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Optimal Medium-Term Electricity Procurement for Cement Producers

IEEE ACCESS 2024年 12卷 54934-54952页

作者： Arellano, Jose Carrion, Miguel Garcia-Cerezo, Alvaro Univ Castilla La Mancha Dept Elect Engn Toledo 45071 Spain

In the current economic and energy situation, it is imperative for major electricity consumers to meticulously determine their electricity procurement. This is the case for cement producers. This work intends to determine the most efficient approach to electricity acquisition, considering their involvement in the electricity pool, power-purchase agreements, and the potential installation of a photovoltaic self-production unit and a battery storage system. To achieve this, we model the electricity consumption flexibility of cement producers, accounting for all production processes associated with cement and clinker manufacturing. This results in the formulation of a mid-term decision-making problem under uncertainty, which is addressed through the application of a two-stage risk-averse stochastic programming formulation. In order to reduce the computational size of the resulting optimization problem, the planning horizon is characterized by a set of representative periods obtained through a procedure based on chronological time-period clustering. To analyze the practical viability of the proposed approach, a realistic case study is solved featuring an existing cement producer, real-world energy pool prices, and data pertaining to renewable energy production. The findings derived from this case study highlight the viability of installing a photovoltaic self-production unit as a strategic measure to reduce the expected procurement expenses for the cement producer. Moreover, the photovoltaic self-production unit proves instrumental in mitigating the vulnerability to elevated procurement costs. It has been also observed that an imprecise modeling of the technical characteristics within the cement manufacturing processes can lead to a substantial underestimation of procurement costs.

关键词： Large electricity consumer power purchase agreements cement plant self-generation facility stochastic programming

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Improving disaster relief plans for hurricanes with social media

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2024年第10期75卷 1965-1978页

作者： Paul, Jomon A. Zhang, Minjiao Yang, Muer Xu, Chong Kennesaw State Univ Dept Econ Finance & Quantitat Anal Kennesaw GA USA Kennesaw State Univ Kennesaw GA USA Univ St Thomas Minneapolis MN USA Kennesaw State Univ Dept Econ Finance & Quantitat Anal 560 Parliament Garden Way Kennesaw GA 30144 USA

Decisions on humanitarian responses to natural disasters are subject to considerable epistemic uncertainty. This paper advocates for postponing the decision point of pre-positioning relief supplies as close to landfall as possible and searching social media right post-landfall so that the demands can be estimated more accurately. We use a realistic hurricane preparedness case to demonstrate the effectiveness of our models and parametric estimation using social media data. The optimal timing to deploy relief supplies before hurricane landfall is noted to be 12 h in advance, which reduces the total cost by 13% more than if relief supplies are deployed 18+ hours in advance. Meanwhile, utilizing social media information can reduce the total cost as well as all kinds of specific costs being considered, excluding the point of dispensing (POD) sites setup cost, by approximately 15%. As the attitude toward risk goes from optimistic, to neutral, and to pessimistic, the number of PODs increases from 3 to 7, and to 8. A similar pattern can be noted in the total costs incurred by these decision-makers. Further, as the aversion to risk increases, locations tend to be chosen farther from landfall with these farther locations serving the less severe patients.

关键词： Disaster management humanitarian logistics social media epistemic uncertainty stochastic programming practice of OR optimization location

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Towards sustainable UAV operations: Balancing economic optimization with environmental and social considerations in path planning

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2024年 181卷

作者： Hu, Zhangchen Chen, Heng Lyons, Eric Solak, Senay Zink, Michael Calif State Univ Coll Business & Econ Fullerton CA 92831 USA Univ Massachusetts Amherst Isenberg Sch Management Amherst MA USA Univ Nebraska Lincoln Coll Business Lincoln NE USA Univ Massachusetts Amherst Coll Engn Amherst MA USA

Unmanned Aerial Vehicles (UAVs), i.e., drones, are expected to be widely used in various applications, such as parcel delivery and passenger transport, with the benefits of mitigating traffic congestion and reducing carbon emissions. In this paper, we study a UAV path planning problem under uncertain weather conditions, and design a data-driven dynamic decision support system for multiple types of UAVs. To this end, we categorize all relevant costs into three types, namely, economic, environmental, and social costs, and formulate a nonlinear two-stage stochastic programming model to establish optimal paths for UAV missions under weather uncertainty. We then discretize the nonlinear model and propose a tight linear approximation for the discretized problem to allow for a near real-time implementation. To quantify weather uncertainty, we propose a weather scenario generation algorithm to map ensemble-based weather forecast information to airspace blockage maps. With comprehensive computational studies through simulations, we show that our proposed stochastic approach can lower operating costs by an average of around 6%, where the savings increase as weather conditions become more severe and complex. We also find that, for missions operated by small UAVs, it is not sufficient to determine a path solely based on economic cost minimization, but it should rather be through total cost minimization, which involves environmental and social costs. Considering only the economic cost in the optimization may lead to much higher non-economic costs. However, for missions operated by large UAVs, it is sufficient to determine paths through economic cost optimization, as including environmental and social costs in the optimization process does not result in solutions that are much different from those obtained by considering only the economic costs. For both small and large UAVs, a path established solely through environmental or social cost minimization may not be economically susta

