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Design of a reliable hierarchical location-allocation model under disruptions for health service networks: A two-stage robust approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 109卷 130-150页

作者： Zarrinpoor, Naeme Fallahnezhad, Mohammad Saber Pishvaee, Mir Saman Shiraz Univ Technol Dept Ind Engn Shiraz Iran Yazd Univ Dept Ind Engn Yazd Iran Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

This study proposes a novel reliable hierarchical location-allocation model for health service network designs where facilities are subject to the risk of disruptions. To characterize the relationship between various levels of the concerned health network, a two-level multi-flow nested hierarchy with service referral is taken into account. The queuing system is considered in the model which handles uncertainty associated with demand as well as service. To ensure the service quality, a minimum limit reflected in the probability of patients' waiting time is defined. The proposed model is formulated based on a two-stage robust optimization approach in which decisions are defined in two stages such that a first-stage solution should be robust against the possible realizations of the disruption that can only be revealed in a second stage. A solution procedure based on Benders decomposition type algorithm is implemented to solve the model. To illustrate the applicability of the proposed model, a practical case study is presented. The results show that the proposed model can be applied as a flexible decision support tool for decision makers to adopt appropriate strategies and policies in designing a reliable and robust health service network. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Hierarchical location-allocation model Health service network design Disruption Queuing theory two-stage robust optimization Benders decomposition algorithm

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The recoverable robust facility location problem

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2015年 79卷 93-120页

作者： Alvarez-Miranda, Eduardo Fernandez, Elena Ljubic, Ivana Univ Talca Dept Ind Engn Curico Chile Univ Politecn Cataluna Dept Stat & Operat Res Barcelona Spain Univ Vienna Dept Stat & Operat Res Vienna Austria ESSEC Business Sch Cergy Pontoise France

This work deals with a facility location problem in which location and allocation (transportation) policy is defined in two stages such that a first-stage solution should be robust against the possible realizations (scenarios) of the input data that can only be revealed in a second stage. This solution should be robust enough so that it can be recovered promptly and at low cost in the second stage. In contrast to some related modeling approaches from the literature, this new recoverable robust model is more general in terms of the considered data uncertainty;it can address situations in which uncertainty may be present in any of the following four categories: provider-side uncertainty, receiver-side uncertainty, uncertainty in-between, and uncertainty with respect to the cost parameters. For this novel problem, a sophisticated branch-and-cut framework based on Benders decomposition is designed and complemented by several non-trivial enhancements, including scenario sorting, dual lifting, branching priorities, matheuristics and zero-half cuts. two large sets of instances that incorporate spatial and demographic information of countries such as Germany and US (transportation) and Bangladesh and the Philippines (disaster management) are introduced. They are used to analyze in detail the characteristics of the proposed model and the obtained solutions as well as the effectiveness, behavior and limitations of the designed algorithm. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Facility location two-stage robust optimization Branch-and-cut L-shaped cuts

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Resilient Expansion Planning of Electricity Grid Under Prolonged Wildfire Risk

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IEEE TRANSACTIONS ON SMART GRID 2023年第5期14卷 3719-3731页

作者： Bayani, Reza Manshadi, Saeed D. Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA San Diego State Univ Dept Elect & Comp Engn San Diego CA 92182 USA

Public safety power shut-off (PSPS) during potentially dangerous weather conditions is an emergency measure within areas prone to wildfires. While balancing the risk of wildfire ignition and the continuation of energy supply is a short-term operation challenge, this paper explores the optimal long-term resilient expansion planning strategies. With the quantified risk of wildfire ignition, the proposed expansion planning problem maximizes the supplied power to the end-users while limiting the risk of wildfire ignition. The presented scheme provides utility decision-makers with three groups of network expansion decisions, namely addition of new lines, modification of existing lines, and installation of distributed energy resources (DERs). Given the uncertainty of DERs and wildfire risk, a two-stage robust optimization problem is proposed which ensures power system resilience against unfavorable events. The case studies illustrate how the presented model balances shutting off customers, DER installation, and line addition/modification while inhibiting wildfire ignition risk. Besides, the results suggest that, with appropriate transmission switching, DER installation can be the optimal choice for meeting the growing demand while limiting wildfire ignition risk. The implications of different risk level choices are illustrated, and the impacts of uncertainties on the expansion decisions are explored. Finally, the results of applying the proposed model to the IEEE 118-bus test network are illustrated.

