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data-driven optimization for Commodity Procurement Under Price Uncertainty

M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2023年第2期25卷 371-390页

作者： Mandl, Christian Minner, Stefan Tech Univ Munich TUM Sch Management Logist & Supply Chain Management D-80333 Munich Germany

Problem definition: We study a practice-motivated multiperiod stochastic commodity procurement problem under price uncertainty with forward and spot purchase options. Existing approaches are based on parametric pricemodels, which inevitably involve price model misspecification and generalization error. Academic/practical relevance: We propose a nonparametric, data-driven approach (DDA) that is consistent with the optimal procurement policy structure but without requiring the a priori specification and estimation of stochastic price processes. In addition to historical prices, DDAis able to leverage real-time feature data, such as economic indicators, in solving the problem. Methodology: This paper provides a framework for prescriptive analytics in dynamic commodity procurement, with optimal purchase policies learned directly from data as functions of features, via mixed integer linear programming (MILP) under costminimization objectives. Hence, DDAfocuses on optimal decisions rather than optimal predictions. Furthermore, we combine optimization with regularization from machine learning (ML) to extract decision-relevant data fromnoise. Results: Based on numerical experiments and empirical data, we show that there is a significant value of feature data for commodity procurement when procurement policy parameters are learned as functions of features. However, overfitting deteriorates the performance of data-driven solutions, which asks for ML extensions to improve out-ofsample generalization. Compared with an internal best-practice benchmark, DDA generates savings of on average 9.1 million euros per annum (4.33%) for 10 years of backtesting. Managerial implications: A practical benefit of DDA is that it yields simple but optimally structured decision rules that are easy to interpret and easy to operationalize. Furthermore, DDA is generalizable and applicable to many other procurement settings.

关键词： commodity procurement data-driven optimization machine learning prescriptive analytics

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data-driven optimization Framework for Voltage Regulation in Distribution Systems

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IEEE TRANSACTIONS ON POWER DELIVERY 2022年第2期37卷 1344-1347页

作者： Hong, Tianqi Zhang, Yichen Argonne Natl Lab Energy Syst Div Lemont IL 60439 USA

This letter proposes a data-driven optimization framework for voltage regulation problems to address the challenge of model inaccuracy and parameter varying. To achieve online voltage optimization, the recursive kernel regression and interior point methods are integrated. The IEEE 123-Bus system and EPRI Ckt5 feeder are selected to validate the effectiveness of the proposed data-driven optimization framework. The proposed method is also compared with a linear function based method.

关键词： Voltage control optimization Kernel Mathematical models Load flow Voltage measurement Linear programming Recursive kernel regression data-driven optimization multiphase distribution system voltage control

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data-driven optimization: A Reproducing Kernel Hilbert Space Approach

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OPERATIONS RESEARCH 2022年第1期70卷 454-471页

作者： Bertsimas, Dimitris Kodur, Nihal MIT Sloan Sch Management 77 Massachusetts Ave Cambridge MA 02139 USA MIT Ctr Operat Res Cambridge MA 02139 USA

We present two methods, based on regression in reproducing kernel Hilbert spaces, for solving an optimization problem with uncertain parameters for which we have historical data, including auxiliary data. The first method approximates the objective function and the second approximates the optimizer. We provide finite sample guarantees and prove asymptotic optimality for both methods. Computational experiments suggest that at least the second method overcomes a curse of dimensionality that afflicts existing methods, extrapolates better to unseen data, and achieves a many-fold decrease in sample complexity even for small dimensions.

关键词： data-driven optimization prescriptive analytics reproducing kernel Hilbert space regression optimality

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data-driven optimization for Cooperative Edge Service Provisioning With Demand Uncertainty

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IEEE INTERNET OF THINGS JOURNAL 2021年第6期8卷 4317-4328页

作者： Li, Liang Shi, Dian Hou, Ronghui Li, Xuanheng Wang, Jie Li, Hui Pan, Miao Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing 100080 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77204 USA Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Peoples R China Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Peoples R China

Multiaccess edge computing (MEC) empowers service providers (SPs) to run applications on the shared edge platforms in close proximity to mobile users, enabling ultralow latency access to a wide variety of cloud services. However, how to decide the amount of edge computing resources to rent for mobile service provisioning poses great challenges as the service demand is unknown to SPs a priori and may vary across the geographically distributed edge sites spatially and temporally. The resource rental decision also significantly affects SPs' deploying profits since it is critical for service deployment and workload assignment. This article investigates the service provisioning problem in a cooperative edge computing system under service demand uncertainty. We develop a holistic solution to make two-timescale decisions on edge resource rental and workload assignment to maximize SP's deploying profits. Briefly, we exploit historical service demand traces at the edge sites to characterize the uncertainty in a data-driven manner and formulate the edge service provisioning problem into a two-stage risk-averse optimization. To solve the formulated problem without compromising the data privacy, we propose an algorithm integrating Benders decomposition (BD) and alternating direction method of multipliers (ADMMs), which enables each edge site to keep the historical traces locally and participate in the optimization process. Based on real-world data sets, extensive simulations are conducted to validate the efficacy of our scheme.

