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15th ASME International Manufacturing Science and Engineering Conference (MSEC)

作者： Meng, Qiunan Xu, Xun Dalian Univ Technol Fac Management & Econ Dalian Peoples R China Univ Auckland Dept Mech Engn Auckland New Zealand

ISBN: (纸本)9780791884751

In a competitive and volatile market, the price is needed to make in consideration of the uncertain demands of the customers and the limited capacities of enterprises. This requires the coordination decisions on pricing, delivery and resource allocation to increase profit and guarantee service quality for firms. The joint decision on pricing and resource allocation with demand and processing time uncertainty is becoming an issue for a profit-maximizing firm that produces various products. We propose a two-stage model based on stochastic programming to address this joint problem. aiming to maximize profit of products. We present a scenario-simulation approach to describe the stochastic variables;then the deterministic two-stage mixed integer linear programming model is formulated depending on those scenarios. We develop an algorithm by ant colony algorithm to obtain the near-optimal solutions of the models above. The numerical experiments were conducted to validate the proposed models. The results show that the stochastic approach outperfonns the deterministic model in the different problem scales and yield the better values of compared metrics. The outcomes also imply that this joint pricing model can provide managerial inspiration for enterprises in the customization environment.

关键词： Pricing Resource Allocation Scenario Simulation two-stage stochastic programming

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stochastic optimization for siting and sizing of renewable distributed generation and D-STATCOMs

e-Prime - Advances in Electrical Engineering, Electronics an...

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2025年

作者： Alejandro Valencia-Díaz Sebastián García H. Ricardo A. Hincapie I. Department of Electrical Engineering Universidad Tecnológica de Pereira Pereira Colombia

This paper presents a novel methodology for the optimal placement and sizing of distributed renewable generators and D-STATCOMs in electrical distribution systems. The problem is formulated as a mixed-integer second-order cone stochastic model with an objective function that minimizes the investment costs of purchasing and installing D-STATCOMs, wind turbines, photovoltaic systems, and small hydropower plants, as well as the expected value of energy purchase cost by the distribution company. A two-stage stochastic programming formulation addresses uncertainties in electrical demand, energy prices, wind-based distributed generation, solar-based distributed generation, and small hydropower-based distributed generation. stochastic scenarios are generated using the k-means clustering technique. Moreover, a relaxed convex model is proposed to reduce the number of candidate nodes for installation, significantly improving computational efficiency while ensuring optimality. The proposed methodology’s accuracy, efficiency, and robustness are validated on two benchmark distribution systems with 70 and 136 nodes, respectively. The results demonstrate that the simultaneous integration of distributed renewable generators and D-STATCOMs effectively reduces operational costs and energy losses, achieving a loss reduction of 42.3% and 13.6% for the 70-node and 136-node test systems, respectively, while enhancing voltage regulation and improving the loading of network components. Furthermore, the model estimates the cost reductions required for solar and wind technologies to become economically viable under uncertainty, providing a practical tool for policymakers to design effective financial incentives. This feature is particularly relevant for developing countries, where high capital costs and limited public resources hinder renewable energy integration.

关键词： D-STATCOMs Electrical distribution networks Mixed-integer second-order cone model Renewable distributed generation two-stage stochastic programming

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A two-stage stochastic programming Model for Emergency Resources Storage Region Division

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Systems Engineering Procedia 2012年 5卷 125-130页

作者： Jing Wang Haoxiong Yang Jianming Zhu School of Business Beijing Technology and Business University Beijing 100048 College of Engineering Graduate University of Chinese Academy of Sciences Beijing 100049

In recent years the frequent emergencies have become one of the important factors which influenced our social development. Emergency resources storage takes an important role in emergency management research which decides whether the disaster relief process can be carried out smoothly. However the emergency resources storage construction is a complicated project. This paper aims to study the regional emergency resources storage. We analyse the necessity of regional emergency resources storage and the first step of regional emergency resources storage system is region division. A two-stage stochastic programming model is proposed to solve the region division problem. Finally, we propose a case study to highlight efficiency of the proposed solution strategy.

