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A two-stage stochastic programming approach for generation and transmission maintenance scheduling with risk management

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2024年 1-23页

作者： Fan, Aoyu Huang, Zhouchun Zheng, Qipeng Luo, Xiaodong Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing Jiangsu Peoples R China Univ Cent Florida Dept Ind Engn & Management Syst Orlando FL USA Shenzhen Res Inst Big Data Shenzhen Peoples R China

In this article, we study the generation and transmission maintenance scheduling problem under uncertainty. We propose a two-stage optimization model with the first stage for weekly maintenance scheduling and the second stage for hourly economic power dispatch. To address the future uncertainties associated with renewable energy penetration and electricity demand, we formulate the problem as a two-stage stochastic mixed-integer programming model and incorporate Conditional Value at Risk (CVaR) to control the risk of having extreme loss of demand. To facilitate practical implementation, we apply the Benders decomposition algorithm tailored for parallel computing as the solution approach for the problem. The maintenance decisions, computational performance, and optimality of the model are evaluated by case studies on IEEE test instances. An extensive sensitivity analysis of CVaR related parameters is performed to illustrate their impact on decisions and risk management. The results show that the proposed risk-constrained model can provide effective annual maintenance plans for both generators and transmission lines on a weekly basis, and the Benders decomposition algorithm is able to solve large-scale problem instances efficiently.

关键词： Maintenance scheduling Renewable energy penetration stochastic programming Benders decomposition CVaR

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Adaptive Two-Stage stochastic programming with an Analysis on Capacity Expansion Planning Problem

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2024年第6期26卷 1981-2350, C2页

作者： Basciftci, Beste Ahmed, Shabbir Gebraeel, Nagi Univ Iowa Tippie Coll Business Dept Business Analyt Iowa City IA 52242 USA Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

Problem definition: : Multistage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that can be dynamically adjusted as uncertainty is realized. Often, for example, because of contractual constraints, such flexible policies are not desirable, and the decision maker may need to commit to a set of actions for a certain number of periods. Two-stage stochastic programming might be better suited to such settings, where first-stage decisions do not adapt to the uncertainty realized. In this paper, we propose a novel alternative approach, named as adaptive two-stage stochastic programming, where each component of the decision policy requiring limited flexibility has its own revision point, a period prior to which the decisions are determined at the beginning of the planning until this revision point, and after which they are revised for adjusting to the uncertainty realized thus far until the end of the planning. We then analyze this approach over the capacity expansion planning problem, that may require limited flexibility over expansion decisions. Methodology/results: : We provide a generic mixed-integer programming formulation for the adaptive two-stage stochastic programming problem with finite support, in particular, for scenario trees, and show that this problem is NP-hard in general. Next, we focus on the capacity expansion planning problem and derive bounds on the value of adaptive two-stage programming in comparison with the two-stage and multistage approaches in terms of revision points. We propose several heuristic solution algorithms based on this bound analysis. These algorithms either provide approximation guarantees or computational advantages in solving the resulting adaptive two-stage stochastic problem. Managerial implications: : We provide insights on the choice of the revision times based on our analytical analysis. We further present an extensive computational study on a generatio

关键词： stochastic programming mixed-integer programming capacity expansion planning approximation algorithm energy

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Biorefinery superstructure optimization under carbon pricing policies using stochastic programming

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APPLIED ENERGY 2024年 376卷

作者： Huynh, D. Ierapetritou, M. Univ Delaware Dept Chem & Biomol Engn 150 Acad St Newark DE 19716 USA

The growing utilization of biomass feedstocks for climate change mitigation has led to increased research on biorefineries. Concurrently, environmental policies are evolving as crucial tools to address these global concerns. Cap-and-trade, carbon tax, and carbon cap policies have been established to reduce carbon dioxide emissions. In this work, a mathematical optimization model is developed to integrate biorefinery process design with carbon pricing policies and crediting mechanisms. A two-stage stochastic mixed integer linear programming model is proposed to account for emissions, feedstock supply, chemical demand, and pricing uncertainties. A bi-objective optimization framework is employed to consider economic and environmental metrics. The framework is a valuable tool for governments and businesses to determine pareto-optimal investment strategies under environmental policies. It is applicable for evaluating prospective chemical technologies with carbon pricing policies beyond biorefineries. The results indicate that carbon crediting mechanisms can minimize the financial penalty by up to 50% under a carbon tax policy. Implementing chemical demand constraints within a cap-and-trade policy reduces potential profits, especially when the carbon prices are high. The stochastic programming approach revealed that underestimating the expected carbon cap leads to lower expected profits. Despite the financial implications of these policies, profitable process designs are achievable.