关键词： Uncertainty optimization Edge computing Servers Robustness Processor scheduling Internet of Things data-driven optimization demand uncertainty edge computing resource provisioning

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data-driven optimization for wind farm siting

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 155卷

作者： Arrieta-Prieto, Mario E. Schell, Kristen R. Univ Nacl Colombia Dept Stat Bogota 110131 Colombia Carleton Univ Dept Mech & Aerosp Engn Ottawa ON K1S 5B6 Canada

Both increasing power output and minimizing the variability of that output are key factors in a highly utilized wind farm. Finding a location that offers this type of wind resource requires the use of stochastic methods to explicitly model the uncertainty of wind power output. While the literature is rich with wind speed resource assessments, this work moves the field to wind farm power assessment at undeveloped locations. A wind farm power prediction is achieved with improved spatial process representation. The power prediction results are utilized directly in a wind farm location siting optimization model which endogenously incorporates uncertai n t y in wind power output. The overa l l framework for siting new wind farms, termed the Farm Finder tool, is demonstrated in a case study in the Electric Reliability Counci l of Texas (ERCOT). It is shown that the formu-lation of this model w i l l find an optimal solution to the siting problem on the Pareto front of high power output, with high reliability. Such location decisions improve the overa l l social welfare in the electricity market.

关键词： v-logit random forest Random-effects model data-driven optimization Genetic algorithm Wind power prediction

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data-driven optimization for seismic-resilient power network planning *

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COMPUTERS & OPERATIONS RESEARCH 2024年 166卷

作者： Oneto, Alfredo Lorca, Alvaro Ferrario, Elisa Poulos, Alan De La Llera, Juan Carlos Negrete-Pincetic, Matias Swiss Fed Inst Technol Reliabil & Risk Engn Inst Energy & Proc Engn Zurich Switzerland Pontificia Univ Catolica Chile Dept Ind & Syst Engn Santiago Chile Pontificia Univ Catolica Dept Elect Engn Santiago Chile Pontificia Univ Catolica Chile UC Energy Res Ctr Santiago Chile CIGIDEN Natl Res Ctr Integrated Nat Disaster & Management Macul Chile Pontificia Univ Catolica Chile Dept Struct & Geotech Engn Santiago Chile Stanford Univ Dept Civil & Environm Engn Stanford CA USA

Many regions of the planet are exposed to seismic hazards that can cause devastating consequences on power systems. Due to these systems' crucial role, the evaluation and planning for their safe and reliable operation are paramount. This paper develops a novel data -driven optimization framework to assess the power network's seismic resilience and plan cost-effective investments for its enhancement. Under a robust optimization scheme, an earthquake attacker-defender model finds the worst -case realization of random earthquake network contingencies within an uncertainty set defined with a large number of scenarios generated by state-of-the-art engineering methods. Moreover, data -driven stochastic -robust optimization is employed in a two -stage seismic -resilient power network planning model, leveraging multiple seismic sources' distributional information. Transmission line expansions and siting and sizing of battery energy storage systems are decided in the first stage, while the second stage decides operational variables. Experiments on a 281 -node Chilean power system provide insights for seismic -resilient planning and demonstrate the efficiency of the proposed approach.

关键词： OR in energy data-driven optimization Robust optimization Power systems resilience Seismic hazards

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data-driven optimization of Reward-Risk Ratio Measures

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INFORMS JOURNAL ON COMPUTING 2021年第3期33卷 1120-1137页

作者： Ji, Ran Lejeune, Miguel A. George Mason Univ Dept Syst Engn & Operat Res Fairfax VA 22030 USA George Washington Univ Dept Decis Sci Washington DC 20052 USA

We investigate a class of fractional distributionally robust optimization problems with uncertain probabilities. They consist in the maximization of ambiguous fractional functions representing reward-risk ratios and have a semi-infinite programming epigraphic formulation. We derive a new fully parameterized closed-form to compute a new bound on the size of the Wasserstein ambiguity ball. We design a data-driven reformulation and solution framework. The reformulation phase involves the derivation of the support function of the ambiguity set and the concave conjugate of the ratio function. We design modular bisection algorithms which enjoy the finite convergence property. This class of problems has wide applicability in finance, and we specify new ambiguous portfolio optimization models for the Sharpe and Omega ratios. The computational study shows the applicability and scalability of the framework to solve quickly large, industry-relevant-size problems, which cannot be solved in one day with state-of-the-art mixed-integer nonlinear programming (MINLP) solvers.