关键词： two-stage stochastic programming Regional Emergency Resources Storage scenario analysis

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An Integrated Disaster Preparedness Model for Retrofitting and Relief Item Transportation

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NEtwoRKS & SPATIAL ECONOMICS 2019年第4期19卷 1031-1068页

作者： Doyen, Alper Aras, Necati Konya Tech Univ Dept Ind Engn TR-42130 Konya Turkey Bogazici Univ Dept Ind Engn TR-34342 Istanbul Turkey

In this study, a two-stage stochastic integer programming model is developed with a centralized planning perspective to simultaneously address mitigation and response decisions in humanitarian logistics, where the mitigation decisions involve both building and transportation infrastructure retrofitting. The objective is to minimize the total cost of retrofitting, relief item transportation and relief item shortage under a limited mitigation budget. Due to the excessive number of binary decision variables, solving the model becomes computationally difficult. Therefore, we propose Lagrangean relaxation to decouple the overall model and solve it by Lagrangean heuristics. Computational results indicate the efficiency of the solution approaches in providing high quality feasible solutions to problem instances of realistic size and complexity.

关键词： Humanitarian logistics Disaster mitigation Disaster response two-stage stochastic programming Lagrangean relaxation

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A stochastic programming APPROACH FOR SHELTER LOCATION AND EVACUATION PLANNING

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RAIRO-OPERATIONS RESEARCH 2018年第3期52卷 779-805页

作者： Bayram, Vedat Yaman, Hande TED Univ Dept Ind Engn Ankara Turkey Bilkent Univ Dept Ind Engn Ankara Turkey

Shelter location and traffic allocation decisions are critical for an efficient evacuation plan. In this study, we propose a scenario-based two-stage stochastic evacuation planning model that optimally locates shelter sites and that assigns evacuees to nearest shelters and to shortest paths within a tolerance degree to minimize the expected total evacuation time. Our model considers the uncertainty in the evacuation demand and the disruption in the road network and shelter sites. We present a case study for a potential earthquake in Istanbul. We compare the performance of the stochastic programming solutions to solutions based on single scenarios and mean values.

关键词： Disaster management evacuation traffic management shelter location constrained system optimal two-stage stochastic programming second order cone programming

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A stochastic programming approach toward optimal design and planning of an integrated green biodiesel supply chain network under uncertainty: A case study

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ENERGY 2018年 156卷 661-687页

作者： Ghelichi, Zabih Saidi-Mehrabad, Mohammad Pishvaee, Mir Saman Iran Univ Sci & Technol Sch Ind Engn Tehran *** Iran

Biodiesel has recently attracted much interest as an appropriate alternative for the fossil diesel which is mostly consumed in the transportation sector. Meanwhile, Jatropha Curcas L. has emerged as one of the most promising biofuel feedstocks due largely to its salient features such as compatibility with arid and semi-arid lands. In this regard, this paper unveils a two-stage stochastic programming model for the design of an integrated green biodiesel supply chain network from Jatropha Curcas feedstocks. The concerned biodiesel supply chain design is an environmentally friendly mixed-integer linear programming, multi-period and multi-product model. Furthermore, a flexible stochastic programming approach is developed and applied to the supply chain network model. This proposed approach is indeed an extension of a two-stage scenario-based stochastic programming approach which incorporates min-max relative regret in a soft worst-case framework. Given the natural variability of long-term decisionmaking, fuel demand and Jatropha Curcas trees yielding are hemmed in by uncertainty in this model. The performance of the proposed framework and biodiesel supply chain network design is corroborated through ten realizations and a myriad of various analyses in a real case study of Iran. The derived results and their analyses acknowledge the efficiency and applicability of the model. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Jatropha curcas L. Biodiesel supply chain network two-stage stochastic programming Maximum relative regret Greenhouse gas emission

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Real-Time Scheduling of Demand Response Options Considering the Volatility of Wind Power Generation

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2019年第4期10卷 1633-1643页

作者： Talari, Saber Shafie-Khah, Miadreza Chen, Yue Wei, Wei Gaspar, Pedro D. Catalao, Joao P. S. Univ Beira Interior C MAST P-6201001 Covilha Portugal Fraunhofer Inst Energy Econ & Energy Syst Technol D-34119 Kassel Germany Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China Univ Porto INESC TEC P-4200465 Porto Portugal Univ Porto Fac Engn P-4200465 Porto Portugal Univ Lisbon Inst Super Tecn INESC ID P-1049001 Lisbon Portugal

In this paper, a new methodology to unleash the potential of demand response (DR) in real-time is presented. Customers may tend to apply their DR potential in the real-time market in addition to their scheduled potential in the day-ahead stage. Thus, the proposed method facilitates balancing the real-time market via DR aggregators. It can be vital, once the stochastic variables of the network such as production of wind power generators do not follow the forecasted production in real-time and have some distortions. two-stage stochastic programming is employed to schedule some DR options in both day-ahead and real-time markets. DR options in real-time are scheduled based on possible scenarios that reflect the behaviors of wind power generation and are generated through Monte-Carlo simulation method. The merits of the method are demonstrated in a 6-bus case study and in the IEEE RTS-96, which shows a notable reduction in total operation cost.

