The relevance of the study is due to the high impact of price policy on the efficiency of
the enterprise. The article presents a description of the method based on inverse calculations
to solve the problem of price optimization. The method involves solving the problem of
unconditional optimization and correction of the obtained values of the arguments taking into
account the restriction. This minimizes the sum of the squares of the argument increments,
taking into account the effect of the arguments on the change of the objective function. The
method is more straightforward in computer implementation, in comparison with the classical
methods of nonlinear optimization, optimization problem the price is reduced to unconstrained
optimization and the solution of the system of equations. The article deals with the problem of
formation of prices for products while maximizing profits from the sale of products and limited
supply. This assumes a linear dependence of demand on price. A comparison of the obtained
result with the solution of the problem in the Mathcad program using standard functions is
presented. The materials of the article are of practical value for organizations in the planning
of pricing policy, as well as for specialists engaged in the development of models for decisionmaking in the field of Economics. The presented method can be used in decision support
systems.
1. Green R., Cornelsen L., Dangour A., Turner R., Shankar B., Mazzocch M.,
Smith R. The effect of rising food prices on food consumption: systematic
review with meta-regression. BMJ, vol. 346, 2013, pp. 1–9.
2. Noparumpa T., Kazaz B., Webste S. Wine futures and advance selling under
quality uncertainty. Manufacturing & service operations management, vol. 3,
2015, pp. 411–426.
3. Berry S., Levinsohn J., Pakes A. Automobile prices in market equilibrium
steven. Econometrica, vol. 63, 2007, pp. 841–890
4. Johnson K., Hong B., Lee A., Simchi-levi D. Analytics for an online retailer:
demand forecasting and price optimization. Manufacturing & Service
Operations Management, vol. 18, 2016, pp. 69–85.
5. Kunz T., Crone S., Meissner J. The effect of data preprocessing on a retail
price optimization system. Decision Support Systems, vol.84, 2016, pp. 16–
27.
6. Reisi M., Gabriel S.A., Fahimnia B. Supply chain competition on shelf space
and pricing for soft drinks: A bilevel optimization approach. International
journal of production economics, vol. 211, 2019, pp. 237–250.
7. Theysohn S., Klein K., Volckner F, Spann M. Dual effect-based market
segmentation and price optimization. Journal of business research, vol. 66,
2013, pp. 480–488.
8. Krasheninnikova E., Garcia J., Maestre R., Fernandez F. Reinforcement
learning for pricing strategy optimization in the insurance industry.
Engineering applications of artificial intelligence, vol.80, 2019, pp.8–19.
9. Gupta V.K., Ting Q.U., Tiwari M.K. Multi-period price optimization problem
for omnichannel retailers accounting for customer heterogeneity. International
journal of production economics, vol. 215, 2019, pp. 155–167.
10. Chen R., Jiang H. Capacitated assortment and price optimization for
customers with disjoint consideration sets. Operations Research Letters, vol.
45, 2017, pp. 170–174.
11. Chen R., Jiang H. Capacitated assortment and price optimization under the
multilevel nested logit model. Operations Research Letters, vol. 47, 2019, pp.
30–35.
12. Qu T., Zhang J. H., Chan F., Srivastava R.S., Tiwari M.K., Park W. Demand
prediction and price optimization for semi-luxury supermarket segment.
Computers & industrial engineering, vol. 113, 2017, pp. 91–102.
13. Tsan-Ming C., Cheng M., Bin S., Qi S. Optimal pricing in mass customization
supply chains with risk-averse agents and retail competition. Omega, vol. 88,
2019, pp. 150–161.
14. Odincov B.E. Obratnye vychislenija v formirovanii jeko-nomicheskih
reshenij. Moscow, Finansy i statistika Publ., 2004. 256 p.
15. Gribanova E.B. Methods for solving inverse problems of economic analysis
by minimizing argument increments. Bulletin of TUSUR, 2018, no. 2, pp. 95–
99.
16. Gribanova E.B. Solving the procurement optimization problem by means of
inverse computation. Economic analysis: theory and practice, 2018, no. 3, pp.
586–596.