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stochastic programming model for incentive-based demand response considering complex uncertainties of consumers

IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第20期14卷 4488-4500页

作者： Zheng, Shunlin Sun, Yi Li, Bin Hu, Yajie Qi, Bing Shi, Kun Li, Yuanfei North China Elect Power Univ Sch Elect & Elect Engn 2 Beinong Rd Beijing Peoples R China China Elect Power Res Inst Rd 15 Qinghe Xiaoying Beijing Peoples R China State Grid Fujian Econ Res Inst Rd 221 Xiufeng Fuzhou Peoples R China

Incentive-based demand response (IBDR) has been recognized as a powerful tool to mitigate supply-demand imbalance in electricity market. However, the complex uncertainties of consumers, including participation uncertainty and responsiveness uncertainty, have been a central challenge to implement IBDR programs. In this paper, a stochastic programming model for IBDR considering the complex uncertainties of consumers is proposed. The proposed model can effectively deals with the above two uncertainties. Besides, the model of energy storage unit (ESU) has been improved to cope with properly the deviation between total actual balancing power and required balancing power. Moreover, the model enhances the applicability of IBDR to be applicable to both curtailment IBDR programs and absorbing IBDR programs by adding dynamic parameters. The model is formulated as a bi-level stochastic programming problem based on uncertain programming theory, and corresponding equivalent model is also given to solve the problem effectively. Finally, simulation results verify merits of the proposed model in cutting down total cost of DRA, decreasing risk cost of DRA and reducing balancing power deviation caused by uncertainty of consumers.

关键词： stochastic processes incentive schemes supply and demand pricing demand side management stochastic programming energy storage power markets uncertain programming theory corresponding equivalent model demand response aggregator reducing balancing power deviation two-consumer IBDR stochastic programming model incentive-based demand response complex uncertainties supply-demand imbalance participation uncertainty responsiveness uncertainty total actual balancing power required balancing power curtailment IBDR programmes bi-level stochastic programming problem

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Scenario-based stochastic linear programming model for multi-period disassembly lot-sizing problems under random lead time

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IFAC-PapersOnLine 2019年第13期52卷 595-600页

作者： Slama, I. Ben-Ammar, O. Masmoudi, F. Dolgui, A. IMT Atlantique LS2N UMR-CNRS 6004 La Chantrerie 4 rue Alfred Kastler - B.P. 20722 Nantes44307 France Faculty of Economics and Management Sciences of Sfax MODILS Airport Road km 4 BP 1088 Sfax3018 Tunisia University of Sfax Tunisia

In the last few years, there has been a growing interest in the disassembly scheduling problem to fulfil the demands of individual disassembled parts over a given planning horizon. An analysis of the literature shows that the disassembly lead time is often considered deterministic. Indeed, in real-life systems, this parameter is rarely known and often has uncertain values that can be caused by the complexity of disassembly operation. In this study, a new scenario-based stochastic linear programming model is proposed to deal with a multi-period, single product type and two-level disassembly lot-sizing problem under lead time uncertainty. The demand for each component is known for each time period and the real disassembly lead time of end-of-life product is an independent random discrete variable with a known probability distribution. The proposed model is used to determine the optimal quantity for disassembled end-of-life products in order to minimize the setup cost for end-of-life product and the sum of average inventory holding and backlogging costs for each component, over the planning horizon and all scenarios. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： stochastic models Linear programming Probability distributions stochastic programming stochastic systems Uncertainty analysis Lot sizing problems Random lead time Reverse logistics Scenario based stochastic programming model

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Scenario-based stochastic linear programming model for multi-period disassembly lot-sizing problems under random lead time 9

Scenario-based stochastic linear programming model for multi...

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9th IFAC/IFIP/IFORS/IISE/INFORMS Conference on Manufacturing modelling, Management and Control (IFAC MIM)

作者： Slama, I. Ben-Ammar, O. Masmoudi, F. Dolgui, A. IMT Atlantique LS2N UMR CNRS 6004 4 Rue Alfred Kastler BP 20722 F-44307 Nantes France MODILS Fac Econ & Management Sci Sfax Airport Rd Km 4BP 1088 Sfax 3018 Tunisia Univ Sfax Engn Sch Sfax Lab Mech Modeling & Prod LA2MP Sfax Tunisia