关键词： data-driven optimization distributionally robust optimization reward-risk ratio Wasserstein metric fractional programming

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data-driven optimization of coastal sea level monitoring: Leveraging known patterns for enhanced reconstruction

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REGIONAL STUDIES IN MARINE SCIENCE 2024年 80卷

作者： Kartal, Elif Altunkaynak, Abdusselam Celik, Anil Istanbul Tech Univ Fac Civil Engn Hydraul & Water Resource Engn Div TR-34469 Istanbul Turkiye

Efficiently configuring sea level monitoring stations is crucial for obtaining accurate spatiotemporal data while managing operational and maintenance costs and addressing the challenges posed by missing data. This study focuses on optimizing the selection of stations within Turkey's coastal sea level monitoring network by leveraging the inherent lower dimensionality in data. The network consists of 18 stations distributed along Turkey's coastline. To identify dominant patterns in historical sea level data, Empirical Orthogonal Function (EOF) analysis was employed, followed by the application of the QR decomposition with column pivoting algorithm. Model performance is assessed using the Nash-Sutcliffe coefficient of efficiency (CE) and root mean square error (RMSE). Remarkably, the results demonstrated that reconstructing the entire dataset, encompassing all 18 stations was possible with a CE of 0.94 and an RMSE of 0.06. Even utilizing data from two or three stations alone achieves acceptable reconstruction accuracy. The effectiveness of EOF analysis combined with QR with column pivoting algorithm suggests promising applications in various scientific fields.

关键词： Sea level surface Station network optimization Empirical orthogonal function QR with column pivoting data-driven optimization

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data-driven optimization for Dallas Fort Worth International Airport deicing activities

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ANNALS OF OPERATIONS RESEARCH 2018年第1-2期263卷 361-384页

作者： Fan, Huiyuan Tarun, Prashant K. Chen, Victoria C. P. Shih, Dachuan T. Rosenberger, Jay M. Kim, Seoung Bum Horton, Robert A. Johnson Controls 631 South Richland Ave York PA 17403 USA Missouri Western State Univ Steven L Craig Sch Business St Joseph MO 64507 USA Univ Texas Arlington Dept Ind Mfg & Syst Engn Arlington TX 76019 USA Tenet PFS 13737 Noel RdSuite 100 Dallas TX 75240 USA Korea Univ Dept Ind Management Engn Seoul 02841 South Korea Dallas Ft Worth Int Airport 3200 East Airfield Dr Dfw Airport TX 75261 USA

Airplane deicing is a safety measure to eliminate/prevent icing on airplanes that can lead to airflow disruption and emergency conditions. Aircraft deicing/anti-icing fluids (ADF) are high in glycol content. At Dallas Fort Worth International Airport (DFW), the major aircraft deicing activities are conducted at designated deicing pads called Source Isolation Deicing Sites, where ADF run-off can be captured and conveyed into the airport's glycol collection system. A portion of ADF drips from the aircraft during taxiing and shears off the aircraft during take-off, entering nearby waterways without treatment. Glycol acts as a nutrient for bacteria in the airport's receiving waterways, resulting in an increase in bacterial growth and a subsequent reduction in dissolved oxygen (DO), potentially endangering aquatic life. This paper proposes a prototype data-driven deicing activities management framework for DFW to address the complexity of airport deicing operations and its impacts. The proposed framework uses stochastic dynamic programming (SDP) to assign airplanes in each hour to deicing pad locations, so as to maximize DO in the receiving waters, subject to airport constraints. Some data were artificially generated using the available knowledge of airport operations. The state transition equations in SDP were estimated. The proposed framework was demonstrated using three cases during major deicing events. Improvements in DO compared with actual DO recorded in the data were mixed;however, the results motivated DFW to implement a new data collection process to replace the artificially-generated data, so that a more accurate optimization could be conducted in the future.

关键词： data-driven optimization Stochastic dynamic programming Airport deicing Water quality Glycol Dissolved oxygen

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data-driven optimization for last-mile delivery

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COMPLEX & INTELLIGENT SYSTEMS 2023年第3期9卷 2271-2284页

作者： Chu, Hongrui Zhang, Wensi Bai, Pengfei Chen, Yahong Capital Univ Econ & Business Sch Management & Engn Beijing 100070 Peoples R China Ocean Univ China Sch Econ Qingdao 266100 Shandong Peoples R China Beijing Wuzi Univ Beijing Intelligent Logist Syst Collaborat Innova Beijing 101149 Peoples R China

This paper considers how an online food delivery platform can improve last-mile delivery services' performance using multi-source data. The delivery time is one critical but uncertain factor for online platforms that also regarded as the main challenges in order assignment and routing service. To tackle this challenge, we propose a data-driven optimization approach that combines machine learning techniques with capacitated vehicle routing optimization. Machine learning methods can provide more accurate predictions and have received increasing attention in the operations research field. However, different from the traditional predict-then-optimize paradigm, we use a new smart predict-then-optimize framework, whose prediction objective is constructed by decision error instead of prediction error when implementing machine learning. Using this type of prediction, we can obtain a more accurate decision in the following optimization step. Efficient mini-batching gradient and heuristic algorithms are designed to solve the joint order assignment and routing problem of last-mile delivery service. Besides, this paper considers the mutual effect between routing decision and delivery time, and provides the corresponding solution algorithm. In addition, this paper conducts a computational study and finds that the proposed method's performance has an approximate 5% improvement compared with other methods.

关键词： data-driven optimization Last-mile delivery Machine learning Prediction Routing behavior

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