Journal of Applied Economic Research
ISSN 2712-7435
Modeling of Promising Interaction Between a Timber Industry Enterprise and a Commodity Exchange in Russia
R.S. Rogulin
Vladivostok State University Economy and Service, Vladivostok, Russia
Abstract
The relevance of the study lies in the absence of works in the literature devoted to the formation of supply chains of materials in volumes sufficient for production using the apparatus of commodity exchanges. The aim of the work is to conduct an empirical study to assess the prospects for the interaction of a timber industry enterprise with a commodity exchange. For the study, a mathematical model was chosen to assess the effectiveness of the purchase of raw materials from the forestry department of the commodity exchange by an enterprise in the timber industry. The hypothesis is that the interaction of the timber industry complex can be beneficial for the enterprise. To ensure the feasibility of purchasing raw materials from the exchange, simulation modeling was chosen. For each individual simulation iteration, a linear integer programming mathematical model was used. To generate some input data, like price, demand, etc., the Monte Carlo method was used. The complexity of the problem lies in the following aspects: polynomial growth of the number of numbers; a large number of restrictions on the increase in the degree of complexity of finding the first feasible solution to the model; search for a solution within the framework of integer optimization; a fairly large number of independent simulation iterations. The practical significance of the study is to prove the expediency of purchasing raw materials by the enterprise from the commodity and raw materials exchange of Russia. The theoretical significance of the study lies in the development of a model for assessing the feasibility of purchasing materials using the exchange apparatus. The scientific novelty is based on the constructed mathematical model of the formation of supply chains and the volume of production, taking into account the demand in the market and the volume of materials. The model was tested on data from one forestry enterprise in the Primorsky Territory. Optimization is carried out in terms of the volume of products produced, the volume of purchased materials from each region and the stock of raw materials in the production warehouse. Based on the testing of data models of the exchange and the forestry enterprise, an analysis was performed of the possibilities for cooperation between the company and the commodity exchange. The work reflects the behavior in the long term of accumulated profit, the nature of changes in stock in the warehouse and the volume of products produced.
Keywords
supply chains; enterprise economics; forest exchange; data analysis; resource consumption rate; warehouse capacity
JEL classification
M24, C51References
1. Amiri, A. (2006). Designing a distribution network in a supply chain system: formulation and efficient solution procedure. European Journal of Operational Research, Vol. 171, Issue 2, 567–576. DOI: 10.1016/j.ejor.2004.09.018.
2. Gebennini, E., Gamberini, R., Manzini, R. (2009). An integrated production–distribution model for the dynamic location and allocation problem with safety stock optimization. International Journal of Production Economics, Vol. 122, Issue 1, 286–304. DOI: 10.1016/j.ijpe.2009.06.027.
3. Konak, A., Coit, D.W., Smith, A.E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering and System Safety, Vol. 91, Issue 9, 992–1007. DOI: 10.1016/j.ress.2005.11.018.
4. Rahimikelarijani, B., Saidi-Mehrabad, M., Barzinpour, F. (2020). A Mathematical Model for Multiple-Load AGVs in Tandem Layout. Journal of Optimization in Industrial Engineering, Vol. 13, 67–80. DOI: 10.22094/joie.2018.537.37.
5. Talatahari, S., Goodarzimehr, V., Taghizadieh, N. (2020). Hybrid Teaching-Learning-Based Optimization and Harmony Search for Optimum Design of Space Trusses. Journal of Optimization in Industrial Engineering, Vol. 13, 177–194. DOI: 10.22094/joie.2019.1866904.1649.
6. Samadi, M., Nouraei, M., Mozaffari, M., Haji Karimi, B. (2020). Optimal Localization of Shopping Centers Using Metaheuristic Genetic Algorithm. Journal of Optimization in Industrial Engineering, Vol. 13, 167–176. DOI: 10.22094/joie.2019.363.0.
7. Ackermann, F., Eden, C. (2020). Strategic Options Development and Analysis. Systems Approaches to Making Change: A Practical Guide. Edited by M. Reynolds, <st1:place w:st="on">S. Holwell</st1:place>. Springer, 139–199. DOI: 10.1007/978-1-84882-809-4_4.