关键词： Fires Planning Ignition Uncertainty Costs Resilience Power grids Wildfire expansion planning two-stage robust optimization resilience uncertainties natural disasters

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Modeling Transmission Line Constraints in two-stage robust Unit Commitment Problem

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IEEE TRANSACTIONS ON POWER SYSTEMS 2014年第3期29卷 1221-1231页

作者： Lee, Changhyeok Liu, Cong Mehrotra, Sanjay Shahidehpour, Mohammad Northwestern Univ Dept IEMS Evanston IL 60208 USA Argonne Natl Lab Decis & Informat Sci Div Argonne IL 60439 USA IIT Elect Power & Power Elect Ctr Elect & Comp Engn Dept Chicago IL 60616 USA

Integration of renewable energy sources and demand response poses new challenges to system operators as they increase the uncertainty of the power supply and demand. Recently, robust optimization techniques are applied to the unit commitment problem with uncertainty as an alternative to the stochastic programming approaches. However, it remains challenging to solve the robust unit commitment model with full transmission line constraints. In this paper, we propose novel acceleration techniques for solving two-stage robust unit commitment problem with consideration of full transmission line constraints. We use 1) the cutting-plane algorithm for the master problem, which dynamically includes critical transmission line constraints, and 2) column-generation methods, including the branch-and-price-and-cut algorithm and heuristic approaches, for the subproblems, which add only necessary transmission line dual variables on the fly. Computational results for the modified IEEE 118-bus system show that the combination of the cutting-plane algorithm and the heuristic column-generation approach greatly reduces the total solution time of the two-stage robust unit commitment problem.

关键词： Load-shift-factor two-stage robust optimization unit commitment wind power

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Incorporating Data-Driven Demand-Price Uncertainty Correlations Into Microgrid Optimal Dispatch

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IEEE TRANSACTIONS ON SMART GRID 2024年第3期15卷 2804-2818页

作者： Qiu, Haifeng Liu, Pengxiang Gu, Wei Zhang, Rufeng Lu, Shuai Gooi, Hoay Beng Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Northeast Elect Power Univ Dept Elect Engn Jilin 132012 Peoples R China

A growing number of microgrid (MG) systems ipate in market trading as prosumers to improve their own operation profits, as well as the flexibility of power supply. To handle the MG dispatch problem with uncertainties in both load demands and electricity prices, this paper provides a data-driven robust optimization (RO) approach. Firstly, the correlations between the uncertainties of demands and prices are verified via the analysis of historical monitoring data from a real-world case. Based on the data-fitting for the rectangular and ellipsoidal intervals, a new data-driven demand-price uncertainty set with correlations is built to eliminate these unreasonable scenarios effectively. Secondly, a two-stage RO dispatch model is established considering multiple uncertainties of renewable-load power and prices in MGs. The bilinear objective term brings huge obstacles to the solution of the RO model, therefore a novel nested iterative algorithm is further exploited. The bilinear terms of power and price in the objective function are linearized by a binary expansion approach. Besides, to avoid the enumerations of numerous binary recourses in problem solving, the block coordinate descent (BCD) theory is adopted to co-optimize different types of variables, thereby enhancing the computational tractability. Numerical simulations indicate the validity and superiority of the developed RO model and the solution algorithm.

关键词： Data-driven uncertainty set demand-price correlations iterative algorithm microgrid dispatch two-stage robust optimization

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Bi-Level two-stage robust Optimal Scheduling for AC/DC Hybrid Multi-Microgrids

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IEEE TRANSACTIONS ON SMART GRID 2018年第5期9卷 5455-5466页

作者： Qiu, Haifeng Zhao, Bo Gu, Wei Bo, Rui Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China State Grid Zhejiang Elect Power Res Inst Hangzhou 310014 Zhejiang Peoples R China Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA

In view of the plurality of ac and dc microgrids connected to the power grid, this paper proposes a bi-level two-stage robust optimal scheduling model for ac/dc hybrid multi-microgrids (HMMs). In this model, the ad/dc HMM system is divided into two interest bodies, called the utility level and supply level, and two-stage robust (also called adaptive robust) optimal scheduling is carried out considering the uncertainties in each level. For the mutual interaction between the two bodies, power constraints and deviation penalties of the interaction lines are introduced in the model to realize bi-level coordinated scheduling and obtain a unified robust dispatch scheme of ad/dc HMMs. The column-and-constraint generation algorithm is used to convert the Min-Max-Min problem of each level into a two-stage mixed-integer linear programming problem, which can be solved quickly and effectively. Case studies verify the rationality and validity of the proposed model and the solving procedure.

关键词： Bi-level scheduling multi-microgrids power interaction two-stage robust optimization uncertainty

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Designing and constructing networks under uncertainty in the construction stage: Definition and exact algorithmic approach

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COMPUTERS & OPERATIONS RESEARCH 2017年 81卷 178-191页

作者： Alvarez-Miranda, Eduardo Pereira, Jordi Univ Talca Dept Ind Engn Curico Chile Univ Adolfo Ibanez Dept Sci & Engn Vina Del Mar Chile

The present work proposes a novel Network optimization problem whose core is to combine both network design and network construction scheduling under uncertainty into a single two-stage robust optimization model. The first-stage decisions correspond to those of a classical network design problem, while the second-stage decisions correspond to those of a network construction scheduling problem (NCS) under uncertainty. The resulting problem, which we will refer to as the two-stage robust Network Design and Construction Problem (2SRNDC), aims at providing a modeling framework in which the design decision not only depends on the design costs (e.g., distances) but also on the corresponding construction plan (e.g., time to provide service to costumers). We provide motivations, mixed integer programming formulations, and an exact algorithm for the 2SRNDC. Experimental results on a large set of instances show the effectiveness of the model in providing robust solutions, and the capability of the proposed algorithm to provide good solutions in reasonable running times. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Network design Network construction two-stage robust optimization Exact algorithms