In the last few years, there has been a growing interest in the disassembly scheduling problem to fulfil the demands of individual disassembled parts over a given planning horizon. An analysis of the literature shows that the disassembly lead time is often considered deterministic. Indeed, in real-life systems, this parameter is rarely known and often has uncertain values that can be caused by the complexity of disassembly operation. In this study, a new scenario-based stochastic linear programming model is proposed to deal with a multi-period, single product type and two-level disassembly lot-sizing problem under lead time uncertainty. The demand for each component is known for each time period and the real disassembly lead time of end-of-life product is an independent random discrete variable with a known probability distribution. The proposed model is used to determine the optimal quantity for disassembled end-of-life products in order to minimize the setup cost for end-of-life product and the sum of average inventory holding and backlogging costs for each component, over the planning horizon and all scenarios. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Reverse logistic disassembly lot-sizing problem random lead time stochastic programming model scenario-based approach

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A stochastic programming model for Closed-loop Supply Chain Network Design Based on Environmental Responsibility

A Stochastic Programming Model for Closed-loop Supply Chain ...

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IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

作者： Yang Yu-xiang Guo Yu-shi Huang Zu-qing China Jiliang Univ Coll Econ & Management Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781479960583

In this paper, a two-stage stochastic programming model is proposed in which uncertainty of demands and the amount of returned products are considered, and specially, the environmental responsibility also considered. A hybrid solution method is developed. Finally, numerical examples are proposed to design a closed-loop supply chain network.

关键词： closed-loop supply chain stochastic programming model hybrid solution method environmental responsibility

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A stochastic programming model for Closed-loop Supply Chain Network Design Based on Environmental Responsibility

A Stochastic Programming Model for Closed-loop Supply Chain ...

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2014 IEEE International Conference on Service Operations and Logistics,and Informatics

作者： Yang Yu-xiang Guo Yu-shi Huang Zu-qing College of Economics Management China Jiliang University College of Economics and Management China Jiliang University

In this paper,a two-stage stochastic programming model is proposed in which uncertainty of demands and the amount of returned products are considered,and specially,the environmental responsibility also considered.A hybrid solution method is ***,numerical examples are proposed to design a closed-loop supply chain network.

关键词： closed-loop supply chain stochastic programming model hybrid solution method environmental responsibility

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Emergency medical service planning considering dynamic and stochastic demands of infected and non-infected patients during epidemics

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2024年第4期75卷 705-719页

作者： Luo, Li Wang, Yikun Jiang, Peng Zhuo, Maolin Wang, Qingyi Sichuan Univ Business Sch Chengdu Peoples R China Chengdu Ind & Trade Coll Sch Finance & Trade Management Chengdu Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China

During a large-scale epidemic, a local healthcare system can be overwhelmed by a large number of infected and non-infected patients. To serve the infected and non-infected patients well with limited medical resources, effective emergency medical service planning should be conducted before the epidemic. In this study, we propose a two-stage stochastic programming model, which integrally deploys various types of emergency healthcare facilities before an epidemic and serves infected and non-infected patients dynamically at the deployed healthcare facilities during the epidemic. With the service equity of infected patients and various practical requirements of emergency medical services being explicitly considered, our model minimizes a weighted sum of the expected operation cost and the equity cost. We develop two comparison models and conduct a case study on Chengdu, a Chinese city influenced by the COVID-19 epidemic, to show the effectiveness and benefits of our proposed model. Sensitivity analyses are conducted to generate managerial insights and suggestions. Our study not only extends the existing emergency supply planning models but also can facilitate better practices of emergency medical service planning for large-scale epidemics.

关键词： stochastic programming model emergency medical service infected and non-infected patients dynamic and stochastic demands epidemics

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An optimization approach to the scale of port cold-chain dedicated terminals considering uncertainties

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ENGINEERING OPTIMIZATION 2022年第11期54卷 1887-1902页

作者： Ma, Qianli Ma, Baiyu He, Ying Sun, Weimeng Dalian Maritime Univ Sch Maritime Econ & Management Dalian Peoples R China Dalian Inst Sci & Technol Sch Transportat Dalian Peoples R China Minist Transport Peoples Republ China Water Transport Bur Beijing Peoples R China

As an important node of the international cold-chain logistics system, the special terminal for refrigerated ships is the core facility for cargo loading and unloading. To minimize the sum of construction costs and operating costs of dedicated cold-chain terminals, a stochastic programming model is established considering uncertainty of the type and time of arrival of refrigerated vessels and special requirements for cargo handling and storage, and the simulation optimization method based on tabu search is used to determine the optimal scale of cold-chain terminals. A case study on the port cold-chain system of an offshore fishery base is then presented to investigate the specific influence of uncertainties on the scale of cold-chain terminals. This provides a reference for scientific planning of cold-chain facilities and improving the efficiency of cold-chain systems in the sea-land exchange node.