关键词： Cap and trade Carbon tax Carbon cap stochastic programming Process design Biomass

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Multi-stage scenario-based stochastic programming for managing lot sizing and workforce scheduling at Vestel

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ANNALS OF OPERATIONS RESEARCH 2025年第2-3期344卷 911-936页

作者： Seyfi, Seyed Amin Yanikoglu, Ihsan Yilmaz, Goerkem Ozyegin Univ Ind Engn Dept TR-34794 Istanbul Turkiye Izmir Univ Econ Ind Engn Dept TR-35330 Izmir Turkiye Vestel Home Appliances R&D TR-45030 Manisa Turkiye

This study proposes a multi-stage stochastic production planning approach for a joint lot sizing and workforce scheduling problem under demand uncertainty. Scenario trees are used to model uncertainty in demand, and a multi-stage scenario-based stochastic linear program is developed. This model allows for both here-and-now and wait-and-see decisions providing flexibility for decision-makers to adjust production quantities according to the realized portion of demand and improve the overall effectiveness of production planning by better managing the number of active lines, workforce, and inventory levels. A matheuristic is developed for large-sized instances, which yields near-optimal solutions in practicable computation times. The proposed methods are demonstrated over a real data set taken from a Turkish home and professional appliances company, Vestel. The results show significant improvements in cost and CPU time performances for benchmark approaches, verifying the effectiveness of the proposed method.

关键词： Production planning Workforce scheduling Multi-stage decision making stochastic programming

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Machine Learning-Enabled Evolutionary Two-Stage stochastic programming

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Pan, Jeng-Shyang Song, Pei-Cheng Chu, Shu-Chuan Snasel, Vaclav Watada, Junzo Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China VSB Tech Univ Ostrava Dept Comp Sci Ostrava Czech Republic Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Japan

Two-stage stochastic programming (2SP) is an effective framework for decision-making and modeling under uncertainty. Some 2SP problems are challenging due to their high dimensionality and nonlinearity. Machine learning can assist in solving 2SP problems by providing data-driven insights and approximations. Evolutionary algorithms are more general and effective methods for handling various 2SP problems by exploiting their structures and features. However, there is still a research gap in combining machine learning and evolutionary algorithms for solving 2SP problems. Therefore, this paper proposes for the first time a Machine Learning-enabled Evolutionary 2SP framework (MLE2SP), which uses machine learning to construct surrogate-assisted evolutionary optimization frameworks for 2SP. It constructs a novel multi-output 2SP surrogate model that considers scenarios and decision variables of both stages for the first time and proposes a data conversion method to handle the high-dimensional decision variables and scenarios. It also proposes a Machine Learning-enabled Differential Evolution Sampling (MLDES) method to update candidate solutions, which extracts knowledge from dominant candidate solutions to guide the evolutionary direction. Moreover, this work provides open sources of linear and nonlinear two-stage stochastic mixed-integer programming problem instances as benchmark test functions. The effectiveness and generality of the proposed algorithm and framework are verified by the test results on the benchmark test functions and a disaster relief logistics problem, which provide a new research direction for designing general and effective two-stage stochastic programming solving frameworks.

关键词： stochastic processes programming Machine learning Evolutionary computation Machine learning algorithms Linear programming Random variables Metaheuristics Heuristic algorithms Decision making stochastic programming machine learning evolutionary computation surrogate model uncertainty

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Designing a multi-objective closed-loop supply chain: a two-stage stochastic programming, method applied to the garment industry in Montreal, Canada

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2024年第3期26卷 6131-6162页

作者： Shafiee Roudbari, Erfan Fatemi Ghomi, S. M. T. Eicker, Ursula Concordia Univ Dept Bldg Civil & Environm Engn Montreal PQ Canada Amirkabir Univ Technol Dept Ind Engn Tehran *** Iran

The global population continues to grow, which expands demand for raw materials. Meanwhile, governments are developing circular economy strategies within cities and their industries. A circular economy utilizes refurbishing, reusing, remanufacturing, and repairing of products and materials. For companies, this involves to set targets and to rethink their supply chain. This paper seeks to model an exhaustive multi-echelon closed-loop supply chain (CLSC) network. This network functions within uncertainty, and the model optimizes three different objectives. The first objective function maximizes the network's profit;the second objective function minimizes network emissions. The last objective function maximizes job positions created by the network. Optimizing three contradicting objectives is a problem, so an augmented epsilon constraint method is applied to improve the model. Given the rise of fast fashion in developed countries, this model is used in the clothing industry in Montreal, Canada. The model includes three scenarios over five years with two types of products. The result shows the attractiveness of such a network for companies looking for profit, sustainability, and entrepreneurship in the garment industry.