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Economies of scale in recoverable robust maintenance location routing for rolling stock

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2018年第PartA期117卷 360-377页

作者： Tonissen, D. D. Arts, J. J. Vrije Univ Amsterdam Sch Business & Econ Amsterdam Netherlands Univ Luxembourg Luxembourg Ctr Logist & Supply Chain Management Luxembourg Luxembourg Eindhoven Univ Technol Sch Ind Engn Eindhoven Netherlands

We consider the problem of locating maintenance facilities in a railway setting. Different facility sizes can be chosen for each candidate location and for each size there is an associated annual facility costs that can capture economies of scale in facility size. Because of the strategic nature of facility location, the opened facilities should be able to handle the current maintenance demand, but also the demand for any of the scenarios that can occur in the future. These scenarios capture changes such as changes to the line plan and the introduction of new rolling stock types. We allow recovery in the form of opening additional facilities, closing facilities, and increasing the facility size for each scenario. We provide a two-stage robust programming formulation. In the first-stage, we decide where to open what size of facility. In the second-stage, we solve a NP-hard maintenance location routing problem. We reformulate the problem as a mixed integer program that can be used to make an efficient column-and-constraint generation algorithm. To show that our algorithm works on practical sized instances, and to gain managerial insights, we perform a case study with instances from the Netherlands Railways. A counter intuitive insight is that economies of scale only play a limited role and that it is more important to reduce the transportation cost by building many small facilities, rather than a few large ones to profit from economies of scale. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Facility location Maintenance routing Rolling stock two-stage robust optimization Column-and-constraint generation

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On the Solution of Large-Scale robust Transmission Network Expansion Planning Under Uncertain Demand and Generation Capacity

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IEEE TRANSACTIONS ON POWER SYSTEMS 2018年第2期33卷 1242-1251页

作者： Minguez, Roberto Garcia-Bertrand, Raquel Arroyo, Jose M. Alguacil, Natalia Univ Castilla La Mancha Spin Off UCLM Hydraul Lab Hidralab Ingn & Desarrollo SL E-13071 Ciudad Real Spain Univ Castilla La Mancha Dept Ingn Elect Elect Automat & Comunicac ETSI Ind E-13071 Ciudad Real Spain

two-stage robust optimization has emerged as a relevant approach to deal with uncertain demand and generation capacity in the transmission network expansion planning problem. Unfortunately, the solution of practical large-scale instances remains a challenge. In order to address this issue, this paper presents an alternative column-and-constraint generation algorithm wherein the max-min problem associated with the second stage is solved by a block coordinate descent method. As a major salient feature, the proposed approach does not rely on the transformation of the second-stage problem to a single-level equivalent. As a consequence, bilinear terms involving dual variables or Lagrange multipliers do not arise, thereby precluding the use of computationally expensive big-M-based linearization schemes. Thus, not only is the computational effort reduced, but also the typically overlooked case-dependent, nontrivial, and time-consuming tuning of bounding parameters for dual variables or Lagrange multipliers is avoided. The practical applicability of the proposed methodology is confirmed by numerical testing on several benchmarks including a case based on the Polish 2383-bus system, which is well beyond the capability of the robust methods available in the literature.

关键词： Block coordinate descent method column-and-constraint generation algorithm transmission network expansion planning two-stage robust optimization uncertainty

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robust scheduling of building energy system under uncertainty

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APPLIED ENERGY 2016年 167卷 366-376页

作者： Wang, Chengshan Jiao, Bingqi Guo, Li Tian, Zhe Niu, Jide Li, Siwei Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China Tianjin Univ Sch Environm Sci & Engn Tianjin 300072 Peoples R China Beijing Guodiantong Network Technol Co Ltd Beijing Peoples R China

This paper proposes a robust scheduling strategy to manage a building energy system with solar power generation system, multi-chiller system and ice thermal energy storage under prediction uncertainty. The strategy employs a two-stage adjustable robust formulation to minimize the system operation cost, wherein a parameter is introduced to adjust the level of conservatism of the robust solution against the modeled uncertainty. Then a column and constraint generation algorithm with modified initialization strategy is adopted to solve this optimization model along with mixed-integer linear programming. Further, we evaluate the performance of the proposed strategy by hourly simulating the system operation of a practical project with Monte Carlo simulation. Numerical results show that the robust scheduling with a proper parameter can be superior to the deterministic strategy in all the studied cases. Additionally, the proposed strategy has similar results with the model-based predictive control strategy while the former only needs to be implemented once. Even in the highest load case, the relative deviation between the two strategies is less than 2%. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Building energy system Ice thermal energy storage two-stage robust optimization Uncertainty

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