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Integrated investment, retrofit and abandonment energy system planning with multi-timescale uncertainty using stabilised adaptive Benders decomposition

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European Journal of Operational Research 2025年第2期325卷 261-280页

作者： Zhang, Hongyu Grossmann, Ignacio E. McKinnon, Ken Knudsen, Brage Rugstad Nava, Rodrigo Garcia Tomasgard, Asgeir Department of Industrial Economics and Technology Management Norwegian University of Science and Technology Høgskoleringen 1 Trondheim7491 Norway School of Mathematical Sciences University of Southampton Building 54 Highfield Campus SouthamptonSO14 3ZH United Kingdom Department of Chemical Engineering Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States School of Mathematics The University of Edinburgh James Clerk Maxwell Building Peter Guthrie Tait Road EdinburghEH9 3FD United Kingdom SINTEF Energy Research Kolbjørn Hejes vei 1B Trondheim7491 Norway

We propose the REORIENT (REnewable resOuRce Investment for the ENergy Transition) model for energy systems planning with the following novelties: (1) integrating capacity expansion, retrofit and abandonment planning, and (2) using multi-horizon stochastic mixed-integer linear programming with multi-timescale uncertainty. We apply the model to the European energy system considering: (a) investment in new hydrogen infrastructures, (b) capacity expansion of the European power system, (c) retrofitting oil and gas infrastructures in the North Sea region for hydrogen production and distribution, and abandoning existing infrastructures, and (d) long-term uncertainty in oil and gas prices and short-term uncertainty in time series parameters. We utilise the structure of multi-horizon stochastic programming and propose a stabilised adaptive Benders decomposition to solve the model efficiently. We first conduct a sensitivity analysis on retrofitting costs of oil and gas infrastructures. We then compare the REORIENT model with a conventional investment planning model regarding costs and investment decisions. Finally, the computational performance of the algorithm is presented. The results show that: (1) when the retrofitting cost is below 20% of the cost of building new ones, retrofitting is economical for most of the existing pipelines, (2) platform clusters keep producing oil due to the massive profit, and the clusters are abandoned in the last investment stage, (3) compared with a traditional investment planning model, the REORIENT model yields 24% lower investment cost in the North Sea region, and (4) the enhanced Benders algorithm is up to 6.8 times faster than the level method stabilised adaptive Benders. © 2025 The Authors

关键词： stochastic programming

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Designing a centralized distribution system for omni-channel retailing

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PRODUCTION AND OPERATIONS MANAGEMENT 2023年第6期32卷 1724-1742页

作者： Guo, Jia Keskin, Burcu B. Calif State Univ East Bay Dept Management Hayward CA USA Univ Alabama Dept Informat Syst Stat & Management Sci Tuscaloosa AL USA Univ Alabama Dept Informat Syst Stat & Management Sci Tuscaloosa AL 35487 USA

We study the optimal supply chain design for a dual-channel retailer that operates physical and web-based stores and has traditionally managed each one separately. With increased pressures from customers and decreased profits due to marked-down leftover inventories in each channel, the retailer considers integrating the supply chain operations of both channels and using in-store inventory more effectively across different channels. In contrast to the existing literature, we focus on the firm's choice of the best omni-channel strategies considering demand segmentation, cost structure, and, more importantly, the inventory rationing ability of the firm. We formulate our problem as a two-stage stochastic programming model and use first-order optimality conditions to study the optimal inventory ordering decisions. Based on different omni-channel strategy decisions, we explore four supply chain design options. We identify the optimal inventory ordering policy under each option and explicitly describe the optimality conditions on cost and demand under perfect and imperfect demand information. When the demand information is imperfect, the demand fulfillment decisions can be characterized as nested protection-level policies. First, we show that omni-channel strategies are not necessarily profitable under all settings as dictated by market conditions. Second, we demonstrate that when the company can allocate its inventory perfectly, running omni-channel strategies may help better serve web-based customers and in-store customers. Finally, we show that, in the worst case, the imperfect demand information may result in losing all the claimed inventory integration benefits when running omni-channel strategies.

关键词： cross-channel fulfillment e-commerce fulfillment inventory rationing omni-channel retailing stochastic programming

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The Benders by batch algorithm: Design and stabilization of an enhanced algorithm to solve multicut Benders reformulation of two-stage stochastic programs

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期309卷 202-216页

作者： Blanchot, Xavier Clautiaux, Francois Detienne, Boris Froger, Aurelien Ruiz, Manuel Univ Bordeaux CNRS INRIA Bordeaux INPIMBUMR 5251 F-33400 Talence France RTE Paris France

This paper introduces a new exact algorithm to solve two-stage stochastic linear programs. Based on the multicut Benders reformulation of such problems, with one subproblem for each scenario, this method relies on a partition of the subproblems into batches. The key idea is to solve at most iterations only a small proportion of the subproblems by detecting as soon as possible that a first-stage candidate solution cannot be proven optimal. We also propose a general framework to stabilize our algorithm, and show its finite convergence and exact behavior. We report an extensive computational study on large-scale instances of stochastic optimization literature that shows the efficiency of the proposed algorithm compared to nine alternative algorithms from the literature. We also obtain significant additional computational time savings using the primal stabilization schemes. (C) 2023 Elsevier B.V. All rights reserved.

