Logistical Modelling of Managerial Decisions in Social and Marketing Business Systems

• Autorzy: Oleksandr Velychko , Liudmyla Velychko
• Języki publikacji: EN

Abstrakty

EN

Logistical modelling of business systems within the context of mathematical logistics, logistical management, operational research as well as rationalistic provision of logistics at an enterprise have been considered in the article. The research was carried out on the methodological basis which included the authors' developments and implied conveying familiar knowledge on new objects within the field of linear programming. Scientific novelty concerns the development of categorical toolkit as well as the existing methodical approaches of rationalistic logistics to managerial decisions. Rational areas of using terms "logistical model" and "model of logistics" in business environment have been determined. The authors' methodology of constructing logistical models in management of separate social and marketing systems of enterprises according to minimization and maximization criteria is presented. Ways of using modelling at not conventional objects of logistical support for managerial decisions have been suggested in the context of studying the moral psychological climate of staff and complex estimation of socioeconomic measures of staff management improvement. The procedure of logistical optimization in the system of distributing and advertising activity of the enterprise has been developed. Approbation of the developed models has been carried out and possibilities for further model's complication by output data, variables, and limitations under specific practical conditions have been grounded. (original abstract)

Słowa kluczowe

EN

Enterprise management; Logistic models; Logistic decisions; Logistic system

PL

Zarządzanie przedsiębiorstwem; Modele logistyczne; Decyzje logistyczne; System logistyczny

• Czasopismo: Journal of International Studies
• Rocznik: 2017
• Tom: 10
• Numer: nr 3
• Strony: 206--219

