Equipment selection is a key strategic decision in the design of a material handling system, because an improper one will lead to operational problems and unnecessary investment costs. It involves determining the numb...
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Equipment selection is a key strategic decision in the design of a material handling system, because an improper one will lead to operational problems and unnecessary investment costs. It involves determining the number and combination of loaders and trucks which will move the material, fulfilling a specified production schedule. Previous works have addressed this problem with deterministic approaches, without considering the inter-dependent availability of trucks and loaders. In order to fill this gap, we developed a stochastic model that combines the selection and equipment replacement problems, subject to a stochastic production rate constraint. This is a new idea that will help decision-makers to decide faster and more reliable. The proposed model optimises the fleet by minimising the total life cycle costs. To solve it, we used a linearisation approach that reduces the computational effort. We tested our approach with a benchmark model, using a mining case study. Results indicate that the solutions ensure with a high probability a determined production target, producing good robust solutions compared to the deterministic counterpart.
Renewable energy sources such as wind and solar have received much attention in recent years, and large amounts of renewable generation are being integrated into electricity networks. A fundamental challenge in power ...
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Renewable energy sources such as wind and solar have received much attention in recent years, and large amounts of renewable generation are being integrated into electricity networks. A fundamental challenge in power system operation is to handle the intermittent nature of renewable generation. In this paper, we present a stochasticprogramming approach to solve a multiperiod optimal power flow problem under renewable generation uncertainty. The proposed approach consists of two stages. In the first stage, operating points of the conventional power plants are determined. The second stage realizes generation from the renewable resources and optimally accommodates it by relying on the demand-side flexibilities. The proposed model is illustrated on a 4-bus and a 39-bus system. Numerical results show that substantial benefits in terms of redispatch costs can be achieved with the help of demand side flexibilities. The proposed approach is tested on the standard IEEE test networks of up to 300 buses and for a wide variety of scenarios for renewable generation.
stochasticprogramming is a powerful analytical method in order to solve sequential decision-making problems under uncertainty. We describe an approach to build such stochasticlinearprogramming models. We show that ...
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stochasticprogramming is a powerful analytical method in order to solve sequential decision-making problems under uncertainty. We describe an approach to build such stochasticlinearprogramming models. We show that algebraic modeling languages make it possible for non-specialist users to formulate complex problems and have solved them by powerful commercial solvers. We illustrate our point in the case of option contracts in supply chain management and propose a numerical analysis of performance. We propose easy-to-implement discretization procedures of the stochastic process in order to limit the size of the event tree in a multi-period environment. (C) 2003 Elsevier Ltd. All rights reserved.
Recruitment of persons for various assignments with required talents in an organization is an important feature, since it plays a vital role in the growth of the organization. To achieve the required expertise in recr...
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Recruitment of persons for various assignments with required talents in an organization is an important feature, since it plays a vital role in the growth of the organization. To achieve the required expertise in recruitment, in this paper linear stochastic programming (LSP) is applied along with cluster analysis technique. The aim of this paper is to obtain an optimal allocation of persons to different jobs, so that the time taken to complete all the jobs is minimum. The time taken for a person to complete a job is assumed to follow Weibull distribution. The parameters of Weibull distribution is obtained through Maximum Likelihood Estimator (MLE) approach, along with Cohen's iterative process.
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