关键词： Large-scale optimization Benders decomposition stochastic programming Cut aggregation

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stochastic infection risk models for aircraft seat assignment considering passenger vaccination status and seat location

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JOURNAL OF AIR TRANSPORT MANAGEMENT 2025年 122卷

作者： Tang, Ching-Hui Hsu, Yi-Hsiang Natl Taiwan Ocean Univ Dept Transportat Sci Keelung 20224 Taiwan

We study optimal aircraft seat assignment for infectious diseases in view of the stochastic risk of infection for a passenger assigned to a seat. The stochastic risk is based on the passengers' vaccination status and the different risk probability distributions corresponding to seat locations at window, middle, or aisle. In addition, the influence of groups of passengers who prefer to be seated together on the risk of infection in the cabin is also analyzed. A stochastic programming technique is applied to develop both non-grouped and grouped scenariobased models. The objective is to minimize the risk of infection for the worst-case scenario, as formulated by the Min-Max objective approach. Numerical tests utilizing statistical data from 2369 flights in Taiwan were performed. The results show that the consideration of passengers' vaccination status during seat assignment is useful, reducing the average risk of infection in the cabin by half. Grouped seat assignment does not seem to have a significant influence on the risk of infection, with an increase of only 1.28 and 1.25 times compared with nongrouped seat assignment. The recommendations are that more heavily vaccinated passengers be assigned to aisle seats, while passengers who have received fewer doses be assigned to window seats. In addition, considering the limited impact of group seating on the risk of infection, it may not be necessary for an airline to decline to accommodate such requests.

关键词： Aircraft seat assignment Infectious disease stochastic risks Seat location Vaccination stochastic programming

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A stochastic policy algorithm for seasonal hydropower planning

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2023年 1-24页

作者： Grini, Hakon S. Danielsen, Anders S. Fleten, Stein-Erik Kleiven, Andreas Norwegian Univ Sci & Technol N-7491 Trondheim Norway

Hydropower producers need to plan several months or years ahead to estimate the opportunity value of water stored in their reservoirs. The resulting large-scale optimization problem is computationally intensive, and model simplifications are often needed to allow for efficient solving. Alternatively, one can look for near-optimal policies using heuristics that can tackle non-convexities in the production function and a wide range of modelling approaches for the price- and inflow dynamics. We undertake an extensive numerical comparison between the state-of-the-art algorithm stochastic dual dynamic programming (SDDP) and rolling forecast-based algorithms, including a novel algorithm that we develop in this paper. We name it Scenario-based Two-stage ReOptimization abbreviated as STRO. The numerical experiments are based on convex stochastic dynamic programs with discretized exogenous state space, which makes the SDDP algorithm applicable for comparisons. We demonstrate that our algorithm can handle inflow risk better than traditional forecast-based algorithms, by reducing the optimality gap from 2.5 to 1.3% compared to the SDDP bound.

关键词： stochastic programming stochastic policy Hydropower reservoir management

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Distributionally robust optimization with multivariate second-order stochastic dominance constraints with applications in portfolio optimization

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OPTIMIZATION 2023年第7期72卷 1839-1862页

作者： Wang, Shuang Pang, Liping Guo, Hua Zhang, Hongwei Dalian Univ Technol Sch Math Sci Dalian Peoples R China Key Lab Computat Math & Data Intelligence Liaonin Dalian Peoples R China

In this paper, we study the stochastic optimization problem with multivariate second-order stochastic dominance (MSSD) constraints where the distribution of uncertain parameters is unknown. Instead, only some historical data are available. Using the Wasserstein metric, we construct an ambiguity set and develop a data-driven distributionally robust optimization model with multivariate second-order stochastic dominance constraints (DROMSSD). By utilizing the linear scalarization function, we transform MSSD constraints into univariate constraints. We present a stability analysis focusing on the impact of the variation of the ambiguity set on the optimal value and optimal solutions. Moreover, we carry out quantitative stability analysis for the DROMSSD problems as the sample size increases. Specially, in the context of the portfolio, we propose a convex lower reformulation of the corresponding DROMSSD models under some mild conditions. Finally, some preliminary numerical test results are reported. We compare the DROMSSD model with the sample average approximation model through out-of-sample performance, certificate and reliability. We also use real stock data to verify the effectiveness of the DROSSM model.

关键词： stochastic programming distributionally robust optimization multivariate second-order stochastic dominance Wasserstein metric

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Distributionally Robust Optimization Using Cost-Aware Ambiguity Sets

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 1855-1860页

作者： Schuurmans, Mathijs Patrinos, Panagiotis Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS Leuven 3001 Belgium

We present a novel framework for distributionally robust optimization (DRO), called cost-aware DRO (Cadro). The key idea of Cadro is to exploit the cost structure in the design of the ambiguity set to reduce conservatism. Particularly, the set specifically constrains the worst-case distribution along the direction in which the expected cost of an approximate solution increases most rapidly. We prove that Cadro provides both a high-confidence upper bound and a consistent estimator of the out-of-sample expected cost, and show empirically that it produces solutions that are substantially less conservative than existing DRO methods, while providing the same guarantees.

