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Online Surrogate Problem Methodology for Stochastic Discrete Resource Allocation Problems
 J. Optim. Theory Appl
"... We consider stochastic discrete optimization problems where the decision variables are nonnegative integers. We propose and analyze an online control scheme which transforms the problem into a “surrogate ” continuous optimization problem and proceeds to solve the latter using standard gradientbas ..."
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Cited by 5 (1 self)
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We consider stochastic discrete optimization problems where the decision variables are nonnegative integers. We propose and analyze an online control scheme which transforms the problem into a “surrogate ” continuous optimization problem and proceeds to solve the latter using standard gradientbased approaches while simultaneously updating both actual and surrogate system states. It is shown that the solution of the original problem is recovered as an element of the discrete state neighborhood of the optimal surrogate state. For the special case of separable cost functions, we show that this methodology becomes particularly efficient. Finally, convergence of the proposed algorithm is established under standard technical conditions and numerical results are included in the paper to illustrate the fast convergence properties of this approach. 1
Generalized Surrogate Problem Methodology for Online Stochastic Discrete Optimization
 Journal of Optimization Theory and Applications
, 2002
"... We consider stochastic discrete optimization problems where the decision variables are nonnegative integers and propose a generalized “surrogate problem ” methodology that modi…es and extends previous work in [1]. Our approach is based on an online control scheme which transforms the problem into ..."
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Cited by 4 (0 self)
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We consider stochastic discrete optimization problems where the decision variables are nonnegative integers and propose a generalized “surrogate problem ” methodology that modi…es and extends previous work in [1]. Our approach is based on an online control scheme which transforms the problem into a “surrogate ” continuous optimization problem and proceeds to solve the latter using standard gradientbased approaches while simultaneously updating both actual and surrogate system states. In contrast to [1], the proposed methodology applies to arbitrary constraint sets. It is shown that, under certain conditions, the solution of the original problem is recovered from the optimal surrogate state. Applications of this approach include solutions to multicommodity resource allocation problems, where, exploiting the convergence speed of the method, one can overcome the obstacle posed by the presence of local optima.
Optimal token allocation in timed cyclic event graphs
 Proceedings of 5th international
, 2000
"... Abstract In this paper we deal with the problem of allocating a given number of tokens in a cyclic timed event graph (CTEG) so as to maximize the firing rate of the net. We propose two different procedures, both involving the solution of a mixed integer linear programming problem. The first one nee ..."
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Cited by 1 (1 self)
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Abstract In this paper we deal with the problem of allocating a given number of tokens in a cyclic timed event graph (CTEG) so as to maximize the firing rate of the net. We propose two different procedures, both involving the solution of a mixed integer linear programming problem. The first one needs the knowledge of the elementary cycles, thus it is convenient only for those classes of CTEGs whose number of elementary cycles is limited by the number of places, like kanban systems. On the contrary, the second one enables us to overcome this difficulty, thus providing an efficient tool for the solution of allocation problems in complex manufacturing systems like job–shop systems.
Inventory Control for Supply Chains with Service Level Constraints: A Synergy between Large Deviations and Perturbation Analysis
, 2002
"... We consider a model of a supply chain consisting of n production facilities in tandem and producing a single product class. External demand is met from the finished goods inventory maintained in front of the most downstream facility (stage 1); unsatisfied demand is backlogged. We adopt a basestock ..."
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We consider a model of a supply chain consisting of n production facilities in tandem and producing a single product class. External demand is met from the finished goods inventory maintained in front of the most downstream facility (stage 1); unsatisfied demand is backlogged. We adopt a basestock production policy at each stage of the supply chain, according to which the facility at stage i produces if inventory falls below a certain level wi and idles otherwise. We seek to optimize the hedging vector w = (w1,..., wn) to minimize expected inventory costs at all stages subject to maintaining the stockout probability at stage 1 below a prescribed level (service level constraint). We make rather general modeling assumptions on demand and production processes that include autocorrelated stochastic processes. We solve this stochastic optimization problem by combining analytical (large deviations) and sample pathbased (perturbation analysis) techniques. We demonstrate that there is a natural synergy between these two approaches.
APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED.
, 2002
"... Directorate, Public Affairs Office (IFOIPA) and is releasable to the National Technical Information Service (NTIS). At NTIS it will be releasable to the general public, including foreign nations. ..."
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Directorate, Public Affairs Office (IFOIPA) and is releasable to the National Technical Information Service (NTIS). At NTIS it will be releasable to the general public, including foreign nations.
unknown title
"... This paper was not presented at any IFAC meeting. This paper was recommended for publication in revised form by Associate Editor ..."
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This paper was not presented at any IFAC meeting. This paper was recommended for publication in revised form by Associate Editor