A model is presented for the derivation and implementation of an optimal linear decision rule for a firm producing and dealing in a number of interacting products, and possessing partial influence on their prices. The behavior of a multi-item production-inventory complex is represented by the dynamics of suitably defined state variables under the influence of decision rules that are linear in the state variables, but otherwise unspecified. The dynamic equations are stochastic owing to the presence of stochastic processes in the forcing terms. The statistical properties of these processes, together with the decision rules, determine the statistics of a functional describing the outcome. The optimum inventory decision is then derived as the "best" linear transformation on the past of the state variables, such that the mean value of the criterion functional is optimized subject to the system of constraints. A mechanism is also developed such that the optimal linear rule may be implemented.
MLA
Banerjee, B. P., and S. S. Sengupta. “Multi-Item Production and Inventory Management Under Price Uncertainty.” Econometrica, vol. 34, .no 4, Econometric Society, 1966, pp. 796-804, https://www.jstor.org/stable/1910100
Chicago
Banerjee, B. P., and S. S. Sengupta. “Multi-Item Production and Inventory Management Under Price Uncertainty.” Econometrica, 34, .no 4, (Econometric Society: 1966), 796-804. https://www.jstor.org/stable/1910100
APA
Banerjee, B. P., & Sengupta, S. S. (1966). Multi-Item Production and Inventory Management Under Price Uncertainty. Econometrica, 34(4), 796-804. https://www.jstor.org/stable/1910100
The Executive Committee of the Econometric Society has approved an increase in the submission fees for papers in Econometrica. Starting January 1, 2025, the fee for new submissions to Econometrica will be US$125 for regular members and US$50 for student members.
By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device. Cookies are used to optimize your experience and anonymously analyze website performance and traffic.