This paper describes the implementation of a new solution approach - Fletcher-Ponnambalam model (FP) - for risk management in hydropower system under deregulated electricity market. The FP model is an explicit method ...
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This paper describes the implementation of a new solution approach - Fletcher-Ponnambalam model (FP) - for risk management in hydropower system under deregulated electricity market. The FP model is an explicit method developed for the first and second moments of the storage state distributions in terms of moments of the inflow distributions. This method provides statistical information on the nature of random behaviour of the system state variables without any discretization and hence suitable for multi-reservoir problems. Also avoiding a scenario-based optimization makes it computationally inexpensive, as there is little growth to the size of the original problem. In this paper, the price uncertainty was introduced into the FP model in addition to the inflow uncertainty. Lake Nipigon reservoir system is chosen as the case study and FP results are compared with the stochastic dual dynamic programming (SDDP). Our studies indicate that the method could achieve optimum operations, considering risk minimization as one of the objectives in optimization.
Recently, airlines and aircraft manufacturers have realized the benefits of the emerging concept of dynamic capacity allocation, and have initiated advanced decision support systems to assist them in this respect. Str...
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Recently, airlines and aircraft manufacturers have realized the benefits of the emerging concept of dynamic capacity allocation, and have initiated advanced decision support systems to assist them in this respect. Strategic airline fleet planning is one of the major issues addressed through such systems. We present background research connected with the dynamic allocation concept, which accounts explicitly for the stochastic nature of passenger demand in the fleet composition problem. We address this problem through a scenario aggregation-based approach and present results on representative case studies based on realistic data. Our investigations establish clear benefits of a stochastic approach as compared with deterministic formulations, as well as its implementation feasibility using state-of-the-art optimization software.
We consider an optimization problem in which some uncertain parameters are replaced by random variables. The minimax approach to stochastic programming concerns the problem of minimizing the worst expected value of th...
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We consider an optimization problem in which some uncertain parameters are replaced by random variables. The minimax approach to stochastic programming concerns the problem of minimizing the worst expected value of the objective function with respect to the set of probability measures that are consistent with the available information on the random data. Only very few practicable solution procedures have been proposed for this problem and the existing ones rely on simplifying assumptions. In this paper, we establish a number of stability results for the minimax stochastic program, justifying in particular the approach of restricting attention to probability measures with support in some known finite set. Following this approach, we elaborate solution procedures for the minimax problem in the setting of two-stage stochastic recourse models, considering the linear recourse case as well as the integer recourse case. Since the solution procedures are modifications of well-known algorithms, their efficacy is immediate from the computational testing of these procedures and we do not report results of any computational experiments. (c) 2004 Elsevier B.V. All rights reserved.
Project scheduling problem is to determine the schedule of allocating resources so as to balance the total cost and the completion time. This paper considers project scheduling problem with stochastic activity duratio...
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Project scheduling problem is to determine the schedule of allocating resources so as to balance the total cost and the completion time. This paper considers project scheduling problem with stochastic activity duration times, which has the objective of minimizing the total cost under some completion time limits. Three types of stochastic models will be built to solve the problem according to different management requirements. Moreover, stochastic simulation and genetic algorithm will be integrated to design a hybrid intelligent algorithm to solve the above models. Finally, some numerical examples are illustrated to show the effectiveness of the algorithm. (c) 2004 Elsevier Inc. All rights reserved.
The first of this two-paper series formulates a stochastic security-constrained multi-period electricity marketclearing problem with unit commitment. The stochastic security criterion accounts for a pre-selected set o...
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The first of this two-paper series formulates a stochastic security-constrained multi-period electricity marketclearing problem with unit commitment. The stochastic security criterion accounts for a pre-selected set of random generator and line outages with known historical failure rates and involuntary load shedding as optimization variables. Unlike the classical deterministic reserve-constrained unit commitment, here the reserve services are determined by economically penalizing the operation of the market by the expected load not served. The proposed formulation is a stochastic programming problem that optimizes, concurrently with the pre-contingency social welfare, the expected operating costs associated with the deployment of the reserves following the contingencies. This stochastic programming formulation is solved in the second companion paper using mixedinteger linear programming methods. Two cases are presented: a small transmission-constrained three-bus network scheduled over a horizon of four hours and the IEEE Reliability Test System scheduled over 24 h. The impact on the resulting generation and reserve schedules of transmission constraints and generation ramp limits, of demand-side reserve, of the value of load not served, and of the constitution of the pre-selected set of contingencies are assessed.
