The research concerns the investigation of predictive models based on optimal control task. It allows increasing the management efficiency due to joint consideration and synchronization of internal and external processes towards the system. In this paper, the predictive model for solving multicriterion product management task was developed. To develop a model, automotive industry data was processed. The paper follows the reflexive approach and provides an application of simulation modelling to solve jointly the optimization problem taking into account the mutual influence of the production subsystems. The feasible solutions were received as functions of time. The solutions obtained were compared with the practical ones that based on historical data. The practical significance of the research lies in using market data to estimate company capabilities preliminarily whether they meet the market needs. At the same time, the objectivity of strategic decisions is increasing due to the formalization of process description, objective data preparation, and the company synchronization with the external environment.
Keywords
Product ManagementProduction SystemIndustrial Engineering ProblemOptimization ProblemCriterion FunctionForecastPredictionPredictive ModellingReflexive Control
References
A. Santander-Mercado and M. Jubiz-Diaz, ‘The economic lot scheduling problem: a survey’, Int. J. Prod. Res., vol. 54, no. 16, pp. 4973–4992, Aug. 2016. Proc. of the 6th International Conference on Applied Innovations in IT, (ICAIIT), March 2018
M. Díaz-Madroñero, J. Mula, and D. Peidro, ‘A review of discrete-time optimization models for tactical production planning’, Int. J. Prod. Res., vol. 52, no. 17, pp. 5171–5205, Sep. 2014.
H. Jalali and I. V. Nieuwenhuyse, ‘Simulation optimization in inventory replenishment: a classification’, IIE Trans., vol. 47, no. 11, pp. 1217–1235, Nov. 2015.
M. E. Peralta Álvarez, M. Marcos Bárcena, and F. Aguayo González, ‘On the sustainability of machining processes. Proposal for a unified framework through the triple bottom-line from an understanding review’,
J. Clean. Prod., vol. 142, pp. 3890–3904, Jan. 2017.
L. A. Mylnikov, A. V. Seledkova, and B. Krause, ‘Forecasting characteristics of time series to support managerial decision making process in production-And-economic systems’, Proc. 2017 20th IEEE Int. Conf. Soft Comput. Meas. SCM 2017 6 July 2017, pp. 853–855.
L. A. Mylnikov, ‘Planning and Management Tasks of Innovation Projects in Production and Economic Systems’, in Key Issues for Management of Innovative Projects, B. L. Moya, M. D. S. de Gracia, and L. F. Mazadiego, Eds. InTech, 2017.
L. Mylnikov and M. Kuetz, ‘The risk assessment method in prognostic models of production systems management with account of the time factor’, Eur. Res. Stud. J., vol. 20, no. 3, pp. 291–310, 2017.
L. Mylnikov, ‘The Management of Innovation Projects’ Implementation in Production-and-Economic Systems: The Use of Prognostic Models for the Risk Metrics of Obtained Decisions’, 2017.
S. D. Tajbakhsh, E. del Castillo, and J. L. Rosenberger, ‘A Bayesian Approach to Sequential Optimization based on Computer Experiments’, Qual. Reliab. Eng. Int., vol. 31, no. 6, pp. 1001–1012, Oct. 2015.
K. S. Moghaddam, ‘Supplier selection and order allocation in closed-loop supply chain systems using hybrid Monte Carlo simulation and goal programming’, Int. J. Prod. Res., vol. 53, no. 20, pp. 6320–6338, Oct. 2015.
L. Mylnikov, ‘Risk Evaluation in Manufacturing Organization Tasks for Product Technological Projects and Establishment of Project Portfolio for Production Systems’, in Proceedings of the 2016 International Conference on Applied mathematics, simulation and modelling, Beijing, China, 2016, pp. 399–402.
E. K. Burke, S. J. Landa, and E. Soubeiga, ‘Hyperheuristic Approaches for Multiobjective Optimization’, in Proceeding of the MIC 2003, Kyoto, Japan, 2003.
Walter R. Mebane, Jr. and Jasjeet Singh Sekhon, ‘Package “rgenoud”’. 19-Jul-2015.
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