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A mathematical programming approach for equitable COVID-19 vaccine distribution in developing countries

ANNALS OF OPERATIONS RESEARCH 2021年 1-34页

作者： Tavana, Madjid Govindan, Kannan Nasr, Arash Khalili Heidary, Mohammad Saeed Mina, Hassan La Salle Univ Business Syst & Analyt Dept Distinguished Chair Business Analyt Philadelphia PA 19141 USA Univ Paderborn Fac Business Adm & Econ Business Informat Syst Dept D-33098 Paderborn Germany Univ Southern Denmark Dept Technol & Innovat Odense Denmark Sharif Univ Technol Grad Sch Management & Econ Tehran Iran Allameh Tabatabai Univ Fac Management & Accounting Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

Developing countries scramble to contain and mitigate the spread of coronavirus disease 2019 (COVID-19), and world leaders demand equitable distribution of vaccines to trigger economic recovery. Although numerous strategies, including education, quarantine, and immunization, have been used to control COVID-19, the best method to curb this disease is vaccination. Due to the high demand for COVID 19 vaccine, developing countries must carefully identify and prioritize vulnerable populations and rationalize the vaccine allocation process. This study presents a mixed-integer linear programming model for equitable COVID-19 vaccine distribution in developing countries. Vaccines are grouped into cold, very cold, and ultra-cold categories where specific refrigeration is required for their storage and distribution. The possibility of storage for future periods, facing a shortage, budgetary considerations, manufacturer selection, order allocation, time-dependent capacities, and grouping of the heterogeneous population are among the practical assumptions in the proposed approach. Real-world data is used to demonstrate the efficiency and effectiveness of the mathematical programming approach proposed in this study.

关键词： Vaccine supply chain Coronavirus vaccine Equitable distribution Location-inventory problem mixed-integer linear programming model COVID-19

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Optimization of carbon emission in an integrated machine-piece scheduling and vehicle routing problem and its solution using MOPSO and NSGAII metaheuristic algorithms

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SCIENTIFIC REPORTS 2024年第1期14卷 1-30页

作者： Heidari, Ali Sheikh-Azadi, Amir-Hosein Hasan-Zadeh, Atefeh Kazemzadeh, Yousef Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Univ Tehran Coll Engn Fouman Fac Engn POB *** Fouman Iran Persian Gulf Univ PGU Dept Petr Engn Bushehr Iran

The growth of the global economy is accompanied by significant energy consumption, and greenhouse gas emissions create various problems such as global warming and environmental degradation. To protect the environment, governments are seeking to reduce carbon emissions. Production systems that operate solely based on economic factors in the workshop only consider problems such as production speed, cost, and processing time. Two aspects can be effective in saving energy and reducing emissions at the production planning level: using routing to find the shortest path for collecting workpieces to the workshop, and turning off machines with long idle times and restarting them at the appropriate time. If the workshop production problem is combined with vehicle routing, a new problem arises. According to the research conducted so far, an integrated mathematical model for production routing has not been designed in a situation where the routing is before the production workshop. In this research, this bi-objective model is introduced, and it is solved using the augmented epsilon-constraint (AEC) method. The proposed mixed-integer linear programming model of this research includes three dimensions: environmental, social (customer satisfaction), and economic simultaneously. Given the high complexity of the mathematical model, MATLAB software and MOPSO and NSGA-II algorithms were used to solve it at higher dimensions. Seven evaluation criteria were used to compare the two proposed algorithms, and the results show that the MOPSO algorithm performs better. The findings suggest that minimizing pollution may involve sacrificing on-time delivery to customers. Consequently, decision-makers must carefully weigh the trade-off between reducing environmental impact and maintaining satisfactory delivery performance, ultimately deciding on an acceptable pollution level.

关键词： Optimization Vehicle Routing Carbon and Energy mixed-integer linear programming model Metaheuristic Algorithms

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Sustainable supply chain of distributed multi-product gas fields based on skid-mounted equipment to dynamically respond to upstream and market fluctuations

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ENERGY 2024年 292卷

作者： Hong, Bingyuan Du, Zhaonan Qiao, Dan Liu, Daiwei Li, Yu Sun, Xiaoqing Gong, Jing Zhang, Hongyu Li, Xiaoping Zhejiang Ocean Univ Natl & Local Joint Engn Res Ctr Harbor Oil & Gas S Sch Petrochem Engn & Environm Zhejiang Key Lab Petrochem Environm Pollut Control Zhoushan 316022 Peoples R China PipeChina Oil & Gas Control Ctr Dongtucheng Rd Beijing 100013 Peoples R China PipeChina West East Pipeline Co Shanghai 200120 Peoples R China China Natl Oil & Gas Explorat & Dev Corp Beijing 100034 Peoples R China China Univ Petr Natl Engn Lab Pipeline Safety MOE Key Lab Petr Engn Beijing Key Lab Urban Oil & Gas Distribut Technol Fuxue Rd 18 Beijing 102249 Peoples R China