8. Scavarda, L.F., Reichhart, A., Hamacher, S., Holweg, M. (2010). Managing product variety in emerging markets. International Journal of Operations & Production Management, Vol. 30, Issue 2, 205–224. DOI: 10.1108/01443571011018716.
9. Billal, M., Hossain, M. (2020). Multi-Objective Optimization for Multi-Product Multi-Period Four Echelon Supply Chain Problems Under Uncertainty. Journal of Optimization in Industrial Engineering, Vol. 13, 1–17. DOI: 10.22094/joie.2018.555578.1529.
10. Ren, J., Tan, S., Yang, L., Goodsite, M.E., Pang, C., Dong, L. (2015). Optimization of emergy sustainability index for bio diesel supply network design. Energy Conversion and Management, Vol. 92, 312–321. DOI: 10.1016/j.enconman.2014.12.066.
11. Cardona-Valdés, Y., Alvarez, A., Ozdemir, D. (2011). A bi-objective supply Alvarez chain design problem with uncertainty. Transportation Research Part C: Emerging Technologies, Vol. 19, Issue 5, 821–832. DOI: 10.1016/j.trc.2010.04.003.
12. El-Sayed, M., Afia, N., El-Kharbotly, A. (2010). A stochastic model for forward–reverse logistics network design under risk. Computer & Industrial Engineering, Vol. 58, Issue 3, 423–431. DOI: 10.1016/j.cie.2008.09.040.
13. Schut, P.Z., Tomasgard, A., Ahmed, S. (2009). Supply chain design under uncertainty using sample average approximation and dual decomposition. European Journal of Operational Research, Vol. 199, Issue 2, 409–419. DOI: 10.1016/j.ejor.2008.11.040.
14. Chen, C.L, Wen, W.C. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers and Chemical Engineering, Vol. 28, Issue 6-7, 1131–1144. DOI: 10.1016/j.compchemeng.2003.09.014.
15. Georgiadis, M.C, Tsiakis, P., Longinidis, P., Sofioglou, M.K. (2011). Optimal design of supply chain networks under uncertain transient demand variations. Omega, Vol. 39, Issue 3, 254–272. DOI: 10.1016/j.omega.2010.07.002.
16. Wang, K.J., Makond, B., Liu, S.Y. (2011). Location and allocation decisions in a two-echelon supply chain with stochastic demand – a genetic-algorithm based solution. Expert Systems with Applications, Vol. 38, Issue 5, 6125–6131. DOI: 10.1016/j.eswa.2010.11.008.
17. Olivares-Benitez, E., González-Velarde, J.L., RíosMercado, R.Z. (2012). A supply chain design problem with facility location and bi-objective transportation choices. Sociedad de Estadísticae Investigación Operativa, Vol. 20, 729–753. DOI: 10.1007/s11750-010-0162-8.
18. Guoquan Zhang, G., Shang, J., Li, L. (2011). Collaborative production planning of supply chain under price and demand uncertainty. European Journal of Operational Research, Vol. 215, Issue 3, 590–603. DOI: 10.1016/j.ejor.2011.07.007.
19. Pishvaee, M.S., Farahani, R.Z., Dullaert, W. (2010). A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers & Operations Research, Vol. 37, Issue 6, 1100–1112. DOI: 10.1016/j.cor.2009.09.018.
20. Easwaran, G., Üster, H. (2010). A closed-loop supply chain network design problem with integrated forward and reverse channel decisions. IIEE Transactions, Vol. 42, Issue 11, 779–792. DOI: 10.1080/0740817X.2010.504689.
21. Mehrbod, M., Tu, N., Miao, L., Dai, W. (2012). Interactive fuzzy goal programming for a multiobjective closed-loop logistics network. Annals of Operations Research, Vol. 201, Issue 1, 367–381. DOI: 10.1007/s10479-012-1192-4.
22. Lu, Z., Bostel, N. (2007). A facility location model for logistics systems including reverse flows: the case of remanufacturing activities. Computers & Operations Research, Vol. 34, Issue 2, 299–323. DOI: 10.1016/j.cor.2005.03.002.
23. Ruiz-Femenia, R., Guillén-Gosálbez, G., Jiménez, L., Caballero, J.A. (2013). Multi-objective optimization of environmentally conscious chemical supply chains under demand uncertainty. Chemical Engineering Science, Vol. 96, 1–11. DOI: 10.1016/j.ces.2013.02.054.