This paper considers a profit-maximizing thermal producer that participates in a sequence of spot markets, namely, day-ahead, automatic generation control (AGC), and balancing markets. The producer behaves as a price-...
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This paper considers a profit-maximizing thermal producer that participates in a sequence of spot markets, namely, day-ahead, automatic generation control (AGC), and balancing markets. The producer behaves as a price-taker in both the day-ahead market and the AGC market but as a potential price-maker in the volatile balancing market. The paper provides a stochastic programming methodology to determine the optimal bidding strategies for the day-ahead market. Uncertainty sources include prices for the day-ahead and AGC markets and balancing market linear price variations with the production of the thermal producer. Results from a realistic case study are reported and analyzed. Conclusions are duly drawn.
The paper deals with the minimization of an integral functional over an L-p space subject to various types of constraints. For such optimization problems, new necessary optimality conditions are derived, based on seve...
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The paper deals with the minimization of an integral functional over an L-p space subject to various types of constraints. For such optimization problems, new necessary optimality conditions are derived, based on several concepts of nonsmooth analysis. In particular, we employ the generalized differential calculus of Mordukhovich and the fuzzy calculus of proximal subgradients. The results are specialized to nonsmooth two-stage and multistage stochastic programs.
This paper develops trading strategies for liquidation of a financial security, which maximize the expected return. The problem is formulated as a stochastic programming problem that utilizes the scenario representati...
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This paper develops trading strategies for liquidation of a financial security, which maximize the expected return. The problem is formulated as a stochastic programming problem that utilizes the scenario representation of possible returns. Two cases are considered, a case with no constraint on risk and a case when the risk of losses associated with trading strategy is constrained by Conditional Value-at-Risk (CVaR) measure. In the first case, two algorithms are proposed;one is based on linear programming techniques, and the other uses dynamic programming to solve the formulated stochastic program. The third proposed algorithm is obtained by adding the risk constraints to the linear program. The algorithms provide path-dependent strategies, i.e., the fraction of security sold depends upon price sample-path of the security up to the current moment. The performance of the considered approaches is tested using a set of historical sample-paths of prices.
General multiperiod optimal consumption and investment problems with proportional transaction costs are investigated in this paper, a GARCH-type process is used to model the risky assets return series so that its time...
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General multiperiod optimal consumption and investment problems with proportional transaction costs are investigated in this paper, a GARCH-type process is used to model the risky assets return series so that its time-varying moments and conditional heteroskedasticity can be properly described. We model this kind of consumption and investment problems as dynamic stochastic optimization problems, which can easily cope with different utility functions and any number of time periods. The procedure to efficiently solve the resulting nonlinear stochastic optimization problem is discussed in detail and a parallelizable decomposition algorithm is devised. Numerical results show the suitability and promise of our methodology.
Owning to similar business nature, it should be possible to directly migrate successful airline revenue management techniques to the hotel domain. However, one of the salient differences between airlines and hotels is...
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Owning to similar business nature, it should be possible to directly migrate successful airline revenue management techniques to the hotel domain. However, one of the salient differences between airlines and hotels is rarely highlighted-the network structure of length of stay or the displacement effect. The hotel patrons go from a first stay-over night to a last stay-over night in consecutive night stays. The arrival demands for multi-night stays and the lengths of stay are stochastic in nature. In this paper, we propose a network optimization model for hotel revenue management under an uncertain environment. The network optimization is in a stochastic programming formulation so as to capture the randomness of the unknown demand (unknown number of arrivals and length of stays). A novel approach of robust optimization techniques for stochastic programming is applied to solve the problem. We also discuss the strategies for hotel management to take into account of risk trade-off, different pricing policies;cancellations and no-show;early check-outs;extended stay and over-booking are discussed. We showed that our proposed model can be modified to adopt these strategic considerations. (C) 2003 Elsevier Ltd. All rights reserved.
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