Vertex cover problem is not only a famous problem in graph theory, but also a problem employed to model many real-life situations. In this paper, the minimum weight vertex cover problem with stochastic weights is stud...
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Vertex cover problem is not only a famous problem in graph theory, but also a problem employed to model many real-life situations. In this paper, the minimum weight vertex cover problem with stochastic weights is studied. We propose for the first time the concepts of expected minimum weight vertex cover, α-minimum weight vertex cover and the most minimum weight vertex cover. According to different decision criteria, three types of models: expected value model, chance-constrained programming and dependent-chance programming are formulated. We produce a hybrid intelligent algorithm integrating stochastic simulation and genetic algorithm to solve the models. Finally, a numerical example is given to illustrate the effectiveness of the algorithm.
We consider a dynamic planning problem for the transport of elderly and disabled people. In particular, we focus on a decision to take one day ahead: which requests should be served with own vehicles, and which reques...
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Bunker fuel oil (ifo), one of the products of petroleum refining, has a strong impact in the production process because it drives the availability of heavy residues that depend on the crude quality. A simplified stoch...
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In this article we discuss weak and strong duality properties of convex semi-infinite programming problems. We use a unified framework by writing the corresponding constraints in a form of cone inclusions. The consequ...
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In this article we discuss weak and strong duality properties of convex semi-infinite programming problems. We use a unified framework by writing the corresponding constraints in a form of cone inclusions. The consequent analysis is based on the conjugate duality approach of embedding the problem into a parametric family of problems parameterized by a finite-dimensional vector.
Since Genetic Network programming (GNP) has been proposed as a new method of evolutionary computation, many studies have been done on its applications which cover not only virtual world problems but also real world sy...
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ISBN:
(纸本)0780393635
Since Genetic Network programming (GNP) has been proposed as a new method of evolutionary computation, many studies have been done on its applications which cover not only virtual world problems but also real world systems like Elevator Group Supervisory Control System (EGSCS) which is a very large scale stochastic dynamic optimization problem. From those researches, most of the significant features of GNP have been verified comparing to Genetic Algorithm (GA) and Genetic programming (GP). Especially, the improvement of the performances on EGSCS using GNP showed an interesting and promising prospect in this field. On the other hand, some studies based on GNP with Reinforcement Learning (RL) revealed a better performance over conventional GNP on some problems such as tile-world models. As a basic study, Reinforcement Learning is introduced in this paper expecting to enhance EGSCS controller using GNP.
We describe and compare heuristic solution methods for a multi-stage stochastic network interdiction problem. The problem is to maximize the probability of sufficient disruption of the flow of information or goods in ...
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We describe and compare heuristic solution methods for a multi-stage stochastic network interdiction problem. The problem is to maximize the probability of sufficient disruption of the flow of information or goods in a network whose characteristics are not certain. In this formulation, interdiction subject to a budget constraint is followed by operation of the network, which is then followed by a second interdiction subject to a second budget constraint. Computational results demonstrate and compare the effectiveness of heuristic algorithms. This problem is interesting in that computing an objective function value requires tremendous effort. We exhibit classes of instances in our computational experiments where local search based on a transformation neighborhood is dominated by a constructive neighborhood.
We study a two-phase, budget-con strained, network-planning problem with multiple hub types and demand scenarios. In each phase, we install (or move) capacitated hubs on selected buildings. We allocate hubs to realize...
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We study a two-phase, budget-con strained, network-planning problem with multiple hub types and demand scenarios. In each phase, we install (or move) capacitated hubs on selected buildings. We allocate hubs to realized demands, under technological constraints. We present a greedy algorithm to maximize expected demand covered and computationally study its performance. (C) 2004 Elsevier B.V. All rights reserved.
In this paper, we propose a framework for selecting a high quality global optimal solution for discrete stochastic optimization problems with a predetermined confidence level using general random search methods. This ...
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In this paper, we propose a framework for selecting a high quality global optimal solution for discrete stochastic optimization problems with a predetermined confidence level using general random search methods. This procedure is based on performing the random search algorithm several replications to get estimate of the error gap between the estimated optimal value and the actual optimal value. A confidence set that contains the optimal solution is then constructed and methods of the indifference zone approach are used to select the optimal solution with high probability. The proposed procedure is applied on a simulated annealing algorithm for solving a particular discrete stochastic optimization problem involving queuing models. The numerical results indicate that the proposed technique indeed locate a high quality optimal solution. (c) 2004 Published by Elsevier B.V. on behalf of IMACS.
This paper addresses the problem of uncertainty in optimizing water networks in process industries. Due to the fact that wastewater flow rates as well as the levels of contaminants may vary widely as a result of chang...
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This paper addresses the problem of uncertainty in optimizing water networks in process industries. Due to the fact that wastewater flow rates as well as the levels of contaminants may vary widely as a result of changes in operational conditions and/or feedstock and product specifications, optimal wastewater network designs should be resilient and able to accommodate such changes. Uncertainties considered in this study are derived from actual operational practice of major water-using units in a typical oil refinery of 400,000 barrels/day throughput. Rather than directly varying the concentrations and mass loads, only seasonal effects have been considered in this research to illustrate applications of the models. Sensitivity analyses reveal that introducing uncertainty in operating conditions results in considerable changes in the connectivity of the units involved in wastewater reuse. The proposed stochastic optimization model produces a flexible wastewater network which is capable of accommodating uncertainties in operating temperature. In the presence of uncertainties, the optimal network minimizes the impact on the reuse connectivity (topology) by providing 32.2 t/h of freshwater in addition to the condensing steam. The stochastic approach adopted in this research has been found to be effective in handling uncertainties and has resulted in flexible and resilient wastewater networks with low expected value of perfect information (EVPI). (c) 2004 Elsevier Ltd. All rights reserved.
We present a stochastic programming approach to capacity planning under demand uncertainty in semiconductor manufacturing. Given multiple demand scenarios together with associated probabilities, our aim is to identify...
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We present a stochastic programming approach to capacity planning under demand uncertainty in semiconductor manufacturing. Given multiple demand scenarios together with associated probabilities, our aim is to identify a set of tools that is a good compromise for all these scenarios. More precisely, we formulate a mixed-integer program in which expected value of the unmet demand is minimized subject to capacity and budget constraints. This is a difficult two-stage stochastic mixed-integer program which cannot be solved to optimality in a reasonable amount of time. We instead propose a heuristic that can produce near-optimal solutions. Our heuristic strengthens the linear programming relaxation of the formulation with cutting planes and performs limited enumeration. Analyses of the results in some real-life situations are also presented. (c) 2005 Wiley Periodicals, Inc.
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