24. Rodriguez, M.A., Vecchietti, A.R., Harjunkoski, L., Grossmann, L.E. (2014). Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part I: MINLP and MILP models. Computers & Chemical Engineering, Vol. 62, 194–210. DOI: 10.1016/j.compchemeng.2013.10.007.
25. Shankar, B.L., Basavarajappa, S., Chen, J.C.H., Kadadevaramath, R.S. (2013). Location and allocation decisions for multi-echelon supply chain network–A multi-objective evolutionary approach. Expert Systems with Applications, Vol. 40, Issue 2, 551–562. DOI: 10.1016/j.eswa.2012.07.065.
26. Sarrafha, K., Rahmati, S.H.A., Niaki, S.T.A., Zaretalab, A. (2014). A bi-objective integrated procurement production and distribution problem of a multiechelon supply chain network design: A new tuned MOEA. Computers & Operations Research, Vol. 54, 35–51. DOI: 10.1016/j.cor.2014.08.010.
27. Pasandideh, S.H.R., Niaki, S.T.A., Asadi, K. (2015). Optimizing a bi-objective multi-product multiperiod three echelon supply chain network with warehouse reliability. Expert Systems with Applications, Vol. 42, Issue 5, 2615–2623. DOI: 10.1016/j.eswa.2014.11.018.
28. Eremina, N.V., Siubaeva, N.I., Tysiachnikova, Ia.G. (2016). Aktualnye problemy ekonomicheskoi bezopasnosti (Summary Shadow Economy And Its Manifestations On The Example Of "Black" Accounting). Wschodnioeuropejskie Czasopismo Naukowe, Vol. 10, No. 1, 39–42.
29. Shah, N.H., Chaudhari, U., Cárdenas-Barrón, L.E. (2020). Integrating credit and replenishment policies for deteriorating items under quadratic demand in a three-echelon supply chain. International Journal of Systems Science: Operations & Logistics, Vol. 7, Issue 1. 34–45. DOI: 10.1080/23302674.2018.1487606.
30. Hajiaghaei-Keshteli M., Fathollahi-Fard A.M. (2019). Sustainable closed-loop supply chain network design with discount supposition. Neural Computing and Applications, Vol. 31, Issue 5, 10–29. DOI: 10.1007/s00521-018-3369-5.
31. Nobil, A.H., Nobil, E., Sarker, B.R. (2020). Optimal decision-making for a single-stage manufacturing system with rework options. International Journal of Systems Science: Operations & Logistics, Vol. 7, Issue 1, 90–104. DOI: 10.1080/23302674.2018.1514087.
About Authors
Rogulin Rodion Sergeevich
Assistant, Department of Mathematics and Modeling, Vladivostok State University of Economics and Service, Vladivostok, Russia (690014, Primorsky Territory, Vladivostok, Gogol street, 41); ORCID: 0000-0002-3235-6429; e-mail: rafassiaofusa@mail.ru.
For citation
Rogulin R.S. Modeling of Promising Interaction Between a Timber Industry Enterprise and a Commodity Exchange in Russia. Journal of Applied Economic Research, 2020, Vol. 19, No. 4, 489-511. DOI: 10.15826/vestnik.2020.19.4.023.
Article info
Received May 27, 2020; Revised July 6, 2020; Accepted September 20, 2020.
DOI: http://dx.doi.org/10.15826/vestnik.2020.19.4.023
Download full text article:
~3 MB, *.pdf
(Uploaded
14.12.2020)
Created / Updated: 2 September 2015 / 20 September 2021
© Federal State Autonomous Educational Institution of Higher Education «Ural Federal University named after the first President of Russia B.N.Yeltsin»
Remarks?
select the text and press:
Ctrl + Enter
Portal design: Artsofte
Contact us
Rector's Office
Rector, Dr. Victor Koksharov
Tel. +7 (343) 375-45-03, e-mail: rector@urfu.ru
Vice-Rector for International Relations, Dr. Maxim Khomyakov
Tel. +7 (343) 375-46-27, e-mail: Maksim.Khomyakov@urfu.ru