V.I. Norkin, Y. Ermoliev and A. Ruszczy'nski: On optimal allocation of indivisibles under uncertainty, Operations Research 46 (1998) 381--395. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
On Stochastic Integer Programming under.. - Dentcheva.. (1998) (Correct)
....rather than to optimize an objective function subject to a probabilistic constraint, but other formulations are possible as well. Another application area are communication and transportation network capacity expansion problems, where arc and node capacities are restricted to be integers [12, 21]. Bond portfolio problems with random integer valued liabilities can be formalized as (1.1) see [5] Many production planning problems involving random indivisible demands fit to our general setting as well. Although we concentrate on integer random variables, all our results easily extend to ....
V.I. Norkin, Y. Ermoliev and A. Ruszczy'nski: On optimal allocation of indivisibles under uncertainty, Operations Research 46 (1998) 381--395.
On Stochastic Integer Programming under Probabilistic Constraints - Dentcheva, al. (1998) (Correct)
....rather than to optimize an objective function subject to a probabilistic constraint, but other formulations are possible as well. Another application area are communication and transportation network capacity expansion problems, where arc and node capacities are restricted to be integers [12, 21]. Bond portfolio problems with random integer valued liabilities can be formalized as (1) see [5] Many production planning problems involving random indivisible demands fit to our general setting as well. Although we concentrate on integer random variables, all our results easily extend to other ....
V.I. Norkin, Y. Ermoliev and A. Ruszczy'nski: On optimal allocation of indivisibles under uncertainty, Operations Research 46 (1998) 381--395.
Optimal Power Generation under Uncertainty via Stochastic.. - Dentcheva, Römisch (1997) (9 citations) (Correct)
....programs involving integrality constraints. Methods for solving (mixed ) integer stochastic programs are rather rare. We refer to [61] for a brief overview of some recent approaches to stochastic integer programming. Moreover, let us mention a recently developed stochastic branch and bound method ([47]) and a dual decomposition method based on relaxing the scenario constraints and on (deterministic) branch and bound techniques ( 9] which also applies to mixed integer situations. Our paper is organized as follows. We introduce and discuss the two stochastic power scheduling models in Section ....
V.I. Norkin, Y. Ermoliev and A. Ruszczy'nski: On optimal allocation of indivisibles under uncertainty, International Institute of Applied Systems Analysis (Laxenburg, Austria), Working Paper WP-94-21, 1994.
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