Efficient operation of the gas field supply chain is an important guarantee for oil and gas energy security, and it needs to dynamically adapt to upstream and market fluctuations. This paper proposes an innovative design and operation optimization mixed integer nonlinear programming (MILP) method for distributed supply chain based on skid equipment. Unlike the traditional methods, the proposed MILP method can simultaneously obtain upstream production planning, midstream modular equipment and processing capacity allocation, and downstream transportation allocation schemes for various natural gas products such as liquefied natural gas (LNG), compressed natural gas (CNG) and pipeline natural gas (PNG) of each time periods by making the maximum net present value (NPV) of the full development cycle as the target. In order to prove the superiority and usability of the proposed method, four operation scheduling modes are compared through two comprehensive case analysis. Additionally, the effects of gas well productivity, market demand and product price are investigated through sensitivity analysis. The results show that comparing with the traditional method, the proposed method can effectively improve the loading rate of the processing equipment, increase the overall revenue of gas field development, integrate the operation in the upstream, midstream and downstream of the supply chain, dynamically adapt to the gas production and marketing fluctuations. This study provides a creative way to obtain better profit, reduce energy utilization and promote cleaner production for sustainable supplies management in gas industry.

关键词： Gas field supply chain mixed-integer linear programming model Production planning Modular infrastructure Operation optimization

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Coordinated G&TEP and carbon capture and storage expansion planning model for emission constrained power systems

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第26期14卷 6650-6662页

作者： Asgharian, Vahid Abdelaziz, Morad Kamwa, Innocent Univ British Columbia Fac Appl Sci Sch Engn Kelowna BC Canada Hydroquebec IREQ Power Syst & Math Varennes PQ Canada

Fossil fuel-fired power plants are still the principal power producers in most power systems. Retrofitting these pollutant generators with carbon capture and storage (CCS) technology can be a key solution to decarbonisation, especially for power systems with low expansion potential for renewable and hydroelectric energy resources. This study presents a coordinated generation and transmission expansion planning (G&TEP) and CCS expansion planning model for carbon emission constrained power systems. The proposed model determines the optimal order and time of retrofitting carbon emitter generators with CCS technology coordinated with the G&TEP. The limits on renewable resources capacity expansion potential and the yearly emission reduction targets are considered. Additionally, the proposed model allows for determining the incentives that are to be offered by the central planning authority to the pollutant generators to incentivise their participation in emission reduction through CCS retrofitting. The problem is formulated as a mixed-integer linear programming model and is decomposed into a master and three subproblems to tackle the large-scale nature of the developed optimisation problem. Numerical results demonstrate that a coordinated G&TEP and CCS expansion planning is a least-cost planning solution for emission constrained power systems with low expansion capacity potential for renewable and hydroelectric resources.

关键词： maintenance engineering power generation planning carbon capture and storage air pollution control integer programming power generation economics power transmission planning coal-fired power stations hydroelectric power stations CCS retrofitting mixed-integer linear programming model coordinated G and TEP CCS expansion planning model least-cost planning solution carbon emission constrained power systems low expansion capacity potential carbon capture and storage expansion planning model fossil fuel-fired power plants pollutant generators low expansion potential renewable energy resources hydroelectric energy resources coordinated generation retrofitting carbon emitter generation units CCS technology renewable resources capacity expansion potential emission reduction targets central planning authority transmission expansion planning optimal order optimisation problem

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Two-step method to reduce metro transit energy consumption by optimising speed profile and timetable

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第9期14卷 1097-1107页

作者： Jin, Bo Sun, Pengfei Wang, Qingyuan Feng, Xiaoyun Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

With the rising energy prices and increasing environmental awareness, the energy efficiency of metro transit system has attracted much attention in recent years. This study proposes a two-step optimisation method to optimise speed profile and timetable, aiming to reduce the operational energy consumption of metro transit system. First, a coasting point searching algorithm is designed to reduce tractive energy consumption by optimising speed profile and running time distribution scheme. Then, a mixed-integer linear programming model is constructed to maximise the overlap time between the accelerating and braking phases by optimising headway and dwell time, in order to improve the utilisation of regenerative braking energy (RBE). Furthermore, numerical simulations are presented based on the data from a Guangzhou Metro Line. The results show that the tractive energy consumption can be reduced by 8.46% and the utilisation of RBE can be improved by 11.6%.

