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Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第6期57卷 135-135页

作者： Aydin, Nezir Murat, Alper Mordukhovich, Boris S. Yildiz Tech Univ Dept Ind Engn TR-34349 Besiktas Istanbul Turkiye Hamad Bin Khalifa Univ Coll Sci & Engn Doha Qatar Wayne State Univ Dept Ind & Syst Engn Detroit MI 48202 USA Wayne State Univ Dept Math Detroit MI 48202 USA

Selecting facility locations requires significant investment to anticipate and prepare for disruptive events like earthquakes, floods, or labor strikes. In practice, location choices account for facility capacities, which often cannot change during disruptions. When a facility fails, demand transfers to others only if spare capacity exists. Thus, capacitated reliable facility location problems (CRFLP) under uncertainty are more complex than uncapacitated versions. To manage uncertainty and decide effectively, stochastic programming (SP) methods are often employed. Two commonly used SP methods are approximation methods, i.e., Sample Average Approximation (SAA), and decomposition methods, i.e., Progressive Hedging Algorithm (PHA). SAA needs large sample sizes for performance guarantee and turn into computationally intractable. On the other hand, PHA, as an exact method for convex problems, suffers from the need to iteratively solve numerous sub-problems which are computationally costly. In this paper, we developed two novel algorithms integrating SAA and PHA for solving the CRFLP under uncertainty. The developed methods are innovative in that they blend the complementary aspects of PHA and SAA in terms of exactness and computational efficiency, respectively. Further, the developed methods are practical in that they allow the specialist to adjust the tradeoff between the exactness and speed of attaining a solution. We present the effectiveness of the developed integrated approaches, Sampling Based Progressive Hedging Algorithm (SBPHA) and Discarding SBPHA (d-SBPHA), over the pure strategies (i.e. SAA). The validation of the methods is demonstrated through two-stage stochastic CRFLP. Promising results are attained for CRFLP, and the method has great potential to be generalized for SP problems.

关键词： stochastic programming Capacitated reliable facility location Hybrid algorithm Progressive hedging Sample average approximation

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Adaptive robust optimization framework for market-based wind power investment

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2024年 40卷

作者： Mahmoud, Haitham A. King Saud Univ Coll Engn Ind Engn Dept POB 800 Riyadh 11421 Saudi Arabia

Wind is distinguished by its eco-friendliness and sustainability, making it one of the most rapidly expanding forms of renewable energy sources (RESs). Hence, it is necessary to determine the most profitable plan for wind farm installation. This paper constructs a novel scheme for market-based wind power investment (WPI) problems using adaptive robust optimization (ARO). A tri-level robust WPI (RWPI) model is established, the first level of which is to minimize the investment cost plus the worst-case loss. In the second level, the worst-case loss (also known as the maximum regret) is identified by maximizing the minimum value of minus profit over the uncertainty sets. The third level maximizes the wind farm profit. Since the profit calculation requires the determination of the locational marginal price (LMP), the third level constitutes bi-level programming, with the upper level being the profit maximization and the lower level being the market clearing process. First, Karush-KuhnTucker (KKT) conditions are applied to convert the bi-level model to a single-level model, resulting in an ARO with binary variables at the third level. Afterward, the nested column-and-constraint generation (NCCG) strategy is employed to solve the ARO with mixed-integer recourse. A case study is used to verify the scalability and practical applicability of the proposed model.

关键词： Nested column-and-constraint generation Robust optimization stochastic programming Uncertainty Wind power investment

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Optimal Operation of Fast Charging Station Aggregator in Uncertain Electricity Markets Considering Onsite Renewable Energy and Bounded EV User Rationality

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第11期20卷 13384-13395页

作者： Chen, Yuanyi Zheng, Yanchong Hu, Simon Xie, Shiwei Yang, Qiang Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ ZJU UIUC Inst Haining 314400 Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310027 Peoples R China Alibaba Zhejiang Univ Joint Res Inst Frontier Technol Hangzhou 310058 Peoples R China Fuzhou Univ Sch Elect Engn & Automat Fuzhou 350108 Peoples R China

The increasing proliferation of electric vehicles (EVs) and renewable energy sources (RESs) poses challenges to the operation of coupled power and transportation networks due to their uncertainties, where EV users' routing and charging behaviors are subjected to their complex decision-making rationality. To economically manage numerous fast charging stations with onsite RESs, this article focuses on the optimal day-ahead bidding and intraday scheduling strategies of a fast charging station aggregator (FCSA) to maximize its profit in the electricity market. Traffic simulation based on boundedly rational dynamic user equilibrium is presented to model charging demand under bounded rationality of EV users. To efficiently manipulate large-scale EVs, a group charging scheduling framework is proposed to reduce decision variables. Uncertainties in electricity prices, RES generation, traffic demand, and user rationality are addressed by stochastic programming. Case studies have validated the effectiveness of the proposed method in reducing the FCSA's operational costs and RES curtailment.