关键词： Optimal demand response real-time market two-stage stochastic programming uncertainty handling wind power generation

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Designing autonomous vehicle incentive program with uncertain vehicle purchase price

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2019年 103卷 226-245页

作者： Chen, Shukai Wang, Hua Meng, Qiang Natl Univ Singapore Dept Civil & Environm Engn Singapore 117574 Singapore Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China

This study proposes an autonomous vehicle incentive program design (AV-IPD) problem for a local government, which aims to promote the adoption of autonomous vehicles (AVs) by deploying AV lanes and subsidizing the purchase of AVs with the objective of system optimum subject to a fixed budget. As it is difficult to anticipate the changing AV market conditions in the future, we take into account the uncertainty in AV purchase price. For the AV-IPD problem, we firstly decide which regular lanes should be converted into exclusive AV lanes. Secondly, we determine the AV purchase subsidy for each realization of AV purchase price with a given AV lane deployment scheme. A binary logit model is applied to characterize the vehicle choice behavior of users. The AV-IPD problem is formulated as a two-stage stochastic programming model with equilibrium constraints. To solve the AV-IPD problem, we develop a solution method based on linear approximation techniques and duality theories. Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and solution method.

关键词： Autonomous vehicle Network design Purchase subsidy two-stage stochastic programming

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two-stage Integrated Electricity and Heat Market Clearing With Energy Stations

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IEEE ACCESS 2019年 7卷 44928-44938页

作者： Wang, Jinran Wei, Zhinong Yang, Bin Yong, Ye Xue, Mingfeng Sun, Guoqiang Zang, Haixiang Chen, Sheng Hohai Univ Coll Energy & Elect Engn Nanjing 210098 Jiangsu Peoples R China State Grid Jiangsu Elect Power Co Ltd Dept Market Nanjing 210029 Jiangsu Peoples R China State Grid Jiangsu Elect Power Co Ltd Wuxi Power Supply Co Wuxi 214002 Peoples R China

Integrated energy systems (IESs) provide a synergistic supply of electricity and heat, which offers a new approach for district heating. However, the operational risk of IESs increases continuously as the extent of renewable energy source (RES) penetration continues to increase. This condition negatively impacts the benefits of IESs to the participants in the integrated energy market (IEM) and increases the costs of the system. This paper addresses these issues by proposing a two-stage stochastic programming model that considers RES penetration for IESs operated independently of electricity grids (i.e., islanded IESs). Then, a two-stage clearing model of the IEM is established, and a market settlement process is designed. The simulation results demonstrate that the two-stage clearing model of the IEM can effectively alleviate the operational risk associated with high RES penetration by allowing market participants to make timely adjustments corresponding to their interests according to changes in RES generation.

关键词： Real-time market integrated electricity and heat system integrated energy market market clearing market settlement two-stage stochastic programming

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Demand Response in Data Centers: Integration of Server Provisioning and Power Procurement

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IEEE TRANSACTIONS ON SMART GRID 2019年第5期10卷 4928-4938页

作者： Kwon, Soongeol Ntaimo, Lewis Gautam, Natarajan SUNY Binghamton Dept Syst Sci & Ind Engn Binghamton NY 13902 USA Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77843 USA

Data centers are among the fastest growing industries in the United States economy in terms of IT service and usage of data. However, data centers consume an excessive amount of energy that contributes to huge energy costs and CO2 emissions;thus, they face challenges in energy cost savings and making a positive impact on the environment. Therefore, data centers have been striving to: 1) improve energy efficiency in server operations by applying server provisioning techniques, and 2) utilize renewable energy and energy storage for power procurement. Although data centers have obtained financial and environmental benefits from the aforementioned efforts, there still exists significant room for improvement by facilitating demand response based on the co-optimization of server operations and power procurement, which has not been well implemented in the current practice. The main objective of this paper is a new holistic approach that considers server operations and power procurement simultaneously for implementing demand response. Also, this paper develops a decision-making model based on two-stage stochastic programming to successfully address the inherent stochastic nature and demonstrate the impact of the proposed model via numerical experiments. The proposed model is designed to support practitioners' strategic and operational decision-making for demand response in data centers.

关键词： Demand response data centers server provisioning power procurement renewable energy energy storage two-stage stochastic programming

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