Abstract
Many problems occurring in production, transport, supply chains and everyday life problems can be formulated in the form of constraint optimization problems (COPs). Most often these are issues related to planning and scheduling, distribution of resources, fleet selection, route and network optimization, configuration of machines and manufacturing systems, timetabling, etc. In the vast majority of cases, these are discrete problems of a combinatorial nature. Significant difficulties in modelling and solving COPs are usually the magnitude of real problems, which translates into a large number of variables and constraints as well as high computational complexity (usually NP-hard problems). The article proposes a data-driven approach, which allows a significant reduction in the magnitude of modelled problems and, consequently, the possibility of solving many real problems in an acceptable time.
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References
Tang, B., Zhu, Z., Luo, J.: A framework for constrained optimization problems based on a modified particle swarm optimization. Math. Probl. Eng. 2016, Article no. 8627083, 19 pages. http://dx.doi.org/10.1155/2016/8627083
Antoniou, A., Lu, W.-S.: Practical Optimization Algorithms and Engineering Applications. Springer, New York (2007)
Dash, S., Tripathy, B.K., Rehman, A.: Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms. IGI GLOBAL. ISBN 9781522528579
Sitek, P., Wikarek, J.: A hybrid programming framework for modeling and solving constraint satisfaction and optimization problems. Sci. Program. 2016, Article no. 5102616 (2016). https://doi.org/10.1155/2016/5102616
Home – AMPL. https://ampl.com/. Accessed 19 Oct 2018
MIPLIB – Mixed Integer Problem. http://miplib.zib.de. Accessed 19 Oct 2018
Apt, K., Wallace, M.: Constraint Logic Programming using Eclipse. Cambridge University Press, New York (2006)
DÃaz-Parra, O., Ruiz-Vanoye, J.A., Loranca, B.B., Fuentes-Penna, A., Barrera-Cámara, R.A.: A survey of transportation problems. Hindawi Publ. Corp. J. Appl. Math. 2014, Article no. 848129, 17 pages. http://dx.doi.org/10.1155/2014/848129
Wikarek, J.: Implementation aspects of hybrid solution framework. In: Recent Advances in Automation, Robotics and Measuring Techniques, vol. 267, pp. 317–328 (2014). https://doi.org/10.1007/978-3-319-05353-0_31
Home LINDO. www.lindo.com. Accessed 19 Oct 2018
Eclipse - The Eclipse Foundation open source community. www.eclipse.org. Accessed 19 Oct 2018
Sitek, P., Wikarek, J.: A multi-level approach to ubiquitous modeling and solving constraints in combinatorial optimization problems in production and distribution. Appl. Intell. 48, 1344–1364 (2018). 10.1007/s10489-017-1107-9
Nielsen, I., Dang, Q.-V., Nielsen, P., Pawlewski, P.: Scheduling of mobile robots with preemptive tasks. In: DCAI, Advances in Intelligent Systems and Computing, vol 290, Springer (2014). https://doi.org/10.1007/978-3-319-07593-8_3
Krystek, J., Kozik, M.: Analysis of the job shop system with transport and setup times in deadlock-free operating conditions. Arch. Control. Sci. 22(4), 371–379 (2012)
Janardhanan, M.N., Li, Z., Bocewicz, G., Banaszak, Z., Nielsen, P.: Metaheuristic algorithms for balancing robotic assembly lines with sequence-dependent robot setup times. Appl. Math. Model. 65, 256–270 (2019). https://doi.org/10.1016/j.apm.2018.08.016
Sitek, P., Wikarek, J., Nielsen, P.: A constraint-driven approach to food supply chain management. Ind. Manag. Data Syst. 117(9): 2115–2138. https://doi.org/10.1108/IMDS-10-2016-0465
Grzybowska, K., Gajšek, B.: Regional logistics information platform as a support for coordination of supply chain. In: Highlights of Practical Applications of Scalable Multi-Agent Systems, The PAAMS Collection, pp. 61–72 (2016). https://doi.org/10.1007/978-3-319-39387-2_6
Deniziak, S., Michno, T., Pieta, P.: IoT-based smart monitoring system using automatic shape identification. In: Advances in Intelligent Systems and Computing book series (AISC), vol. 511, pp. 1–18 (2015). https://doi.org/10.1007/978-3-319-46535-7_1
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Wikarek, J., Sitek, P. (2020). A Data-Driven Approach to Constraint Optimization. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2019. AUTOMATION 2019. Advances in Intelligent Systems and Computing, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-030-13273-6_14
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DOI: https://doi.org/10.1007/978-3-030-13273-6_14
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