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Should environmental constraints be considered in linear programming based water value calculators?

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年第0期117卷 105662-000页

作者： Guisandez, Ignacio Ignacio Perez-Diaz, Juan Nowak, Wolfgang Haas, Jannik Univ Politecn Madrid Dept Hydraul Energy & Environm Engn E-28040 Madrid Spain Univ Stuttgart Dept Stochast Simulat & Safety Res Hydrosyst IWS D-70569 Stuttgart Germany

We evaluate the practical usefulness of incorporating maximum ramping rates and minimum environmental flows into a linear programming based water value calculator for hydropower plants that participate in the day-ahead electricity market. The methodology consists of three steps: first computing the water value once with and once without environmental constraints, then simulating the plant operations using each water value, and finally comparing the simulation profits. A set of nine representative hydropower plants formed by combinations of three real locations (in Colombia, Norway and Spain) and three turbine configurations (from one to three Francis units) are individually analyzed. Each plant is simulated in two synthetic 10-year long series subject to fifteen combinations of maximum ramping rates and minimum flows with the two above-mentioned water values, totaling 540 simulations. The results indicate that incorporating the analyzed environmental constraints into a linear programming based water value calculator can be significantly profitable only when the hydropower plants have only one or at most two turbines.

关键词： Maximum ramping rate Minimum environmental flow Linear programming Mixed integer linear programming stochastic dynamic programming

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Myopic optimal policy for a multi-period, two-delivery-lead-times, stochastic inventory problem with minimum cumulative commitment and capacity

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2011年第2期133卷 719-727页

作者： Xu, Ningxiong Cornell Univ Sch Civil & Environm Engn Ithaca NY 14853 USA

This paper studies a single-product, multi-period, stochastic inventory problem that imposes the lower and upper bounds on the cumulative order quantity during a planning horizon and allows two delivery lead times. This model includes three features. The first one is that a buyer purchases a fixed capacity from a supplier at the beginning of a planning horizon and the buyer's total cumulative order quantity during the planning horizon is constrained with the capacity. The second one is that the buyer agrees to purchase the product at least a certain percentage of the purchased capacity during the planning horizon. The third one is that the supplier allows the buyer to order the product with two-delivery-lead-times. We identify conditions under which a myopic ordering policy is optimal. We also develop an algorithm to calculate the optimal capacity when the minimum cumulative order quantity commitment is a certain percentage of the capacity. We then use the algorithm to evaluate the effect of the various parameters on the buyer's minimum expected total cost during the planning horizon. Our computation shows that the buyer would benefit from the commitments and two-delivery-lead-times. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Additive function Capacity Minimum cumulative order quantity commitment Myopic policy stochastic dynamic programming Two-delivery-lead-times

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Reducing passenger waiting time in oversaturated metro lines with passenger flow control policy

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2023年 117卷

作者： Liang, Jinpeng Zang, Guangzhi Liu, Haitao Zheng, Jianfeng Gao, Ziyou Dalian Maritime Univ Sch Transportat Engn Dalian Peoples R China China Acad Railway Sci Corp Ltd Inst Comp Technol Beijing Peoples R China Sichuan Univ Business Sch Chengdu Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China

Oversaturation during peak hours brings about severe challenges for metro operations management in megacities. It can deteriorate passengers' service level experience and increase the safety risk at congested platforms. This paper focuses on designing an online passenger flow control policy to manage passenger flow in each origin-destination (OD) pair so that the total passenger waiting time during the research horizon can be minimized. Suppose that the OD demand information reveals sequentially over time, we formulate the online passenger flow control problem as stochastic dynamic programming (DP). An efficient online adaptive policy is designed to guide the real-time flow control decisions at each stage. To evaluate the performance of our approach, we exploit the realistic transit data from the Beijing metro system to carry out a series of numerical experiments. The computational results show that our approach can significantly reduce the expected total passenger waiting time as well as alleviate metro station congestion compared with the first-come-first-serve (FCFS) policy. The benefits of our approach are obtained by exploiting the reusable nature of train capacity to transport more passengers during rush hours.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： metro system oversaturation passenger flow control online algorithm stochastic dynamic programming

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Model-Based stochastic Optimal Air-Fuel Ratio Control With Residual Gas Fraction of Spark Ignition Engines

