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scenario-based stochastic programming for an airline-driven flight rescheduling problem under ground delay programs

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2021年 150卷 102360-102360页

作者： Woo, Young-Bin Moon, Ilkyeong Seoul Natl Univ Dept Ind Engn Seoul South Korea Seoul Natl Univ Inst Ind Syst Innovat Seoul South Korea

We address an airline-driven flight rescheduling problem within a single airport in which a series of ground delay programs (GDPs) are considered. The objective of the problem is to minimize an airline's total relevant cost (TRC) consisting of delay costs, misconnection costs, and cancellation costs that would result from flight rescheduling. We introduce three solution approaches-the greedy approach, the stochastic approach, and the min-max approach-that revise the daily flight scheduling whenever the schedule is affected by a GDP or further GDP changes. The greedy approach simply searches for a solution using currently updated static GDP information, and the other two approaches provide a solution by considering possible scenarios for changes of the GDP. Using real-world data in existing literature and some generated scenarios, we present extensive computational results to assess the performance of the approaches. We also report the values of information on GDP the solution approaches refer to. Deliberating various cost parameter settings an airline might consider, we discuss the value of information in implementing the proposed solution approaches.

关键词： Ground delay programs scenario-based stochastic programming Airline operations Flight rescheduling

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Sustainable project selection and scheduling using scenario-based stochastic programming: a case study of industrial projects

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stochastic ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2024年第2期38卷 593-619页

作者： Rahimi, Fatemeh Davari-Ardakani, Hamed Ameli, Mariam Beheshtkhah, Mahdi Kabiri Kharazmi Univ Fac Engn Dept Ind Engn Tehran Iran Kharazmi Univ Dept Math Tehran Iran

Due to a wide variety of real-world constraints, proper project portfolio selection is a critical issue for project-oriented organizations. In this paper, a bi-objective stochastic mixed-integer linear programming model is developed to cope with the project selection and scheduling problem in the presence of greenhouse gas emissions, and non-hazardous/hazardous wastes regulatory restrictions. Moreover, reinvesting proceeds of projects as well as loans are allowed to finance projects over the planning horizon. The proposed model maximizes the net present value of the expected project portfolio's terminal wealth under uncertain conditions, as well as the sustainability score of the project portfolio, simultaneously. The sustainability score is calculated by one of the recent multi-criteria decision-making methods, SECA, based on seven qualitative sustainability indicators and by solving a non-linear optimization model. To assess the performance of the proposed model, a case study of eighteen industrial projects is applied. Since the duration of industrial projects is usually uncertain, the proposed model is reformulated as a scenario-based stochastic programming model. Furthermore, the CPLEX solver and Branch and Benders algorithm are used to solve the problem. Results show that the Branch and Benders algorithm is much more efficient than the CPLEX solver. Results show that increasing the carbon and landfill tax rates is not always an appropriate decision made by policymakers to control various types of emissions. Such decisions may not only make the projects less attractive for investment but also, do not significantly reduce the negative environmental effects, which decreases sustainability in both economic and environmental dimensions. This highlights the importance of considering each problem's attitudes for setting regulations where copying does not always create the same solutions for sustainability issues.

关键词： Resource-Constrained Project Selection and Scheduling Problem SECA approach Greenhouse gas emissions Carbon tax Landfill tax scenario-based stochastic programming Branch and Benders algorithm Project finance Sustainable development goals (SDGs) Optimization

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An efficient scenario-based stochastic programming for optimal planning of combined heat, power, and hydrogen production of molten carbonate fuel cell power plants

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ENERGY 2015年 83卷 734-748页

作者： Bornapour, Mosayeb Hooshmand, Rahmat-Allah Univ Isfahan Dept Elect Engn Esfahan Iran

In this paper, a stochastic model is proposed for planning the location and operation of Molten Carbonate Fuel Cell Power Plants (MCFCPPs) in distribution networks when used for Combined Heat, Power, and Hydrogen (CHPH) simultaneously. Uncertainties of electrical and thermal loads forecasting;the pressures of hydrogen, oxygen, and carbon dioxide imported to MCFCPPs;and the nominal temperature of MCFCPPs are considered using a scenario-based method. In the method, scenarios are generated using Roulette Wheel Mechanism (RWM) based on Probability Distribution Functions (PDF) of input random variables. Using this method, probabilistic specifics of the problem are distributed and the problem is converted to a deterministic one. The type of the objective functions, placement, and operation of MCFCPPs as CHPH change this problem to a mixed integer nonlinear one. So, multi-objective Modified Firefly Algorithm (MFA) and Pareto optimal method are employed for solving the multi-objective problem and for compromising between the objective functions. During the simulation process, a set of non-dominated solutions are stored in a repository. The 69-bus distribution system is used for evaluating the proper function of the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Molten carbonate fuel cell power plants Combined heat Power and hydrogen Modified firefly algorithm Pareto optimal set scenario-based stochastic programming

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A multi-objective robust scenario-based stochastic chance constrained programming model for sustainable closed-loop agri-food supply chain

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COMPUTERS & CHEMICAL ENGINEERING 2025年 194卷

作者： Rahbari, Misagh Khamseh, Alireza Arshadi Mohammadi, Mohammad Kharazmi Univ Fac Engn Dept Ind Engn Tehran Iran

