This paper proposes a solution approach for the optimal capacity planning problem in multi-area power systems in the presence of randomness in the availability of additional units. The problem is formulated as a two-s...
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ISBN:
(纸本)9781424412969
This paper proposes a solution approach for the optimal capacity planning problem in multi-area power systems in the presence of randomness in the availability of additional units. The problem is formulated as a two-stage recourse model in the mixed-integer stochastic programming framework. The uncertainties in area generation, transmission fines, and load are incorporated in the model and characterized by their discrete probability distributions. Reliability index used in this analysis is expected unserved energy as this index integrates duration and magnitude of load loss. The objective function is to minimize expansion cost in the first stage and, at the same time, to minimize operation and expected unserved energy costs in the second stage. The first stage decision variables on the number of additional units are represented by binary variables to allow individual unit availability considerations. The solution algorithm utilizes the L-shaped method.
This study is concerned with the determination of an optimal appointment schedule in an outpatient-inpatient hospital system where the inpatient exams can be cancelled based on certain rules while the outpatient exams...
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This study is concerned with the determination of an optimal appointment schedule in an outpatient-inpatient hospital system where the inpatient exams can be cancelled based on certain rules while the outpatient exams cannot be cancelled. stochastic programming models were formulated and solved to tackle the stochasticity in the procedure durations and patient arrival patterns. The first model, a two-stage stochastic programming model, is formulated to optimize the slot size. The second model further optimizes the inpatient block (IPB) placement and slot size simultaneously. A computational method is developed to solve the second optimization problem. A case study is conducted using the data from Magnetic Resonance Imaging (MRI) centers of Lahey Hospital and Medical Center (LHMC). The current schedule and the schedules obtained from the optimization models are evaluated and compared using simulation based on FlexSim Healthcare. Results indicate that the overall weighted cost can be reduced by 11.6% by optimizing the slot size and can be further reduced by an additional 12.6% by optimizing slot size and IPB placement simultaneously. Three commonly used sequencing rules (IPBEG, OPBEG, and a variant of ALTER rule) were also evaluated. The results showed that when optimization tools are not available, ALTER variant which evenly distributes the IPBs across the day has the best performance. Sensitivity analysis of weights for patient waiting time, machine idle time and exam cancellations further supports the superiority of ALTER variant sequencing rules compared to the other sequencing methods. A Pareto frontier was also developed and presented between patient waiting time and machine idle time to enable medical centers with different priorities to obtain solutions that accurately reflect their respective optimal tradeoffs. An extended optimization model was also developed to incorporate the emergency patient arrivals. The optimal schedules from the extended model show on
In this paper, by analyzing the essential characteristic of stochastic programming and the deficiencies of the existing methods, we propose the concept of synthesizing effect function for processing the objective func...
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ISBN:
(纸本)9780769538044
In this paper, by analyzing the essential characteristic of stochastic programming and the deficiencies of the existing methods, we propose the concept of synthesizing effect function for processing the objective function and constraints, and further we give an axiomatic system for synthesizing effect function. Finally, we establish a general solution model based on synthesizing effect function for stochastic programming problem, and analyze the model through an example. All the results indicate that our method not only includes the existing methods for stochastic programming, but also effectively merge the decision preferences into the solution, so it can be widely used in many fields such as complicated system optimization and artificial intelligence etc.
Transmission expansion planning (TEP) is helping the system operator to decide the optimal solution for building new lines and in the same time to increase the reliability and safety of the existing power system. The ...
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ISBN:
(纸本)9781538639436
Transmission expansion planning (TEP) is helping the system operator to decide the optimal solution for building new lines and in the same time to increase the reliability and safety of the existing power system. The proposed problem is a mixed-integer nonlinear programming problem (MINLP) and it is solved using stochastic programming. stochastic programming is applied when uncertain environment occurs, in this case the uncertain environment refers to the production of renewable energy sources (RES) and its dependence on the short-term weather conditions. stochastic Optimization weights all scenarios considered in this paper in order to obtain an expected total cost. The expected total cost includes the cost associated with the construction of new transmission lines, generation cost and load shedding cost.
The possibility of successful applications of stochastic programming decision models has been limited by the assumed complete knowledge of the distribution F of the random parameters as well as by the limited scope of...
