Subject. This article deals with the issues of placing and locating the information and logistics center of the Federal District's clusters. Objectives. The article aims to create a technology to identify the center of innovative industrial clusters, which would ensure their effective economic, financial, information, and logistics cooperation. Methods. To solve the problem of location of the information and logistics center of clusters in order to anticipate the evolution of the Federal District, we used simulation modeling algorithms, genetic algorithm, and the simulated annealing and pattern search methods. Results. These methods have been tested for the Volga (Privolzhsky) Federal District of the Russian Federation. As a result of analysis, modeling and calculations, it turns out that the information and logistics center of the Volga (Privolzhsky) Federal District should be the City of Kazan, the Republic of Tatarstan. Conclusions and Relevance. The deployment of the information and logistics center for the Volga (Privolzhsky) Federal District in Kazan can significantly reduce the transaction costs associated with the regulation of information and transportation flows within the Federal District under study. This will reduce the financial costs in the District and increase the synergistic effect of a large innovation system uniting innovation and industrial clusters in a large area of the entire Federal District. The additional synergies will help the governmental structures and their experts conduct a better policy of economic and financial foresight of the evolution of the Volga (Privolzhsky) Federal District.
Koshelev E.V., Trifonov Yu.V., Yashin S.N. [Methods foresight of the cluster using the arbitrage technology]. Innovatsii = Innovations, 2017, no. 11, pp. 42–53. URL: Link (In Russ.)
Akinc U., Khumawala B.M. An Efficient Branch and Bound Algorithm for the Capacitated Warehouse Location Problem. Management Science, 1977, vol. 23, no. 6, pp. 545–665. URL: Link
Cornuejols G., Fisher M.L., Nemhauser G.L. Location of Bank Accounts to Optimize Float: An Analytic Study of Exact and Approximate Algorithms. Management Science, 1977, vol. 23, no. 8, pp. 789–810.
Krarup J., Pruzan P.M. The Simple Plant Location Problem: Survey and Synthesis. European Journal of Operational Research, 1983, vol. 12, iss. 1, pp. 36–81. URL: Link90181-9
Kolen A. Solving Covering Problems and the Uncapacitated Plant Location on the Trees. European Journal of Operational Research, 1983, vol. 12, iss. 3, pp. 266–278. URL: Link90197-2
Mirchandani P., Jagannathan R. Discrete Facility Location with Nonlinear Diseconomies in Fixed Costs. Annals of Operations Research, 1989, vol. 18, pp. 213–224. URL: Link
Mirchandani P.B., Francis R.L. (Eds). Discrete Location Theory. New York, John Wiley & Sons, 1990, 576 p.
Campbell J.F. Integer Programming Formulations of Discrete Hub Location Problems. European Journal of Operational Research, 1994, vol. 72, iss. 2, pp. 387–405. URL: Link90318-2
Voznyuk I.P. [The task of locating production plants on the capacitated communication 2-tree data structure]. Diskretnyi Analiz i Issledovanie Operatsii. Seriya 2, 2000, vol. 7, no. 1, pp. 3–8. URL: Link (In Russ.)
Burkard R., Dollani H., Lin Yixun, Rote G. The Obnoxious Center Problem on a Tree. SIAM Journal on Discrete Mathematics, 2001, vol. 14, iss. 4, pp. 498–509. URL: Link
Tamir A. Improved Complexity Bounds for Center Location Problems on Networks by Using Dynamic Data Structures. SIAM Journal on Discrete Mathematics, 1988, vol. 1, iss. 3, pp. 377–396. URL: Link
Murray A.T., Tong D. Coverage Optimization in Continuous Space Facility Siting. International Journal of Geographical Information Science, 2007, vol. 21, iss. 7, pp. 757–776. URL: Link
Ageev A.A., Gimadi E.Kh., Kurochkin A.A. [Polynomial algorithm for the path facility location problem with uniform capacities]. Diskretnyi Analiz i Issledovanie Operatsii, 2009, vol. 16, no. 5, pp. 3–18. URL: Link (In Russ.)
Gimadi E.Kh. [An optimal algorithm for an outerplanar facility location problem with improved time complexity]. Trudy Instituta matematiki i mekhaniki UrO RAN = Proceedings of Krasovskii Institute of Mathematics and Mechanics UB RAS, 2017, vol. 23, no. 3, pp. 74–81. (In Russ.) URL: Link
Huang T., Bergman D., Gopal R. Predictive and Prescriptive Analytics for Location Selection of Add-on Retail Products. Production and Operations Management, 2019, vol. 28, iss. 7, pp. 1858–1877. URL: Link
Kalyanmoy D. Multi-Objective Optimization Using Evolutionary Algorithms. New York, John Wiley & Sons, 2001, 518 p.
Lopatin A.S. [Simulated annealing method]. Stokhasticheskaya optimizatsiya v informatike, 2005, vol. 1, no. 1, pp. 133–149. URL: Link (In Russ.)
Ingber L., Rosen B. Genetic Algorithms and Very Fast Simulated Reannealing: A Comparison. Mathematical and Computer Modelling, 1992, vol. 16, iss. 11, pp. 87–100. URL: Link90108-W
Conn A.R., Gould N.I.M., Toint Ph. A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bounds. SIAM Journal on Numerical Analysis, 1991, vol. 28, no. 2, pp. 545–572. URL: Link
Conn A.R., Gould N.I.M., Toint Ph.L. A Globally Convergent Lagrangian Barrier Algorithm for Optimization with General Inequality Constraints and Simple Bounds. Mathematics of Computation, 1997, vol. 66, no. 217, pp. 261–288. URL: Link
Kolda T.G., Lewis R.M., Torczon V. A Generating Set Direct Search Augmented Lagrangian Algorithm for Optimization with a Combination of General and Linear Constraints. Technical Report SAND2006-5315. Oak Ridge, Sandia National Laboratories, August 2006, 44 p. URL: Link