关键词： stochastic programming model cold-chain infrastructure terminal scale system optimization

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A joint stochastic programming approach for aircraft landing and terminal traffic flow problem under weather scenarios

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Journal of the Air Transport Research Society 2024年第2期3卷 255-264页

作者： T.C.Yeung Chenliang Zhang Zhongyi Jin Kam K.H.Ng Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHung HomHong Kong SARChina

A stochastic programming model on the combination of aircraft landing problem and terminal traffic flow management under uncertainty is proposed in this *** reality,various kinds of uncertainties,including adverse weather events,occur more frequently and interrupt air traffic *** of these uncertain events can appear and disappear in a short ***,the occurrence of these events affects the flights significantly,delaying the flights or event harming the safety of ***,it is essential to respond to these uncertainties to ensure the level of safety at *** operation may cease due to strong wind shear,turbulence,microburst or other extreme weather scenarios,is limited due to the restricted airspace capacity,and we extend the problem covering the terminal *** proposed model can significantly reduce the total delay time of aircraft in the computations.

关键词： stochastic programming model Aircraft landing problem Terminal traffic flow problem Sample Average Approximation

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stochastic Integrated Supplier Selection and Disruption Risk Assessment Under Ripple Effect 1

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International-Federation-of-Information-Processing-Working-Group-5.7 (IFIP WG 5.7) International Conference on Advances in Production Management Systems (APMS)

作者： Liu, Ming Liu, Zhongzheng Chu, Feng Zheng, Feifeng Chu, Chengbin Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China Univ Paris Saclay Univ Evry IBISC F-91025 Evry France Donghua Univ Glorious Sun Sch Business & Management Shanghai 200051 Peoples R China Univ Paris Est ESIEE Paris F-93162 Noisy Le Grand France

ISBN: (数字)9783030859060

ISBN: (纸本)9783030859060;9783030859053

The impact of the COVID-19 pandemic in the supply chain (SC) evokes the need for valid measures to cope with the SC disruption risk. Supplier selection and disruption risk assessment, as valid measures, have received increasing attentions from academia. However, most of existing works focus on supplier selection and disruption risk assessment separately. This work investigates an integrated supplier selection and disruption risk assessment problem under ripple effect. The objective is to minimize the weighted sum of the disrupted probability and the total cost for the manufacturer. For the problem, a new stochastic programming model combined with Bayesian network (BN) is formulated. Then, an illustrative example is conducted to demonstrate the proposed method.

关键词： Supply chain disruption risk Supplier selection Disruption risk assessment stochastic programming model Bayesian network

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Managing hospital inpatient beds under clustered overflow configuration

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COMPUTERS & OPERATIONS RESEARCH 2022年 148卷

作者： Gong, Xuran Wang, Xiuxian Zhou, Liping Geng, Na Shanghai Jiao Tong Univ Dept Ind Engn & Management Shanghai Peoples R China Shanghai Jiao Tong Univ Sino US Global Logist Inst Shanghai Peoples R China

Inpatient beds represent one of the most critical resources in hospitals, of which the availability has been under increasing pressure in China. Such beds are usually to be either used exclusively ("dedicated") by each department or shared ("pooled") among departments. This paper considers a generalized bed configuration known as the clustered overflow configuration. The departments are partitioned and managed as clusters. Each department has its dedicated beds and can also admit patients to overflowed beds shared among the departments in the same cluster. A mixed-integer stochastic programming model is developed to optimize the partition and bed-allocation decisions. The objective is to minimize the weighted total cost of rejecting patients, holding pa-tients waiting, nursing cost, and partitioning cost. A simulation-based metaheuristic approach (SMA) is proposed to solve the problem. A niching genetic algorithm (NGA) framework is first proposed to optimize the partitions, and then each partition is evaluated by optimizing the underlying bed-allocation plan through adaptive hyperbox algorithm-based local search (AHA-LS). In AHA-LS, two speeding mechanisms are proposed to effectively identify promising partitions and discard the worst ones. One is an original sequential simulation budget allocation (SSBA) mechanism to sequentially allocate the simulation iteration to evaluate the bed allocation decisions for a given partition;the other is optimal computing budget allocation (OCBA) to allocate the simulation budget to evaluate the bed-allocation plans. The elite clusters form a cluster pool, based on which a set covering model is proposed and solved to obtain a better solution. Case studies are conducted based on real data collected from a public hospital in Shanghai, China. The numerical results collectively demonstrate the applicability and effi-ciency of the proposed method. Sensitivity analyses are also performed to gain managerial insights.

关键词： Bed allocation Clustered overflow configuration stochastic programming model Simulation optimization Simulation-based metaheuristic

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