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When supply chain networks become more complex through the application of modern trends such as outsourcing and global marketing, supply chains become more uncertain. Supply chain planning under uncertainty is a chall...
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ISBN:
(纸本)9783319684963;9783319684956
When supply chain networks become more complex through the application of modern trends such as outsourcing and global marketing, supply chains become more uncertain. Supply chain planning under uncertainty is a challenge for decision makers. Without considering uncertainties in supply chain planning, global supply chains may suffer enormous economic costs. When probability distributions for uncertain parameters can be estimated, stochastic programming can be used for capturing the characteristics of uncertainties and generating flexible production and transportation plans for global supply chains. This paper presents an outline on how to use stochastic programming for decision support under uncertainty. This includes a high level exposition of how to quantify uncertainties, develop stochastic programming models, generate representative scenarios, apply algorithms for model solving, undertake experimental design and present computational results. Through exemplifying supply chain planning and decision making under uncertainty by using stochastic programming, this paper aims to provide a valuable reference for future research in this area.
Model predictive thrust control (MPTC) is one of the most effective approaches for linear induction motor (LIM) drive system. It can achieve the optimization of multiple objectives. However, the process of tuning the ...
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ISBN:
(纸本)9781728164014
Model predictive thrust control (MPTC) is one of the most effective approaches for linear induction motor (LIM) drive system. It can achieve the optimization of multiple objectives. However, the process of tuning the weighting factors in the objective function is the main drawback of MPTC. It greatly increases the computation burden. In this paper, the tuning process of weighting factors is regarded as a random sampling process. Then, a novel weighting factor optimization method based on the stochastic programming technique is proposed to select the suitable control action for LIM. It will optimize with flux and thrust together to avoid the adjustment of weighting factor. In this paper, the optimal model can be solved by Monte Carlo simulation. At last, the simulation results have shown better dynamic and steady state performance of the proposed method.
The authors consider the problem of active international portfolio management with basket options to achieve optimal asset allocation and combined market risk and currency risk management via multi-stage stochastic pr...
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The authors consider the problem of active international portfolio management with basket options to achieve optimal asset allocation and combined market risk and currency risk management via multi-stage stochastic programming(MSSP). The authors note particularly the novel consideration and signi?cant bene?t of basket options in the context of portfolio optimization and risk *** empirical tests strongly demonstrate that basket options consistently have more clearly improvement on portfolio performances than a portfolio of vanilla options written on the same underlying assets. The authors further show that the MSSP model provides as a supportive tool for asset allocation,and a suitable test bed to empirically investigate the performance of alternative strategies.
In recent years the deregulation of energy markets and expansion of volatile renewable energy supplies has triggered an increased interest in stochastic optimization models for thermal and hydro-thermal scheduling. Se...
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In recent years the deregulation of energy markets and expansion of volatile renewable energy supplies has triggered an increased interest in stochastic optimization models for thermal and hydro-thermal scheduling. Several studies have modelled this as stochastic linear or mixed-integer optimization problems. Although a variety of efficient solution techniques have been developed for these models, little is published about the added value of stochastic models over deterministic ones. In the context of day-ahead and intraday unit commitment under wind uncertainty, we compare two-stage and multi-stage stochastic models to deterministic ones and quantify their added value. We show that stochastic optimization models achieve minimal operational cost without having to tune reserve margins in advance, and that their superiority over deterministic models grows with the amount of uncertainty in the relevant wind forecasts. We present a modification of the WILMAR scenario generation technique designed to match the properties of the errors in our wind forcasts, and show that this is needed to make the stochastic approach worthwhile. Our evaluation is done in a rolling horizon fashion over the course of two years, using a 2020 central scheduling model of the British National Grid with transmission constraints and a detailed model of pump storage operation and system-wide reserve and response provision. Solving stochastic problems directly is computationally intractable for large instances, and alternative approaches are required. In this study we use a Dantzig-Wolfe reformulation to decompose the problem by scenarios. We derive and implement a column generation method with dual stabilisation and novel primal and dual initialisation techniques. A fast, novel schedule combination heuristic is used to construct an optimal primal solution, and numerical results show that knowing this solution from the start also improves the convergence of the lower bound in the column generation
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