关键词： energy conservation energy consumption railways optimisation linear programming railway engineering integer programming regenerative braking search problems speed profile timetable operational energy consumption metro transit system coasting point searching algorithm tractive energy consumption running time distribution scheme mixed-integer linear programming model overlap time regenerative braking energy Guangzhou Metro Line step method metro transit energy consumption rising energy prices increasing environmental awareness energy efficiency two-step optimisation method

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Rule-based operation task-aware energy management for ship power systems

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第25期14卷 6348-6358页

作者： Hein, Kyaw Xu, Yan Senthilkumar, Yuvarekha Gary, Wilson Gupta, Amit Kumar Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Nanyang Technol Univ Rolls Royce NTU Corp Lab Singapore Singapore Rolls Royce Elect Rolls Royce Singapore

This study proposes an operation task-aware energy management strategy for ship power systems that consist of main engines, diesel-electric engines, and energy storage systems. The proposed strategy aims to meet the fuel consumption and task-dependent objectives of the vessel by optimally dispatching the generation and storage units. Firstly, rule-based decisions are made based on the operational task requirements and specifications by the classification societies. Then, in the optimisation stage, these decision variables are used as inputs to formulate and update the operational constraints and the objectives of the optimisation. The optimisation problem is formulated as a mixed-integer linear programming model, and with the help of rule-based decision variables, the problem can be efficiently solved by the exhaustive search algorithm. Four case studies with different operation task sequences and ship topologies are performed to demonstrate the effectiveness of the dispatching scheme. Furthermore, the results indicate the simplicity and practicality for the actual implementation, as well as flexibility and applicability of the proposed optimal energy dispatch for different types of vessels.

关键词： energy management systems integer programming ships linear programming power generation dispatch optimisation energy storage rule-based operation task-aware energy management ship power systems operation task-aware energy management strategy main engines diesel-electric engines energy storage systems task-dependent objectives storage units rule-based decisions operational task requirements optimisation stage operational constraints optimisation problem mixed-integer linear programming model rule-based decision variables different operation task sequences ship topologies optimal energy dispatch

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Strategic wind power investment in competitive electricity markets considering the possibility of participation in intraday market

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第14期14卷 2676-2686页

作者： Mousavi, Seyed Milad Barforoshi, Taghi Babol Noshirvani Univ Technol Dept Elect & Comp Engn Babol Sar Iran

Wind power application in producing electrical energy is an integral part of the increased eco-friendly generation. Accordingly, wind power producers may have a dominant position in some electricity markets. This study mainly concerns a strategic wind power investor who owns a number of units and seeks to optimise its expansion plans on sizes and sites of new generation capacities in a horizon year. Here, the investor is a price-maker in both day-ahead (DA) and intraday (ID) markets, while acts as a deviator in the balancing market. A stochastic bi-level model is proposed wherein the upper-level, the investor maximises the expected profit. Both DA and ID market clearings are considered in the lower-level (LL). The model is formulated as a mathematical program with equilibrium constraints (MPEC) by replacing the LL problem with its Karush-Kuhn-Tucker (KKT) conditions. The MPEC model is then converted to a mixed-integer linear programming model. A three-bus illustrative example and IEEE 24-bus reliability test system are used to demonstrate the effectiveness of the proposed model. The results confirm that the presence of the ID market in the investment model has increased the expected profits of the investor and also increases invested wind capacity.

关键词： wind power investment power markets integer programming mathematical programming wind power plants pricing power generation economics linear programming strategic wind power investment competitive electricity markets intraday market wind power application electrical energy increased eco-friendly generation wind power producers dominant position strategic wind power investor expansion plans generation capacities horizon year price-maker balancing market stochastic bi-level model upper-level expected profit lower-level Karush-Kuhn-Tucker conditions MPEC model mixed-integer linear programming model IEEE 24-bus reliability test system ID market investment model wind capacity

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Warm-start piecewise linear approximation-based solution for load pick-up problem in electrical distribution system

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IET SMART GRID 2020年第3期3卷 385-393页

作者： Yun, Jingyang Hu, Weidong Zhou, Yun Yan, Zheng Feng, Donghan Li, Naihu Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China Minist Educ Key Lab Control Power Transmiss & Convers SJTU Shanghai Peoples R China Chint Grp Shanghai Peoples R China

As the core sub-problem of both network reconfiguration and service restoration of the electrical distribution system (EDS), the load pick-up (LPP) problem in EDS searches the optimal configuration of the EDS, aiming to minimise the power loss or provide as much power as possible to load ends. The piecewise linearisation (PWL) approximation method can be used to tackle the network power flow constraints' non-linearity in the LPP problem model, and transform the LPP model into a mixed-integer linear programming model (LPP-MILP model). The errors in the PWL approximation of the network power flow constraints may affect the feasibility of the solving results of the LPP-MILP model. The single method to reduce the PWL approximation errors by increasing the number of discretisations in PWL function is not stable. Moreover, the solution efficiency of the LPP-MILP model is sacrificed severely. In this study, a warm-start PWL approximation-based solution for the LPP problem is proposed. The variable upper bounds in the PWL approximation functions are renewed dynamically in the warm-start solution procedure to reduce the PWL approximation errors with higher computational efficiency. Modified IEEE 33-bus radial distribution test system and a real and large distribution system, 1066-bus system, are used to test and verify the effectiveness and robustness of the proposed methodology.