关键词： Closed-loop supply chain Circular economy Sustainable network Multi-objective optimization stochastic programming Garment industry

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Software for Data-Based stochastic programming Using Estimation

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INFORMS JOURNAL ON COMPUTING 2023年第6期35卷 1218-1224页

作者： Chen, Xiaotie Woodruff, David L. Univ Calif Davis Dept Math Davis CA 95616 USA Univ Davis Grad Sch Management Davis CA 95616 USA

We describe software for stochastic programming that uses only sampled data to obtain both a consistent sample-average solution and a consistent estimate of confidence intervals for the optimality gap using bootstrap ... 详细信息

关键词： data-driven stochastic programming bootstrap bagging

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Interval two-stage stochastic programming model under uncertainty for planning emission rights trading in the Yellow River basin of China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第14期30卷 40298-40314页

作者： Yu, Qianwen Wu, Fengping Shen, Junyuan Xu, Xia Suzhou Univ Sci & Technol Business Sch Suzhou 215009 Peoples R China Hohai Univ Business Sch Nanjing 211100 Peoples R China Suzhou Inst Dev & Planning Studies Suzhou 215004 Peoples R China Tongling Univ Architectural Engn Sch Tongling 244000 Peoples R China

As a critical way to realize the optimal allocation of water environment capacity resources in the basin, emission rights trading faces multiple uncertainties, making it extremely hard and challenging to formulate appropriate decisions and plans. Therefore, this study uses interval two-stage stochastic programming (ITSP) method to model the emission rights trading process with multiple uncertainties. It can promote the secondary optimal allocation of the emission rights between the demander and the supplier after the initial allocation. Externalities caused by environmental problems are internalized through the form of emission rights trading, thereby reducing the transaction costs and promoting the coordination and integrity of water pollution control among governments in a basin. Finally, the Yellow River basin is taken as an example for case analysis. The results show that the net revenue of emission rights system in the transaction status is better than that in the non-transaction status, and the average gap of net income reaches [171.031, 193.056] billion yuan. Under different reduction policies, the average water pollutant emission reduction in transaction status is [451.15, 628.34] thousand tons, which is generally less than [516.57, 670.05] thousand tons in non-transaction status. As policies get stricter and assimilative capacity of water bodies dwindles, reduction shrinks, leading to higher risks and economic loss from being unable to meet the discharge demand. When reduction policies are relatively loose and assimilative capacity is high, emission rights trading volume peaks. At this time, the trading volume of COD reached [29.05, 40.76] thousand tons, and that of NH3-N reached [3.74, 4.31] thousand tons. All these findings will offer insights for decision-makers on how to strike a balance between economic benefits and emission rights trading plans in the Yellow River basin.

关键词： Emission rights trading Yellow River basin stochastic programming Uncertainty Interval parameter

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Compromising allocation for optimising agri-food supply chain distribution network: a fuzzy stochastic programming approach

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第6期15卷 2019-2041页

作者： Gupta, Srikant Chaudhary, Sachin Singh, Rajesh Kr Garza-Reyes, Jose Arturo Kumar, Vikas Jaipuria Inst Management Jaipur Rajasthan India Autonomous State Med Coll Dept Community Med Shahjahanpur Uttar Pradesh India Management Dev Inst Gurgaon India Univ Derby Ctr Supply Chain Improvement Kedleston Rd Campus Derby DE22 1GB England Birmingham City Univ Birmingham England

The management of Agri-food supply chains is a complex task, given the unique product characteristics, perishability, uncertain demand, and specific storage requirements. This research introduces an innovative approach to optimizing product allocation among producers, brokers, wholesalers, and retailers, focusing on minimizing transportation costs and network delivery time through multi-objective programming. To address uncertainties, supply and demand constraints are modelled using a gamma distribution, and the maximum likelihood estimation method determines their parameters with specified probabilities. The study conducts a case analysis to showcase the model's practical effectiveness, and a numerical comparison with alternative approaches is included. The primary goal of this study is to enhance the efficiency of agri-food supply chain management practices, providing valuable insights for practitioners in the field, with a focus on cost reduction and improved delivery time.

关键词： Multi-objective optimization Supply chain network stochastic programming Gamma distribution Maximum likelihood estimation Fuzziness

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A stochastic programming APPROACH FOR SOLAR SALT PRODUCTION PROCESS OPTIMISATION

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SOUTH AFRICAN JOURNAL OF INDUSTRIAL ENGINEERING 2023年第3期34卷 97-110页

作者： Coetzer, R. Bisset, C. North West Univ Sch Ind Engn Potchefstroom South Africa

The solar salt mining process involves collecting salt water from the sea and trapping it in an interlinked network of shallow ponds where the sun evaporates most of the water. As soon as the brine reaches its saturation point, salt crystallisation occurs. Several factors influence this mining process, each with a degree of variability that results in a risk factor owing to uncertainty. This study proposes a two-stage stochastic programming model, called 'a recourse model', that maximises salt crystallisation while providing solutions that are hedges against uncertainty. First, the theoretical background on optimisation theory is provided, followed by an overview of the mining processes. Second, the recourse model is verified and validated using historical data and comparing the results with its deterministic counterpart. The main contribution of this study is the formulation of the recourse model and the value that this approach adds when dealing with uncertainty in any decision-making process.

关键词： stochastic programming

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