combinatorial problem substantially.
In order to compare the effectiveness of the proposed approach to the mathematical programming approach, illustrative example was implemented in both environments for the same data instances.
KeywordsConstraint logic programming Mathematical programming Scheduling Decision support Hybrid approach Robust scheduling
- 5.Sitek, P., Wikarek, J.: A hybrid approach to the optimization of multiechelon systems. In: Mathematical Problems in Engineering, Article ID 925675. Hindawi Publishing Corporation (2014). doi: 10.1155/2014/925675
- 6.Sitek, P., Nielsen I.E., Wikarek, J.: A hybrid multi-agent approach to the solving supply chain problems. In: Procedia Computer Science KES, pp. 1557–1566 (2014)Google Scholar
- 9.Achterberg, T., Berthold, T., Koch, T., Wolter, K.: Constraint integer programming, a new approach to integrate CP and MIP. Lect. Notes Comput. Sci. 5015, 6–20 (2008)Google Scholar
- 11.Blazewicz, J., Lenstra, J.K., Rinnooy Kan, A.H.G.: Scheduling subject to resource constraints: classification and complexity. Discrete Appl. Math. 5, 11–24 (1983)Google Scholar
- 13.Sitek, P.: A hybrid CP/MP approach to supply chain modelling, optimization and analysis. In: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, pp. 1345–1352. doi: 10.15439/2014F89 (2014)
- 14.Eclipse, Eclipse—The Eclipse Foundation open source community website. Accessed May 4, www.eclipse.org (2015)
- 15.Lindo Systems INC, LINDO™ Software for Integer Programming, Linear Programming, Nonlinear Programming, Stochastic Programming, Global Optimization, Accessed May 4, www.lindo.com (2015)
- 18.Krenczyk D., Jagodzinski J.: ERP, APS and simulation systems integration to support production planning and scheduling. In: Advances in Intelligent Systems and Computing, vol. 368, pp. 451–461. Springer International Publishing (2015)Google Scholar
- 19.Gola, A., Świeć, A.: Computer-aided machine tool selection for focused flexibility manufacturing systems using economical criteria. Actual Prob. Econ. 10(124), 383–389 (2011)Google Scholar
- 20.Bocewicz, G., Nielsen, P., Banaszak, Z., Dang, V.Q.: Cyclic steady state refinement: multimodal processes perspective. In: IFIP Advances in Information and Communication Technology, vol. 384, pp. 18–26. AICT (2012)Google Scholar
- 21.Bąk, S., Czarnecki R., Deniziak S.: Synthesis of real-time cloud applications for internet of things. Turk. J. Electr. Eng. Comput. Sci. doi: 10.3906/elk-1302-178 (2013)