关键词： Costs TV stochastic processes Machine learning Upper bound Training Standards stochastic programming constrained optimization

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Backtesting coordinated hydropower bidding using neural network forecasting

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2023年第3期14卷 847-867页

作者： Bringedal, Amanda Saebo Sovikhagen, Anne-Marthe Liaklev Aasgard, Ellen Krohn Fleten, Stein-Erik Norwegian Univ Sci & Technol Alfred Getz Vei 3 N-7034 Trondheim Norway SINTEF Energy Res Sem Saelands Vei 11 N-7034 Trondheim Norway

A stochastic programming model for a price-taking, profit-maximizing hydropower producer participating in the Nordic day-ahead and balancing market is developed and evaluated by backtesting over 200 historical days. We find that the producer may gain 0.07% by coordinating its trades in the day-ahead and balancing market, compared to considering the two markets sequentially. It is thus questionable whether a coordinated bidding strategy is worthwhile. However, the gain from coordinating trades is dependent on the quality of the forecasts for the balancing market. The limited gain of 0.07% comes from using an artificial neural network prediction model that is trained on historical data on seasonal effects, day-ahead market price, wind and temperature forecasts. To quantify the effect of the forecasting model on the gain of coordination, we therefore develop a benchmarking framework for two additional prediction models: a naive forecast predicting zero imbalance in expectation, and a perfect information forecast. Using the naive method, we estimate the lower bound of coordination to be 0.0% which coincides with theory. When having perfect information, we find that the upper bound for the gain is 3.8% which indicates that a substantial gain in profits can be obtained by coordinated bidding if accurate prediction methods could be developed.

关键词： Electricity markets Hydropower Neural network stochastic programming

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Practicable robust stochastic optimization under divergence measures with an application to equitable humanitarian response planning

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OR SPECTRUM 2023年第3期45卷 759-806页

作者： Caunhye, Aakil M. Alem, Douglas Univ Edinburgh Business Sch 29 Buccleuch Pl Edinburgh EH8 9JU Scotland

We seek to provide practicable approximations of the two-stage robust stochastic optimization model when its ambiguity set is constructed with an f-divergence radius. These models are known to be numerically challenging to various degrees, depending on the choice of the f-divergence function. The numerical challenges are even more pronounced under mixed-integer first-stage decisions. In this paper, we propose novel divergence functions that produce practicable robust counterparts, while maintaining versatility in modeling diverse ambiguity aversions. Our functions yield robust counterparts that have comparable numerical difficulties to their nominal problems. We also propose ways to use our divergences to mimic existing f-divergences without affecting the practicability. We implement our models in a realistic location-allocation model for humanitarian operations in Brazil. Our humanitarian model optimizes an effectiveness-equity trade-off, defined with a new utility function and a Gini mean difference coefficient. With the case study, we showcase (1) the significant improvement in practicability of the robust stochastic optimization counterparts with our proposed divergence functions compared to existing f-divergences, (2) the greater equity of humanitarian response that the objective function enforces and (3) the greater robustness to variations in probability estimations of the resulting plans when ambiguity is considered.

关键词： stochastic programming Ambiguity Robust stochastic optimization Moreau-Yosida regularization f-divergence Equitable humanitarian logistics

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Optimization of sustainable coal procurement for power plants under maritime transportation uncertainty☆

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CLEANER LOGISTICS AND SUPPLY CHAIN 2025年 14卷

作者： Liu, Qiang Wang, Xiao Liu, Huashi Meng, Lei Hao, Chenxu CHN ENERGY Huanghua Port Co Ltd Bohai New Area Shenhua Ave Cangzhou Hebei Peoples R China Hebei Bulk Port Technol Innovat Ctr Cangzhou 061113 Hebei Peoples R China

This study introduces a stochastic optimization model designed for enhancing coal procurement and logistics management at port-based coal-fired power plants. Addressing the dual pressures of economic efficiency and environmental sustainability, this model incorporates the complexities of maritime transportation uncertainties, varying demand, and stringent carbon emission controls. Through this framework, we effectively align procurement strategies with inventory management to optimize operations under dynamic conditions. The model's distinctiveness lies in its integration of real-time maritime variables and adaptive responses to environmental policies, setting a new standard for green logistics in the energy sector. Key results demonstrate the effectiveness of mixed coal procurement strategies across different demand scenarios, highlighting significant savings in carbon emissions and operational costs. Our findings provide valuable insights for policymakers and industry stakeholders aiming to achieve sustainable development goals in the energy sector.

关键词： Coal-fired power plants Coal procurement Green supply chain management Supply chain uncertainty stochastic programming

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