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2014年第3期22卷 896-910页

作者： Yang, Jun Shen, Tielong Jiao, Xiaohong Yanshan Univ Dept Elect Engn Qinhuangdao 066004 Peoples R China Sophia Univ Dept Mech Engn Tokyo 1028554 Japan

In this paper, a stochastic optimal control scheme for the air-fuel ratio is proposed, which considers the cyclic variations of the residual gas fraction (RGF). Initially, a cylinder pressure-based measurement of the RGF is derived by following the physics of inlet-exhaust process. Then, a dynamical model is presented to describe the cyclic variation of the air charge, fuel charge, and combustion products under a cyclically varied RGF, where the RGF is modeled as a Markovian stochastic process. Using this model, a feedback control law is derived, which optimizes the quadratic cost function in the stochastic sense with respect to the stochastic property of the residual gas. The cost function reflects the tradeoff between the accuracy of the regulation of the air-fuel ratio with the fluctuation in the fuel injection. Finally, a sampling process-based statistical analysis for the RGF is presented based on the experiments conducted on a full-scaled gasoline engine test bench, and the proposed control law is validated based on a numerical simulation and experiments.

关键词： Air-fuel ratio residual gas fraction (RGF) spark ignition engine stochastic dynamic programming

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Hybridizing ANN-NSGA-II model with genetic programming method for reservoir operation rule curve determination (Case study Zayandehroud dam reservoir)

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SOFT COMPUTING 2021年第22期25卷 14081-14108页

作者： Moeini, Ramtin Nasiri, Kamran Univ Isfahan Fac Civil Engn & Transportat Dept Civil Engn Esfahan *** Iran

One of the most important and effective works of water resource planning and management is determining the specific, applicable, regulated operating policies of the Zayandehroud dam reservoir, as a case study, in which it should be user-friendly and straightforward for the operator. For this purpose, different methods have been proposed in which each of them has its limitations. Due to the unique capabilities of the genetic programming (GP) model, here, this method is used to determine the operating rule curves and policies of the dam reservoir. For this purpose, here, two cases are proposed in which, in the first case, each month is individually simulated and modeled. However, in the second case, all months are simulated simultaneously. A second case is proposed here to determine simple and more applicable operation rule curves. In addition, two approaches are suggested for each case in which in the first approach, the influential input variables are selected by presenting the hybrid method. In the proposed hybrid method, the artificial neural network (ANN) model is equipped with non-dominated sorting genetic (NSGA-II) algorithm leading to a hybrid method named the ANN-NSGA-II method. However, in the second approach, the influential input variables are selected automatically using the GP method. Here, the hybrid method is proposed and used to overcome the limitations of existing usual method. In other words, it is proposed to reduce the number of influential input variables of data-driven methods and select the effective ones. The obtained results of all proposed cases and approaches are presented and compared with the standard operation policy method, stochastic dynamic programming, ANN model, and NLP method. Comparison of the results shows the acceptable performance of the proposed cases and approaches. In other words, the best-obtained values of (stability index) SI index and water deficit (objective function value) are 49.3% and 32, respectively.

关键词： Operation rule curve stochastic water inflow values Genetic programming stochastic dynamic programming Artificial neural network

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Multiperiod portfolio optimization with terminal liability: Bounds for the convex case

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2005年第1-2期32卷 29-59页

作者： Edirisinghe, NCP Univ Tennessee Coll Business Adm Management Sci Program Knoxville TN 37996 USA

This paper is concerned with an investor trading in multiple securities over many time periods in order to meet an outstanding liability at some future date. The investor is concerned with maximizing the expected profits from portfolio rebalancing under an initial wealth restriction to meet the future liabilities. We formulate the problem as a discrete-time stochastic optimization model and allow asset prices to have continuous probability distributions on compact domains. For the case of Markovian price uncertainty and convex terminal liability, we develop a simplicial approximation, under which bounds on the problem can be computed efficiently. Computations only require evaluating a dynamic programming recursion, which thus, allows its application to problems with a large number of trading periods. The bounds are tight in that they are exact in certain cases. Numerical results are given to demonstrate the computational efficiency of the procedure.

关键词： portfolio optimization stochastic multi-stage programming generalized moment problems stochastic dynamic programming option pricing

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The convergence of value iteration in average cost Markov decision chains

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OPERATIONS RESEARCH LETTERS 1996年第1期19卷 11-16页

作者： Sennott, LI Department of Mathematics 4520 Illinois State University Normal IL 61790-4520 USA

Let J be the (constant) minimum long-run expected average cost in a Markov decision chain with countable state space. We desire the existence of an average cost optimal stationary policy and, in addition, that J is the limit of nu(n)(.)/n, where nu(n)(.) is the minimum n-step expected cost. Three sets of sufficient conditions for this to hold are given. The results generalize Ghosh and Marcus (1992).