关键词： Bounded rationality charging dispatch dynamic user equilibrium electricity market fast charging station aggregator (FCSA) stochastic programming

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Joint Planning of Utility-Owned Distributed Energy Resources in an Unbalanced Active Distribution Network Considering Asset Health Degradation

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IEEE TRANSACTIONS ON SMART GRID 2024年第4期15卷 3768-3781页

作者： Leng, Ruoxuan Li, Zhengmao Xu, Yan Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Aalto Univ Espoo 02150 Finland

Rapid integration of distributed energy resources (DERs) in active distribution networks (ADNs) necessitates advanced planning methods to optimally determine the size, site, and installation time of DERs. However, existing approaches often assume balanced networks and neglect health degradation of DER assets, limiting the accuracy and practicality of the planning results. This paper proposes a new planning method for utility-owned distributed generators (DGs) and energy storage systems (ESSs) in an unbalanced ADN considering asset health degradation. First, the three-phase branch flow is modeled for unbalanced characteristics of ADNs, and host DERs separately in different phases. Then, based on the Wiener degradation process, the aging path of each DG unit is modeled to estimate its available capacity along with service time;the ESS aging is modeled to reflect the degradation cost during charging and discharging. Finally, a copula-based stochastic programming method is presented considering the correlations between renewables and power demands. The inclusion of market volatility in electricity price uncertainty further enhances planning realism. Numerical case studies on an IEEE-34 bus three-phase ADN demonstrate the effectiveness and advantages of the proposed method.

关键词： Degradation Planning Costs Aging Uncertainty Load flow Computational modeling Unbalanced active distribution network (ADN) stochastic programming correlated uncertainties degradation

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A target-time-windows technique for project scheduling under uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第2期314卷 792-806页

作者： Lamas, Patricio Goycoolea, Marcos Pagnoncelli, Bernardo Newman, Alexandra Alicanto Labs Santiago Chile Pontificia Univ Catolica Chile Escuela Adm Santiago Chile Univ Cote Azur SKEMA Business Sch Lille France Colorado Sch Mines Dept Mech Engn Golden CO 80401 USA

We address the problem of determining the start times of activities in order to maximize the expected net present value of a project given precedence constraints. We assume that each activity has a random duration and profit with a known probability distribution. Most approaches generate either: a baseline schedule that is robust to uncertainty (using proactive approaches), or a policy that reacts to the revelation of uncertainty (using reactive approaches). We propose an integrated proactive-reactive technique that generates both a baseline time window for each activity's start time, and a policy that indicates how the schedule should be adapted for each realization of uncertainty. The time window explicitly constrains the extent to which the realized start times vary. An important feature of our approach is that, once computed, it can easily be communicated and implemented in practice. Numerical experiments show that the objective value of the solutions generated by our technique can be within 4%, on average, of the optimal value obtained with perfect information, and up to 50% better when compared to an earliest-start policy. Moreover, the variability of activities' start times can be 10 times smaller when compared to those generated by other policies. We solve an instance with 300 scenarios and 357 activities in 30 min, illustrating the scalability of our technique on a real-world problem that produces out-of-sample feasible solutions with a desired probability.

关键词： OR in natural resources Project scheduling Net present value stochastic programming

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A RANDOMIZED PROGRESSIVE HEDGING ALGORITHM FOR stochastic VARIATIONAL INEQUALITY

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2024年第3期20卷 1142-1155页

作者： Mu, Zhenguo Nanjing Univ Posts & Telecommun Coll Sci Nanjing Peoples R China

Recently, stochastic variational inequality (SVI) has been extended from single stage to multistage by Rockafellar and Wets (Math. Program., 165:331-360, 2017) and progressive hedging algorithm (PHA) has also been extended from stochastic programming to multistage stochastic linear comple-mentarity and SVI by Rockafellar and Sun (Math. Program., 174:453-471, 2019). However, the per-iteration cost of PHA can be prohibitively high when the scenario set is large, despite the decomposition and parallelizable nature of the algorithm. To address this issue, we propose a randomized PHA that allows us to control the per-iteration cost by randomly selecting a small subset of scenarios and updating only the corresponding variable components while freezing the variables corresponding to the unselected scenarios at the current iteration. By measuring the quality of an approximate solution using a re-stricted merit function, we demonstrate that despite the significant reduction in per-iteration cost, the randomized PHA converges in expectation and in an ergodic sense at the same sublinear rate as the original PHA.

关键词： stochastic variational inequality progressive hedging algorithm nonanticipativity stochastic programming

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Discrete Approximation and Convergence Analysis for a Class of Decision-Dependent Two-Stage stochastic Linear Programs

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Journal of the Operations Research Society of China 2024年第3期12卷 649-679页

作者： Jie Jiang Zhi-Ping Chen College of Mathematics and Statistics Chongqing UniversityChongqing401331China School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’an710049ShaanxiChina Center for Optimization Technique and Quantitative Finance Xi’an International Academy for Mathematics and Mathematical TechnologyXi’an710049ShaanxiChina

Customary stochastic programming with recourse assumes that the probability distribution of random parameters is independent of decision *** studies demonstrated that stochastic programming models with endogenous uncertainty can better reflect many real-world activities and applications accompanying with decision-dependent *** this paper,we concentrate on a class of decision-dependent two-stage stochastic programs(DTSPs)and investigate their discrete *** develop the discrete approximation methods for DTSPs,we first derive the quantitative stability results for *** on the stability conclusion,we examine two discretization schemes when the support set of random variables is bounded,and give the rates of convergence for the optimal value and optimal solution set of the discrete approximation problem to those of the original *** we extend the proposed approaches to the general situation with an unbounded support set by using the truncating *** an illustration of our discretization schemes,we reformulate the discretization problems under specific structures of the decision-dependent ***,an application and numerical results are presented to demonstrate our theoretical results.

