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10th International Conference on Applied Energy (ICAE)

作者： Shen, Xinwei Guo, Qinglai Sun, Hongbin Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Environm Sci & New Energy Technol Engn Shenzhen Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing Peoples R China

The implementation of regional Integrated Energy System (IES) in urban areas enable the integration of multiple energy systems including electric, cooling and heating power systems. In this paper, we propose a stochastic programming planning method for IES based on Energy Hub model, in which energy price uncertainties from bulk energy systems are considered. In terms of investment planning for IES, the considered candidate options are combined cooling, heating and power (CCHP), gas boiler, centralized air conditioner with different sizes, which are described by several generalized constraints. Moreover, the operation cost of importing natural gas and electric power are considered, where several typical days based on annual load profile are included so that the cost can be simulated more accurately. The price uncertainties are described as normal distribution and simulated by multi-scene, thus a two-stage stochastic programming model is formulated with investment planning as first-stage problem and IES operation as second-stage problem. Case studies demonstrated the effectiveness of proposed model, in which the benefits of IES resulted from complementary and coordination between multi energy systems are illustrated. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： Integrated Energy System Multi-Energy Systems two-stage stochastic programming price uncertainties multi-scene

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A two-stage stochastic programming Model for Outpatient Appointment Scheduling

A Two-stage Stochastic Programming Model for Outpatient Appo...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Ma Shuang Chen Songlin Cai Xiaotian Univ Sci & Technol Beijing Donlinks Sch Econ & Management Beijing 100083 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Chinese Acad Sci & Technol Dev Beijing Peoples R China

ISBN: (纸本)9781728138046

Healthcare providers are facing increasing pressure to improve efficiency for healthcare delivery. Efficient appointment scheduling can improve retention and quality of healthcare services. This paper studies outpatient appointment scheduling as a two-stage stochastic optimization problem with a multi-server model. The first stage optimizes doctor planning according to capacity constraints and patient requests, based on which the second stage optimizes patient planning by taking into account uncertain service durations. Numerical experiments have been conducted and shown that the proposed two-stage stochastic programming model can reduce hospital operating costs while improving patient satisfaction.

关键词： Appointment scheduling outpatient service two-stage stochastic programming

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Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach

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APPLIED ENERGY 2018年 222卷 932-950页

作者： Mavromatidis, Georgios Orehounig, Kristina Carmeliet, Jan Swiss Fed Inst Technol Chair Bldg Phys Stefano Franscini Pl 1 CH-8093 Zurich Switzerland Empa Duebendorf Lab Urban Energy Syst Dubendorf Switzerland Empa Duebendorf Lab Multiscale Studies Bldg Phys Dubendorf Switzerland

Uncertainty introduces significant complexity to the design process of distributed energy systems (DES) and introduces the risk of suboptimal decisions when the design is performed deterministically. Therefore, it is important that computational DES design models are able to account for the most relevant uncertainty sources when identifying optimal DES configurations. In this paper, a model for optimal DES design under uncertainty is presented and is formulated as a two-stage stochastic Mixed-Integer Linear Program. As uncertain parameters, energy carrier prices and emission factors, building heating and electricity demands, and incoming solar radiation patterns are considered and probabilistic scenarios are used to describe their uncertainty. The model seeks to make cost-optimal DES design decisions (technology selection and sizing) before these uncertain parameters are known, while it also identifies the optimal operation of the selected DES configuration for multiple uncertain scenarios. Moreover, two strategies for emission reduction are employed that set CO2 limits either to the system's average emissions under uncertainty ('neutral' strategy) or individually to the system's emissions for every possible uncertainty outcome to ensure a more robust emission performance ('aggressive' strategy). To illustrate the model's application, the design of a DES for a Swiss urban neighbourhood of 10 buildings is investigated. Multiple optimal DES configurations are obtained by using the 'neutral' and 'aggressive' emission reduction strategies and the trade-offs between the systems' economic and emission performance are analysed. Moreover, the optimal DES are contrasted in terms of technology selection and energy consumption shares among fossil fuels, grid electricity and renewable energy. Finally, all model outputs are compared to results obtained from a deterministic design model. The comparison showed that the deterministic model leads to underestimations of the system co

关键词： Distributed energy systems Uncertainty Multi-objective optimisation two-stage stochastic programming Scenario generation Scenario reduction

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Combining two-stage stochastic programming and recoverable robustness to minimize the number of late jobs in the case of uncertain processing times

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JOURNAL OF SCHEDULING 2018年第6期21卷 607-617页

作者： van den Akker, Marjan Hoogeveen, Han Stoef, Judith Univ Utrecht Dept Informat & Comp Sci Princetonpl 5 NL-3584 CC Utrecht Netherlands

