Uncertainty is an inherent characteristic in most industrial processes, and a variety of approaches including sensitivity analysis, robust optimization and stochastic programming have been proposed to deal with such u...
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Uncertainty is an inherent characteristic in most industrial processes, and a variety of approaches including sensitivity analysis, robust optimization and stochastic programming have been proposed to deal with such uncertainty. Uncertainty in a steady state nonlinear real-time optimization (RTO) system and particularly making robust decisions under uncertainty in real-time has received little attention. This paper discusses various sources of uncertainty within such closed loop RTO systems and a method, based on stochastic programming, that explicitly incorporates uncertainty into the RTO problem is presented. The proposed method is limited to situations where uncertain parameters enter the constraints nonlinearly and uncertain economics enter the objective function linearly. Our approach is shown to significantly improve the probability of a feasible solution in comparison to more conventional RTO techniques. A gasoline blending example is used to demonstrate the proposed robust RTO approach. (C) 2002 Elsevier Science Ltd. All rights reserved.
This paper describes a methodology for managing capacity, inventory, and shipments for an assortment of retail products produced by multiple vendors. The vendors differ in lead times, costs, and production flexibility...
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This paper describes a methodology for managing capacity, inventory, and shipments for an assortment of retail products produced by multiple vendors. The vendors differ in lead times, costs, and production flexibility. Product demand is uncertain and fluctuates over time. We develop an optimization model to choose the production commitments that maximize the retailer's expected gross profit, given demand forecasts and vendors' capacity and flexibility constraints. The model has been incorporated into a decision support system, developed in collaboration with supply chain planners at a global retailer of seasonal and fashion merchandise. The software has been tested by two major retailers.
We consider the problem of constructing mean-risk models which are consistent with the second degree stochastic dominance relation. By exploiting duality relations of convex analysis we develop the quantile model of s...
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We consider the problem of constructing mean-risk models which are consistent with the second degree stochastic dominance relation. By exploiting duality relations of convex analysis we develop the quantile model of stochastic dominance for general distributions. This allows us to show that several models using quantiles and tail characteristics of the distribution are in harmony with the stochastic dominance relation. We also provide stochastic linear programming formulations of these models.
A two-stage stochastic programming model for the short-or mid-term cost-optimal electric power production planning is developed. We consider the power generation in a hydro-thermal generation system under uncertainty ...
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A two-stage stochastic programming model for the short-or mid-term cost-optimal electric power production planning is developed. We consider the power generation in a hydro-thermal generation system under uncertainty in demand (or load) and prices for fuel and delivery contracts. The model involves a large number of mixed-integer (stochastic) decision variables and constraints linking time periods and operating power units. A stochastic Lagrangian relaxation scheme is designed by assigning (stochastic) multipliers to all constraints that couple power units. It is assumed that the stochastic load and price processes are given (or approximated) by a finite number of realizations (scenarios). Solving the dual by a bundle subgradient method leads to a successive decomposition into stochastic single unit subproblems. The stochastic thermal and hydro subproblems are solved by a stochastic dynamic programming technique and by a specific descent algorithm, respectively. A Lagrangian heuristics that provides approximate solutions for the primal problem is developed. Numerical results are presented for realistic data from a German power utility and for numbers of scenarios ranging from 5 to 100 and a time horizon of 168 hours. The sizes of the corresponding optimization problems go up to 400.000 binary and 650.000 continuous variables, and more than 1.300.000 constraints.
The traffic control is a critical issue in ATM networks. In ATM, the traffic control is implemented at different levels: cell level, call level and network flow level. The virtual path (VP) distribution involves both ...
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ISBN:
(纸本)081944698X
The traffic control is a critical issue in ATM networks. In ATM, the traffic control is implemented at different levels: cell level, call level and network flow level. The virtual path (VP) distribution involves both call level and flow level controls. The VP distribution is a logic network design problem based on the physical network. Several VP optimization schemes have been proposed, and a large number of these schemes are based on the flow assignment (FA) model. In this paper, an improved flow model is proposed with a non-linear objective function. The proposed model incorporates two concepts: VP capacity and VP flow, to perform the optimization. The proposed model distributes traffic on all available VPs evenly, and has redundant capacities. Hence, the resulting VP system is resilient to input traffic changes and physical link failures. In addition to the proposed FA model, we introduce a stochastic programming (SP) methodology to allocate virtual paths when the incoming traffic changes stochastically. Experimental results show that the proposed flow model and the stochastic methodology improve the performance of ATM networks.
A chance-constrained problem of portfolio selection is to choose a portfolio to minimize standard deviation under the condition that the probability that the portfolio's rate of return is greater than an expected ...
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ISBN:
(纸本)0780370872
A chance-constrained problem of portfolio selection is to choose a portfolio to minimize standard deviation under the condition that the probability that the portfolio's rate of return is greater than an expected rate of return is no less than a confidence level. When short selling is not allowed, the chance-constrained problem of portfolio selection is investigated in this paper, its deterministic equivalent mathematical model is established, its properties of existence and uniqueness of the optimal solution is discussed, and the steps of obtaining the optimal solution are given. The programs for efficient frontier, permission set and the optimal solution are devised by Matlab. Finally, an illustrative example is provided.
This paper addresses the disassembly planning of heterogeneous batches of returned products. Disassembly is used to enhance the release of material fractions for recovery. The mass fractions of materials that are actu...
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ISBN:
(纸本)0819442976
This paper addresses the disassembly planning of heterogeneous batches of returned products. Disassembly is used to enhance the release of material fractions for recovery. The mass fractions of materials that are actually released during the recovery process are not known in advance and henceforth decisions concerning disassembly are to be made under uncertainty. The present paper provides a stochastic programming method to deal with uncertainty and to assess the value of information.
The growth of the Internet and the World Wide Web has created an enormous demand for wideband data distribution around the globe. Satellite networks provide global reach and wide area coverage, especially to remote, r...
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ISBN:
(纸本)081944698X
The growth of the Internet and the World Wide Web has created an enormous demand for wideband data distribution around the globe. Satellite networks provide global reach and wide area coverage, especially to remote, rural and inaccessible regions. With a limited bandwidth, congestion is likely to occur when the demand for the bandwidth is high. In this paper, we present a capacity and flow assignment (CFA) model for the satellite ATM networks. We then present a stochastic programming approach to optimize the CFA in the satellite networks. The proposed model has been evaluated with a prototype network with 4 nodes, and the simulation results are promising.
This paper considers the estimation of a stochastically cointegrating regression within the stochastic cointegration modelling framework introduced in McCabe et al. (stochastic cointegration: testing, 2001). A stochas...
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stochastic linear programming (SLP) models involve multivariate integrals. Although in the discretely distributed case these integrals become sums they typically contain a large amount of terms. The purpose of this pa...
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