关键词： stochastic programming Decision-dependence Discrete approximation Stability Convergence rate

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Assessing uncertain technological progress in the decarbonization pathway of China's hydrogen energy system

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ENERGY ECONOMICS 2025年 141卷

作者： Zhou, Wenji Ren, Hongtao Zhang, Xiao-Bing Shao, Shuai Renmin Univ China Sch Appl Econ Beijing 100872 Peoples R China East China Univ Sci & Technol Sch Business Shanghai 200237 Peoples R China Int Inst Appl Syst Anal Schlosspl 1 A-2361 Laxenberg Austria Tech Univ Denmark DK-2800 Copenhagen Denmark

Hydrogen energy is regarded as a promising solution for decarbonizing hard-to-abate sectors, while its role in the energy transition remains debatable. One of the key reasons is that uncertainty in technological progress has significant impacts on investment decision-making. To assess these effects, this study employs the MESSAGEix framework to develop a hydrogen energy system optimization model in China's context and integrates it with a stochastic scenario-tree generation method to assess the effects of uncertain technological progress on decarbonizing China's hydrogen energy system. The modeled system covers a full range of hydrogen production and consumption associated with different technical options for decarbonization, i.e., renewable energy-based water electrolysis (green hydrogen) and fossil-derived hydrogen coupled with carbon capture and storage. The model simulates a wide range of stochastic crucial cost metrics under the carbon-neutral constraint and compares it to a baseline without an emission constraint. Results show that disruptive technological breakthroughs in renewable electricity generation are essential to decarbonizing the hydrogen production system. The proposed hybrid modeling approach proves that computing is effective and could be applied to many other stochastic programming problems in long-term energy system planning.

关键词： Hydrogen energy Energy system model stochastic programming Technological progress Carbon neutrality

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Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing

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OPERATIONS RESEARCH 2024年第6期72卷 2375-2390页

作者： Bansak, Kirk Paulson, Elisabeth Univ Calif Berkeley Dept Polit Sci Berkeley CA 94720 USA Harvard Sch Business Technol & Operat Management Unit Boston MA 02163 USA

This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multiyear randomized control trial in Switzerland, seeks to maximize the average predicted employment level (or any measured outcome of interest) of refugees through a minimum-discord online assignment algorithm. The performance of this algorithm is tested on real refugee resettlement data from both the United States and Switzerland, where we find that it is able to achieve near-optimal expected employment, compared with the hindsight-optimal solution, and is able to improve upon the status quo procedure by 40%-50%. However, pure outcome maximization can result in a periodically imbalanced allocation to the localities over time, leading to implementation difficulties and an undesirable workflow for resettlement resources and agents. To address these problems, the second algorithm balances the goal of improving refugee outcomes with the desire for an even allocation over time. We find that this algorithm can achieve near-perfect balance over time with only a small loss in expected employment compared with the employment-maximizing algorithm. In addition, the allocation balancing algorithm offers a number of ancillary benefits compared with pure outcome maximization, including robustness to unknown arrival flows and greater exploration.

关键词： dynamic assignment algorithms stochastic programming load balancing refugee matching machine learning

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An effective hybrid goal programming approach for multi-objective straight assembly line balancing problem with stochastic parameters

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OPERATIONAL RESEARCH 2020年第4期20卷 1939-1976页

作者： Mardani-Fard, Heydar Ali Hadi-Vencheh, Abdollah Mahmoodirad, Ali Niroomand, Sadegh Univ Yasuj Dept Math Yasuj Iran Islamic Azad Univ Khorasgan Isfahan Branch Dept Math Esfahan Iran Islamic Azad Univ Masjed Soleiman Branch Dept Math Masjed Soleiman Iran Firouzabad Inst Higher Educ Dept Ind Engn Firouzabad Fars Iran

A new multi-objective straight assembly line balancing problem is focused in this study. The problem happens in a stochastic environment where the task times and the task performing quality levels are distributed normally. The objectives like equipment purchasing cost, worker time dependent wage, and average task performing quality of the assembly line are to be optimized simultaneously. A mixed integer non-linear formulation is proposed for the problem. Applying a chance-constrained modeling approach and some linearization techniques the model is converted to a crisp multi-objective mixed integer linear formulation. To tackle such problem, a hybrid fuzzy programming approach is proposed and combined with a typical goal programming method to construct a new hybrid goal programming approach. The computational experiments of the study results in a superior performance of the proposed approach comparing to the literature.

关键词： Assembly line balancing stochastic programming Goal programming Fuzzy programming Multi-objective optimization

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