关键词： Drone UAV path planning Weather evolution Emissions UAV noise stochastic programming

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Profit-based unit commitment models with price-responsive decision-dependent uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期314卷 1052-1064页

作者： Lejeune, Miguel A. Dehghanian, Payman Ma, Wenbo George Washington Univ Washington DC 20052 USA

Highlighting the increasing importance of demand elasticity in electricity markets and its impact on the revenues of power generating companies, this paper proposes new profit-based unit commitment models that effectively capture the uncertainty in the willingness-to-pay the price set for the elastic demand. To develop a new revenue scheme for power generating companies, we use a coupling function to model the willingness-to-pay response of the elastic demand as a decision-dependent source of uncertainty. The coupling function reflects how power generating companies' pricing decisions may influence the market appeal (i.e., the buyer's willingness-to-pay) and how it affects their revenues. The optimization models are stochastic mixed-integer nonlinear problems with nonconvex continuous relaxations and are not amenable to a numerical solution in their original forms. We devise a convexification reformulation method and derive valid inequalities to strengthen the formulation. We propose a learning framework to parameterize the willingnessto-pay functions and the concept of the value of the decision-dependent solution to quantify the value of the uncertainty modeling approach. Numerical tests on power systems of various sizes, demand portfolios, and price elasticity levels show (i) how the valid inequalities speed up the solution process, (ii) the benefits of properly modeling decision-dependent uncertainty and demand elasticity, and (iii) how the incorporation of decision-dependent uncertainty in demand elasticity can change the power generating companies' decisions and revenue estimation.

关键词： stochastic programming Price responsiveness Profit-based unit commitment Willingness-to-pay Decision-dependent uncertainty

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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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Integrated optimization of vessel dispatching and empty container repositioning considering turnover time uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 197卷

作者： Xiang, Xi Wang, Zihao Gong, Lin Jia, Shuai Liu, Xin Liu, Minxia Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Intelligent Transportat Thrust Guangzhou 511400 Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China

The global trade disproportion results in the accumulation of containers in import-dominated ports and shortages in export-dominated ports, causing congestion and high freight costs, thus hindering maritime shipping economy development. To address these issues, this study develops a stochastic programming model considering uncertain container turnover times. The model integrates decisions for vessel deployment and empty container repositioning over multiple planning periods through a two-stage decision process, aiming to minimize the total cost, including vessel deployment, container leasing, and penalty costs for unfulfilled demand. By formulating the scenario selection problem as a p-median problem, we effectively reduce the model size. We propose an accelerated Benders decomposition algorithm which leverages the independence of sub-problems in the second stage to enable parallel computation. Numerical experiments show that our Benders decomposition algorithm improves solution speed by over 63% compared to the Gurobi optimization solver. Furthermore, our integrated optimization approach proves to be more cost-effective than the reactive method used by shipping lines, achieving an average cost savings of 0.72%. Additionally, our method of constructing turnover time scenarios to address uncertainty saves approximately 0.45% in costs compared to using the probability distribution of container turnover time.

关键词： Maritime shipping Vessel deployment Empty container repositioning stochastic programming Benders decomposition

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A stochastic variance reduced gradient method with adaptive step for stochastic optimization

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OPTIMAL CONTROL APPLICATIONS & METHODS 2024年第3期45卷 1327-1342页

作者： Li, Jing Xue, Dan Liu, Lei Qi, Rulei Qingdao Univ Sch Math & Stat Qingdao Peoples R China Qingdao Univ Sch Math & Stat Qingdao 266071 Peoples R China

In this paper, we propose a stochastic variance reduction gradient method with adaptive step size, referred to as the SVRG-New BB method, to solve the convex stochastic optimization problem. The method could be roughly viewed as a hybrid of the SVRG algorithm and a new BB step mechanism. Under the condition that the objective function is strongly convex, we provide the linear convergence proof of this algorithm. Numerical experiment results show that the performance of the SVRG-New BB algorithm can surpass other existing algorithms if parameters in the algorithm are properly chosen. This paper introduces the SVRG-New BB method, enhancing SVRG by incorporating the New BB step mechanism for adaptive step size calculation in stochastic variance reduction gradient methods. It eliminates the need for manual step size tuning, enhancing efficiency and applicability to complex problems in machine learning. SVRG-New BB has been shown to achieve linear convergence under strong convexity, and numerical experiments demonstrate its superior performance over existing stochastic gradient methods in solving regularized logistic regression problems. image

关键词： Barzilai-Borwein step sizes machine learning stochastic programming stochastic variance reduced gradient

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Innovative Applications of Unsupervised Learning in Uncertainty-Aware Pharmaceutical Supply Chain Planning