Minimizing the number of late jobs on a single machine is a classic scheduling problem, which can be used to model the situation that from a set of potential customers, we have to select as many as possible whom we want to serve, while selling no to the other ones. This problem can be solved by Moore-Hodgson's algorithm, provided that all data are deterministic. We consider a stochastic variant of this problem, where we assume that there is a small probability that the processing times differ from their standard values as a result of some kind of disturbance. When such a disturbance occurs, then we must apply some recovery action to make the solution feasible again. This leads us to the area of recoverable robustness, which handles this uncertainty by modeling each possible disturbance as a scenario;in each scenario, the initial solution must then be made feasible by applying a given, simple recovery algorithm to it. Since we cannot accept previously rejected customers, our only option is to reject customers that would have been served in the undisturbed case. Our problem therefore becomes to find a solution for the undisturbed case together with a feasible recovery to every possible disturbance. Our goal hereby is to maximize the expected number of served customers;we assume here that we know the probability that a given scenario occurs. In this respect, our problem falls outside the area of the standard' recoverable robustness, which contains the worst-case recovery cost as a component of the objective. Therefore, we consider our approach as a combination of two-stage stochastic programming and recoverable robustness. We show that this problem is hard in the ordinary sense even if there is only one scenario, and we present some sufficient conditions that allow us to find a part of the optimal solution in polynomial time. We further evaluate several solution methods to find an optimal solution, among which are dynamic programming, branch-and-bound, and branch-and-p

关键词： two-stage stochastic programming Recoverable robustness stochastic scheduling Single machine scheduling Late jobs

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A chance-constrained two-stage stochastic programming model for humanitarian relief network design

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2018年 108卷 55-83页

作者： Elci, Ozgun Noyan, Nilay Sabanci Univ Ind Engn Program TR-39956 Istanbul Turkey Sabanci Univ Smart Mobil & Logist Lab TR-39956 Istanbul Turkey

We consider a stochastic pre-disaster relief network design problem, which mainly determines the capacities and locations of the response facilities and their inventory levels of the relief supplies in the presence of uncertainty in post-disaster demands and transportation network conditions. In contrast to the traditional humanitarian logistics literature, we develop a chance-constrained two-stage mean-risk stochastic programming model. This risk-averse model features a mean-risk objective, where the conditional value at-risk (CVaR) is specified as the risk measure, and enforces a joint probabilistic constraint on the feasibility of the second-stage problem concerned with distributing the relief supplies to the affected areas in case of a disaster. To solve this computationally challenging stochastic optimization model, we employ an exact Benders decomposition-based branch and-cut algorithm. We develop three variants of the proposed algorithm by using alternative representations of CVaR We illustrate the application of our model and solution methods on a case study concerning the threat of hurricanes in the Southeastern part of the United States. An extensive computational study provides practical insights about the proposed modeling approach and demonstrates the computational effectiveness of the solution framework. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Humanitarian logistics two-stage stochastic programming Risk-averse Probabilistic constraint Conditional value-at-risk Benders decomposition

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Optimal logistics planning for modular construction using two-stage stochastic programming

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AUTOMATION IN CONSTRUCTION 2018年 94卷 47-61页

作者： Hsu, Pei-Yuan Angeloudis, Panagiotis Aurisicchio, Marco Imperial Coll London Dyson Sch Design Engn London SW7 2AZ England Imperial Coll London Dept Civil & Environm Engn London SW7 2AZ England

The construction sector is currently undergoing a shift from stick-built construction to modular building systems that take advantage of modern prefabrication techniques. Long established in-situ construction practices are thus being replaced by processes imported from the manufacturing sector, where component fabrication takes place within a factory environment. As a result of this transformation, current construction supply chains, which have focused on the delivery of raw materials to sites, are no longer apt and need to make way to new, strengthened, and time-critical logistics systems. The aim of this study is to establish a mathematical model for the optimisation of logistics processes in modular construction covering three tiers of operation: manufacturing, storage and assembly. Previous studies have indicated that construction site delays constitute the largest cause of schedule deviations. Using the model outlined in this paper we seek to determine how factory manufacturing and inventory management should react to variations in the demand on construction sites. A two-stage stochastic programming model is developed to capture all possible demand variations on site. The model is evaluated using a case study from the residential construction sector. The application shows that the model is effective and can serve as decision support to optimise modular construction logistics.