Twórcy

autor

Oleksandr Velychko

• Dnipropetrovsk State Agrarian and Economic University, Ukraine

autor

Liudmyla Velychko

• Dnipropetrovsk National University named after O. Honchar, Ukraine

Bibliografia

• Aksyonov, K., Bykov, E., Aksyonova, O., & Kai, W. (2013b). Application ofBPsim. DSS system for Decision Support in a Construction Corporation. Applied Mechanics and Materials, 256-259, 2886-2889. doi:10.4028/www.scientific.net/AMM.256-259.2886.
• Aksyonov, K., Bykov, E., Aksyonova, O., Goncharova, N., & Nevolina, A. (2013a). Decision support for a fuel company using simulation of logistical processes, In 2013 IEEE 8th Conference on Industrial Electronics and Applications, 1718-1722. doi:10.1109/ICIEA.2013.6566645.
• Bonanno, F., Consoli, A., Lombardo, S., & Raciti, A. (1998). A logistical model for performance evaluations of hybrid generation systems. IEEE Transactions on Industry Applications, 34(6), 1397-1403. doi: 10.1109/28.739027
• Bortolini, R., Shigaki, J. S. I., & Formoso, C. T. (2015). Site logistics planning and control using 4D modeling: a study in a lean car factory building site. In 23rd Annual Conference of the International Group for Lean Construction, 55(51), 361-370.
• Caffrey, K., Chinn, M., Veal, M., & Kay, M. (2015). Biomass supply chain management in North Carolina (part 2): biomass feedstock logistical optimization. AIMS Energy, 4(2), 280-299. doi:10.3934/energy.2016.2.280.
• Ching, W. K., Yuen, W. O., Ng, M. K., & Zhang, S. Q. (2006). A linear programming approach for determining optimal advertising policy. IMA Journal of Management Mathematics, 17(1), 83-96. doi:https://doi.org/10.1093/imaman/dpi039.
• Ficken, F. A. (2015). The simplex method of linear programming. Republication of the edition published by Holt, Rinehart and Winston, New York: Courier Dover Publications.
• Ghilic-Micu, B., Stoica, M., & Sinioros, P. (2016). Inter-organizational Performance and Business Process Management in Collaborative Networks. Economic Computation and Economic Cybernetics Studies and Research, 50(2), 107-122.
• Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk-Definition, measure and modeling. Omega, 52, 119-132. doi: https://doi.org/10.1016/j.omega.2014.10.004.
• Ighravwe, D. E., & Oke, S. A. (2014). A non-zero integer non-linear programming model for maintenance workforce sizing. International Journal of Production Economics, 150, 204-214. doi:10.1016/j.ijpe.2014.01.004.
• Jung, T., & Wickrama, K. A. S. (2007). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2(1), 302-317. doi:10.1111/j.1751-9004.2007.00054.x.
• Kalinina, E. A., & Hardin, E. (2012). Logistical Simulation of Spent Nuclear Fuel Disposal in a Salt Repository with Low Temperature Limits. Sandia National Laboratories, No. SAND2012-10585C. Retrieved from https://www .osti.gov/scitech/servlets/purl/1078874.
• Karande, C., Mehta, A., & Srikant, R. (2013). Optimizing budget constrained spend in search advertising. In Proceedings of the sixth ACM international conference on Web search and data mining, February 4-8, Rome, Italy, 697-706.
• Karsak, E. E. (2000). A fuzzy multiple objective programming approach for personnel selection. In Systems, Man, and Cybernetics, IEEE International Conference on, 3, 2007-2012. doi:10.1109/ICSMC.2000.886409.
• Lee, K. C., Jalali, A., & Dasdan, A. (2013). Real time bid optimization with smooth budget delivery in online advertising. In Proceedings of the Seventh International Workshop on Data Mining for Online Advertising, August 11,Chicago, Illinois, 1- 13.
• Long-banga, M., Pingb, G., Juanb, Z., & Zheng-haoa, Z. (2013). Modeling and Analysis on Multi-AS Associated Cascading Failure. Journal of Logistical Engineering University, 1, 017.
• Matthews, C. H. (2004). Using linear programming to minimize the cost of nurse personnel. Journal of health care finance, 32(1), 37-49.
• Mihalovič, M. (2016). Performance Comparison of Multiple Discriminant Analysis and Logit Models in Bankruptcy Prediction. Economics and Sociology, 9(4), 101-118. doi:10.14254/2071-789X.2016/9-4/6.
• Mirchandani, P., Adler, J., & Madsen, O. B. (2013). New logistical issues in using electric vehicle fleets with battery exchange infrastructure. Procedia-Social and Behavioral Sciences, 108, 3-14. doi:https://doi.org/10.1016/j.sbspro.2013.12.815.
• Morozova, I. V., Postan, M. Y., & Dashkovskiy, S. (2013). Dynamic Optimization Model for Planning of Integrated Logistical System Functioning. InDynamics in Logistics, Springer Berlin Heidelberg, 291-300. doi:https://doi.org/10.1007/978-3-642-35966-8_24.
• Nelles, J., Kuz, S., & Schlick, C. M. (2015). Ergonomic Visualization of Logistical Control Parameters for Flexible Production Planning and Control in Future Manufacturing Systems. In International Conference on Human-Computer Interaction, Springer International Publishing, 684-689. doi:https://doi.org/10.1007/978-3-319-21383-5_116.
• Nezlek, J. B. (2011). Multilevel modeling for social and personality psychology, London: SAGE Publications Ltd.
• Oliveira, L. K., Nunes, N. T. R., & Novaes, A. G. N. (2010). Assessing model for adoption of new logistical services: An application for small orders of goods distribution in Brazil. Procedia-Social and Behavioral Sciences, 2(3), 6286-6296. doi:https://doi.org/10.1016/j.sbspro.2010.04.038.
• Pérez-Gladish, B., González, I., Bilbao-Terol, A., & Arenas-Parra, M. (2010). Planning a TV advertising campaign: a crisp multiobjective programming model from fuzzy basic data. Omega, 38(1), 84-94. doi:https://doi.org/10.1016/j.omega.2009.04.004.
• Postan, M. Y. (2016). Application of Semi-Markov Drift Processes to Logistical Systems Modeling and Optimization. In Dynamics in Logistics, Springer International Publishing, 227-237. doi:https://doi.org/10.1007/978-3-319-23512-7_22.
• Sarder, M. D., Miller, C., & Richard, B. (2011). Modeling Logistical and Economical Impact of Oil Spill on the Gulf Coast. In IIE Annual Conference, Proceedings Institute of Industrial Engineers-Publisher, 1-4.
• Schumann-Bölsche, D., Schön, A. M., & Streit-Juotsa, L. (2015). Modeling and analyzing logistical processes in Cameroon from Douala seaport to the hinterland. Journal of Global Business and Technology, 11(2), 31-36.
• Seuring, S. (2013). A review of modeling approaches for sustainable supply chain management. Decision support systems, 54(4), 1513-1520. doi:https://doi.org/10.1016/j.dss.2012.05.053.
• Soto-Silva, W. E., Nadal-Roig, E., González-Araya, M. C., & Pla-Aragones, L. M. (2016). Operational research models applied to the fresh fruit supply chain. European Journal of Operational Research, 251(2), 345-355. doi:https://doi.org/10.1016/j.ejor.2015.08.046.
• Topaloglu, S., & Selim, H. (2010). Nurse scheduling using fuzzy modeling approach. Fuzzy Sets and Systems, 161(11), 1543-1563. doi:https://doi.org/10.1016/j.fss.2009.10.003.
• Vasylieva, N. K. (2016). Cluster models of households' agrarian production development. Economic Annals-XXI, 3-4(2), 13-16. doi:10.21003/ea.v158-03.
• Vasylieva, N. K., Vinichenko, I. I., & Katan L. I. (2015). Economic and mathematical evaluation of Ukrainian agrarian market by branches. Economic Annals-XXI, 9-10, 41-44.
• Vasylieva, N., & Pugach, A. (2017). Economic assessment of technical maintenance in grain production of Ukrainian agriculture. Bulgarian Journal of Agricultural Science, 23(2), 198-203.
• Velychko, O. (2014). Fundamental basis and connection of modern entrepreneurial logistics and SCM. Review of European Studies, 6(4), 135-146. doi:10.5539/res.v6n4p135.
• Velychko, O. (2015). Integration of SCOR-modeling and logistical concept of management in the system of internal transportation of milk cooperative. Mediterranean Journal of Social Sciences, 6(1S2), 14-24. doi:10.5901/mjss.2015.v6n1s2p14.
• Velychko, O., & Velychko L. (2017). Management of inter-farm use of agricultural machinery based of the logistical system «BOA». Bulgarian Journal of Agricultural Science, 23(4), 534-543.
• Xin, X., Wenhui, F., Yuming, Y., Bin, G., Junhai, C., & Wei, W. (2007). HLA Based High Level Modeling and Simulation for Integrated Logistical Supporting System. In 2007 IEEE International Conference on Automation and Logistics, 2041-2045. doi:10.1109/ICAL.2007.4338910.
• Yan, S., Lin, C. K., & Chen, S. Y. (2014). Logistical support scheduling under stochastic travel times given an emergency repair work schedule. Computers & Industrial Engineering, 67, 20-35. doi:https://doi.org/10.1016/j.cie.2013.10.007.

Identyfikatory

DOI

10.14254/2071-8330.2017/10-3/15