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IEEE ACCESS 2024年 12卷 107984-107999页

作者： Kochakkashani, Farid Kayvanfar, Vahid Baldacci, Roberto George Washington Univ Dept Elect & Comp Engn Washington DC USA Hamad Bin Khalifa Univ Coll Sci & Engn Div Engn Management & Decis Sci Doha Qatar

The significance of resiliency, reliability, and equity in the pharmaceutical supply chain is often overlooked but becomes evident in the wake of disastrous events. Disruptive incidents underscore the critical importance of these concepts, necessitating the development of innovative frameworks to effectively address the challenges that emerge in their aftermath. This paper introduces a framework specifically designed to address the issues arising from disruptions within the pharmaceutical supply chain. A novel mixed-integer nonlinear programming (MINLP) model is proposed to formulate the pharmaceutical supply chain that encompasses the distribution of both cold and non-cold pharmaceuticals and vaccines. The abundance of diverse pharmaceuticals and vaccines, each with its distinct characteristics, presents a formidable planning obstacle. A noteworthy contribution of this study lies in innovatively applying AI-driven methodologies to pharmaceutical supply chain, employing five pioneering unsupervised learning algorithms for improved inventory management and control. The model's uncertainty is effectively addressed through an innovative joint chance constraint (JCC) formulation. By employing JCC, the model ensures a high level of reliability in satisfying uncertain patient demands. The MINLP formulation with JCCs presents significant computational complexities and intractability. To alleviate these issues, state-of-the-art reformulation algorithms are provided to transform the model into its equivalent mixed-integer linear programming form. The results indicate the efficiency of the equivalent reformulation techniques and illustrate the capabilities of the model to alleviate the resiliency, reliability, and equity concerns.

关键词： Pharmaceutical supply chain equity resiliency mathematical optimization stochastic programming unsupervised learning Pharmaceutical supply chain equity resiliency mathematical optimization stochastic programming unsupervised learning

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A column generation approach for the quay crane allocation problem considering uncertain operation efficiency

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 190卷

作者： Tang, Xufeng Li, Xinqi Yin, Ming Shanghai Maritime Univ Coll Transportat & Commun Shanghai 201306 Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China

This study constructs a mixed integer programming model for the integrated quay crane allocation problem to minimize vessels' waiting time and delay time cost. Taking the uncertainty of quay crane operation efficiency into consideration, this study constructs a stochastic programming model and a distributional robust chance constraint (DRCC) model. In addition, column generation is applied to solve the three proposed models. Moreover, this paper uses real-world data from Shanghai Yangshan Port to verify the effectiveness of proposed model and algorithm. To prove the robustness of the DRCC model, this study conducts an uncertain parameters sensitivity analysis, and analyzes the performance of the models proposed above. The results show that the proposed DRCC model has better anti-interference ability. The conclusion drawn from this study has potential implications for resource allocation within port operations.

关键词： Port Arrangement Quay Crane Allocation Problem Distributional Robust Optimization stochastic programming Column Generation

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An Integrated Predictive Maintenance and Operations Scheduling Framework for Power Systems Under Failure Uncertainty

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INFORMS JOURNAL ON COMPUTING 2024年第5期36卷 1335-1358页

作者： Okumusoglu, Bahar Cennet Basciftci, Beste Kocuk, Burak Sabanci Univ Ind Engn Program TR-34956 Istanbul Turkiye Univ Iowa Tippie Coll Business Dept Business Analyt Iowa City IA 52242 USA

Maintenance planning plays a key role in power system operations under uncertainty as it helps providers and operators ensure a reliable and secure power grid. This paper studies a short-term condition -based integrated maintenance planning with operations scheduling problem while considering the possible unexpected failure of generators as well as transmission lines. We formulate this problem as a two -stage stochastic mixedinteger program with failure scenarios sampled from the sensor -driven remaining lifetime distributions of the individual system elements whereas a joint chance constraint consisting of Poisson Binomial random variables is introduced to account for failure risks. Because of its intractability, we develop a cutting -plane method to obtain an exact reformulation of the joint chance constraint by proposing a separation subroutine and deriving stronger cuts as part of this procedure. We also derive a second -order cone programming -based safe approximation of this constraint to solve large-scale instances. Furthermore, we propose a decomposition algorithm implemented in parallel fashion for solving the resulting stochastic program, which exploits the features of the integer L-shaped method and the special structure of the maintenance and operations scheduling problem to derive valid and stronger sets of optimality cuts. We further present preprocessing steps over transmission line flow constraints to identify redundancies. To illustrate the computational performance and efficiency of our algorithm compared with more conventional maintenance approaches, we design a computational study focusing on a weekly plan with daily maintenance and hourly operational decisions involving detailed unit commitment subproblems. Our computational results on various IEEE instances demonstrate the computational efficiency of the proposed approach with reliable and cost-effective maintenance and operational schedules.

关键词： mixed-integer programming joint chance constraints condition-based maintenance unit commitment power systems stochastic programming

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