关键词： linear programming load flow integer programming piecewise linear techniques approximation theory distributed power generation power loss piecewise linearisation approximation method network power flow constraints LPP problem model LPP model mixed-integer linear programming model LPP-MILP model PWL approximation errors PWL function PWL approximation-based solution PWL approximation functions radial distribution test system warm-start piecewise linear approximation-based solution load pick-up problem electrical distribution system core sub-problem network reconfiguration service restoration EDS

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Optimizing Prices and Periods in Time-of-use Electricity Tariff Design Using Bilevel programming 1

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20th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP)

作者： Alves, Maria Joao Antunes, Carlos Henggeler Soares, Ines Univ Coimbra CeBER INESC Coimbra Coimbra Portugal Univ Coimbra Fac Econ INESC Coimbra Coimbra Portugal Univ Coimbra Dept Elect Engn & Comp INESC Coimbra Coimbra Portugal INESC Coimbra Coimbra Portugal

ISBN: (数字)9783030436803

ISBN: (纸本)9783030436797;9783030436803

In this paper, a comparison is made between two bilevel programming models to design time-of-use tariffs in the electricity retail market. The upper-level objective function consists of the maximization of the retailer's profit and the lower-level problem relates to the minimization of the consumer's cost. In the first model, the periods in which prices apply are pre-defined and the aim is to determine the price values. In the second model, which is developed for the first time in this paper, both the periods and prices are decision variables, thus leading to a very large search space for the upper-level problem due to the number of combinations periods-prices. For the model with variable periods, a hybrid approach combining a genetic algorithm for the upper-level search with a mixed-integer linear programming solver to obtain optimal solutions to the lower-level problem is herein developed. Computational results comparing the two models are presented.

关键词： Bilevel optimization Genetic algorithm mixed-integer linear programming model Time-of-use pricing Electricity retail market Demand response

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Resilience-directional robust power dispatching of microgrids under meteorological disasters

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IET RENEWABLE POWER GENERATION 2019年第12期13卷 2084-2093页

作者： Qiu, Haifeng Gu, Wei Wu, Zhi Zhou, Suyang Pan, Guangsheng Yang, Xiaomei Yuan, Xiaodong Ding, Xiaohua Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China Jiangsu Key Lab Smart Grid Technol & Equipment Nanjing 210096 Jiangsu Peoples R China State Grid Jiangsu Elect Power Co Nanjing 210024 Jiangsu Peoples R China NARI Grp Corp State Grid Elect Power Res Inst Nanjing 211106 Jiangsu Peoples R China

This study proposes a novel resilience-directional robust dispatch (RRD) model for an islanded AC/DC hybrid microgrid (HMG). The inherent uncertainties on the source-load power and the occurrence of meteorological disasters are considered in this model. When a meteorological disaster strikes, the wind turbine (WT), photovoltaic (PV), and bidirectional converter of the HMG should be offline to ensure the stability of the HMG and the safety of these sensitive units. When affected by such double uncertainties, the output constraints of the WT, PV and load are bilinear but are linearised via big-M approach. The proposed RRD model manifests as a min-max-min tri-layer problem with mixed-integer recourse variables, which is difficult to solve directly. Therefore, a nested column-and-constraint generation algorithm is adopted to convert the tri-layer problem to a two-stage mixed-integer linear programming (MILP) model. The MILP problem is addressed by the commercial solver, thereby obtaining the minimal operating cost and establishing robust scheduling plans with the worst disaster scenario. The effectiveness and rationality of the proposed RRD model and its solution methodology are verified in numerical tests.

关键词： scheduling distributed power generation disasters integer programming stochastic programming linear programming minimisation meteorological disasters novel resilience-directional robust dispatch model islanded AC DC hybrid microgrid HMG inherent uncertainties source-load power meteorological disaster strikes wind turbine PV bidirectional converter double uncertainties output constraints RRD model manifests min-max-min tri-layer problem mixed-integer recourse variables nested column -constraint generation algorithm mixed-integer linear programming model MILP problem minimal operating cost establishing robust scheduling plans worst disaster scenario

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