关键词： Logistics Modular construction Inventory Supply chain two-stage stochastic programming

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Regional Integrated Energy System Planning considering Energy Price Uncertainties: A two-stage stochastic programming Approach

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Energy Procedia 2019年 158卷 6564-6569页

作者： Xinwei Shen Qinglai Guo Hongbin Sun Shenzhen Enviromental Science and New Energy Technology Engineering Laboratory Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen People’s Republic of China The Department of Electrical Engineering State Key Laboratory of Power System Tsinghua University Beijing People’s Republic of China

关键词： Integrated Energy System Multi-Energy Systems two-stage stochastic programming price uncertainties multi-scene

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two-stage stochastic programming for the railroad blocking problem with uncertain demand and supply resources

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 106卷 275-286页

作者： Hasany, Reza Mohammad Shafahi, Yousef Sharif Univ Technol Dept Civil Engn Azadi AvePOB 11155-8639 Tehran Iran

The railroad blocking problem is classified in the tactical level of freight rail transportation problems. The objective of this problem is to determine the optimal paths for each shipment such that the railway limitations are satisfied. In this problem, the quantities of both demand and supply resource indicators are often assumed to be certain and known, but because a blocking solution is designed for a relatively long period of time, this assumption is not reasonable. In this paper, we have developed a two-stage stochastic program for this problem to consider the uncertainty inherent in demand and supply resource indicators. Due to the size and complexity of the stochastic program and the impossibility of using commercial software in even the simplest instances, two solution methods have been proposed. The first method developed is based on the L-Shaped method, and the second method is a modification of the first one that uses a new initial solution (which is obtained by adapting a side optimization model) together with the 1 Shaped method. Extensive experiments on test networks show that the two methods outperform the commercial software and that the second method is superior to the first one. We finally present the application of the uncertain model and the computational results of the second method for the Railways of Iran as a real-size example, and we show that the application of the stochastic model could reduce total cost by more than 12 million dollars per three-month horizon compared with the deterministic solution. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Freight rail transportation Uncertainty two-stage stochastic programming Railroad blocking problem

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A two-stage stochastic programming Model for Optimal Reactive Power Dispatch with High Penetration Level of Wind Generation

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2017年第1期12卷 53-63页

作者： Cui, Wei Yan, Wei Lee, Wei-Jen Zhao, Xia Ren, Zhouyang Wang, Cong Chongqing Univ Dept Elect Engn Chongqing Peoples R China Chongqing Univ Dept Elect Engn Chongqing Peoples R China Univ Texas Arlington Dept Elect Engn Arlington TX 76019 USA

The increasing of wind power penetration level presents challenges in classical optimal reactive power dispatch (ORPD) which is usually formulated as a deterministic optimization problem. This paper proposes a two-stage stochastic programming model for ORPD by considering the uncertainties of wind speed and load in a specified time interval. To avoid the excessive operation, the schedule of compensators will be determined in the first-stage while accounting for the costs of adjusting the compensators (CACs). Under uncertainty effects, on-load tap changer (OLTC) and generator in the second-stage will compensate the mismatch caused by the first-stage decision. The objective of the proposed model is to minimize the sum of CACs and the expected energy loss. The stochastic behavior is formulated by three-point estimate method (TPEM) to convert the stochastic programming into equivalent deterministic problem. A hybrid Genetic Algorithm-Interior Point Method is utilized to solve this large-scale mixed-integer nonlinear stochastic problem. two case studies on IEEE 14-bus and IEEE 118-bus system are provided to illustrate the effectiveness of the proposed method.

关键词： Costs of adjusting the compensators two-stage stochastic programming Three-point estimate method Uncertainty Wind power

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A two-stage stochastic programming model for inventory management in the blood supply chain

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2017年 187卷 27-41页

作者： Dillon, Mary Oliveira, Fabricio Abbasi, Babak RMIT Univ Sch Business IT & Logist Melbourne Vic 3000 Australia RMIT Univ Sch Sci Melbourne Vic 3000 Australia

Managing inventories in the blood supply chain is a challenging task, mainly due to the uncertain nature of the demand for blood units, the perishable nature of the blood, and a strong subjective bias towards criteria other than cost minimisation. In this paper, we propose a two-stage stochastic programming model for defining optimal periodic review policies for red blood cells inventory management that focus on minimising operational costs, as well as blood shortage and wastage due to outdating, taking into account perishability and demand uncertainty. The adoption of this framework allows the consideration of more general stochastic processes to model the demand uncertainty than approaches currently available in literature. Moreover, this framework renders a model that can be solved efficiently by general purpose off-the-shelf optimisation software. To illustrate the potential benefits of adopting the proposed model to support the definition of optimal blood inventory control policies, a case study is presented considering realistic data representing the daily demand for blood, which was generated using the average and standard deviation of the demand for eight types of blood. Results suggest meaningful insights concerning practices that could lead to improvement in the blood supply chain performance. In particular, we observed that it would be possible to revise the current inventory control policies by reducing current target levels to diminish wastage and total cost without compromising the service level. Finally, the consideration of blood substitutions showed further improvement in the performance of the blood inventory management system.

关键词： Blood supply chain Inventory management stochastic demand Perishability two-stage stochastic programming

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