关键词： stochastic dynamic programming average cost Markov decision process value iteration

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Optimal Condition-Based Mission Abort Decisions

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IEEE TRANSACTIONS ON RELIABILITY 2023年第1期72卷 408-425页

作者： Qiu, Qingan Maillart, Lisa M. Prokopyev, Oleg A. Cui, Lirong Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15261 USA Qingdao Univ Coll Qual & Standardizat Qingdao 266071 Peoples R China

Failures of safety-critical mission-based systems, such as aircraft and submarines, could result in significant losses and damage. To enhance the survivability of such systems, their missions may be aborted if the failure risk becomes too high. We investigate such mission abort policies under a completely observed two-stage degradation process that progresses stochastically from "normal" to "defective" to "failure." Mission abort decisions are considered as a function of the duration of the defective stage. This mission abort problem is formulated as a discrete-time optimal stopping problem with the goal of minimizing the expected total cost of mission failure and system failure. In addition to deriving some structural properties, we also numerically evaluate several intuitive heuristic policies. Finally, a joint optimization problem is formulated to simultaneously identify the optimal mission abort policy and the optimal investment to delay system deterioration.

关键词： Costs Degradation Stress Optimization Inspection dynamic programming Decision making Delay time model delayed deterioration mission abort stochastic dynamic programming

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The stochastic container relocation problem with flexible service policies

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2020年 141卷 116-163页

作者： Feng, Yuanjun Song, Dong-Ping Li, Dong Zeng, Qingcheng Univ Liverpool Sch Management Chatham St Chatham L69 7ZH Kent England Dalian Maritime Univ Sch Maritime Econ & Management 1 Linghai Rd Dalian 116026 Peoples R China

This paper investigates the stochastic Container Relocation Problem in which a flexible service policy is adopted in the import container retrieval process. The flexible policy al-lows the terminal operators to determine the container retrieval sequence to some extent, which provides more opportunity for reducing the number of relocations and the truck waiting times. A more general probabilistic model that captures customers' arrival preference is presented to describe the randomness for external truck arrivals within their appointed time windows. Being a multi-stage stochastic sequential decision-making problem, it is first formulated into a stochastic dynamic programming (SDP) model to minimize the expected number of relocations. Then, the SDP model is extended considering a secondary objective representing the truck waiting times. Tree search-based algorithms are adapted to solve the two models to their optimality. Heuristic algorithms are designed to seek high-quality solutions efficiently for larger problems. A discrete-event simulation model is developed to evaluate the optimal solutions and the heuristic solutions respectively on two performance metrics. Extensive computational experiments are performed based on instances from literature to verify the effectiveness of the proposed models and algorithms. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： stochastic container relocation problem Appointment time window Flexible service stochastic dynamic programming Tree search-based algorithm

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An analysis of airport-airline vertical relationships with risk sharing contracts under asymmetric information structures

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2014年 44卷 80-97页

作者： Hihara, Katsuya Univ Tokyo GraSPP Bunkyo Ku Tokyo Japan

We analyze the double moral hazard problem at the joint venture type airport-airline vertical relationship, where two parties both contribute efforts to the joint venture but neither of them can see the other's efforts. With the continuous-time stochastic dynamic programming model, we show that by the de-centralized utility maximizations of two parties under very strict conditions, i.e., optimal efforts' cost being negligible and their risk averse parameters both asymptotically approaching to zero, the vertical contract could be agreed as the optimal sharing rule, which is the linear function of the final state with the slope being the product of their productivity difference and uncertainty (diffusion rate) level index. If both parties' productivities are same, or the diffusion rate of the underlying process is unity, optimal linear sharing rule do not depend on the final state. If their conditions not dependent on final state are symmetric as well, then risk sharing disappears completely. In numerical examples, we illustrate the complex impact of uncertainty increase and end-of-period load factor improvement on the optimal sharing rule, and the relatively simple impact on total utility levels. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Airport-airline vertical relationship Load factor guarantee mechanism Double hidden action/moral hazard Risk sharing stochastic dynamic programming

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