The agri-food supply chain management plays a crucial role in ensuring the interests of supply chain components and food security in society. Additionally, due to the nature of agri-food products, sustainability dimensions have always been of concern to organizations engaged in this field. The importance of the timely and quality provision of agri-food products has doubled after the global crisis. Therefore, this study focuses on optimizing and analyzing the sustainable multi-objective closed-loop supply chain network for agri-food products, with a case study on the canned food under uncertainty. Strategic and operational decisions and other features are considered to achieve more accurate results. To address the various dimensions of sustainability, the problem is considered as a four-objective one, aiming to maximize the use of available production throughput for factories, maximize job opportunities created, minimize supply chain costs, and ultimately minimize unmet demands. The carbon cap and trade mechanism is used to control greenhouse gas emissions in the supply chain network. A robust scenario-based stochastic chance constrained programming approach is employed to deal with the uncertainty, and also validation is performed using various criteria. Moreover, an augmented epsilon-constraint optimization approach is used to solve the multi-objective problem and achieve Pareto optimal solutions. Finally, sensitivity analysis is employed to prepare for potential changes in some problem parameters.

关键词： Closed-loop supply chain scenario-based stochastic programming Agri-food supply chain Chance constrained programming Sustainability Robust optimization

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Low-carbon economic dispatch of iron and steel industry empowered by wind-hydrogen energy: Modeling and stochastic programming

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APPLIED ENERGY 2025年 387卷

作者： Wu, Haotian Ke, Deping Xu, Jian Song, Lin Liao, Siyang Zhang, Pengcheng Wuhan Univ Sch Elect Engn Wuhan 430072 Peoples R China

The advancement of iron and steel production techniques is facilitating the transition of the iron and steel industry (ISI) from coal as the primary energy source to renewable alternatives such as wind and hydrogen. This also implies that the traditional scheduling method of the ISI, which considers only a single form of energy, requires immediate upgrading. To address this issue, this paper proposes a low-carbon stochastic economic dispatch model that considers the multi-energy coupled ISI. The implementation of a resource task network, which defines discrete steel production, permits the incorporation of gas-based ironmaking and stochastic wind-hydrogen scenarios into an extended resource task network (ERTN). This ERTN ultimately provides a mathematical representation of the overall operation of the ISI. Additionally, a carbon trading model for the ISI based on the actual carbon policies in southern China is constructed to provide additional guidance on the energy use of the ISI. To overcome the computational challenges posed by the considerable number of binary variables and scenarios inherent to the ERTN, a Lagrangian Benders decomposition algorithm (LBDA) has been developed. This approach entails decomposing the original model into a master problem and multiple subproblems, thereby facilitating more efficient optimization. The simulation results demonstrate that the proposed model is capable of rationally arranging iron and steel production and optimizing the energy utility to maximize the overall economy of ISI, and the LBDA is able to guarantee optimality while significantly enhancing the solution efficiency.

关键词： Low-carbon iron and steel production Hydrogen energy Extended resource task network Lagrangian benders decomposition scenario-based stochastic programming

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Optimal Bidding Strategy for Demand Response Aggregator in Day-Ahead Markets via stochastic programming and Robust Optimization 12

Optimal Bidding Strategy for Demand Response Aggregator in D...

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12th International Conference on the European Energy Market (EEM)

作者： Wei, Ming Zhong, Jin Univ Hong Kong Dept Elect & Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781467366922

This paper evaluates the optimal bidding strategy for demand response (DR) aggregator in day-ahead (DA) markets. Because of constraint of minimum power quantity requirement, small-sized customers have to become indirect participants of electricity markets via the DR aggregator, who could offer various contracts accessing customers' demand reduction capacity in advance. In day-ahead markets, DR aggregator schedules those contracts and submits accumulated DR offers to the system operator. The objective is to maximize the profit of the DR aggregator. The key element affecting the bidding decision and aggregator's profit is the uncertain hourly DA prices. The stochastic programming adopts scenario-based approach for helping the profit-seeking DR aggregator control uncertainties. Robust optimization employs forecast values with bounded price intervals to address uncertainties while adjusting the robustness of the solution flexibly. Both scenarios can be modelled as mixed-integer linear programming (MILP) problems which could be solved by available solvers.

关键词： Day-ahead markets demand response aggregator price uncertainties robust optimization scenario-based stochastic programming

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A multi-objective stochastic model for a reverse logistics supply chain design with environmental considerations

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第7期12卷 8017-8040页

作者： Moslehi, M. Saleh Sahebi, Hadi Teymouri, Ashkan Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

Some electronic devices have a short lifetime, and variety-seeking and consumerism are increasingly growing in today's societies. Moreover, electronic wastes contain precious substances such as gold, silver, copper, and aluminum. The proper disposal and processing of them by recycling offer considerable advantages to the environment, given the hazardous natures of such devices' substances. The proposed reverse logistics with waste electrical and electronic equipment (WEEE) is an important task considered by researchers, the use of which offers economic benefits and reduces the environmental impacts of wastes. The present study models the electrical and electronic equipment (EEE) reverse logistics process as a bi-objective mixed-integer programming model under uncertainties. The mathematical model investigates two objectives: an economic objective and an environmental objective. The first is minimizing cost, while the second is maximizing the environmental score by reverse logistics processes in recovering and recycling. The parameters of demand and WEEE return rate which is obtained from the customer were considered as two uncertain parameters. A scenario-based stochastic programming (SSP) approach is applied to deal with the uncertainties. A case study of an electronic equipment manufacturer in Esfahan, Iran was included. The model was solved by a nominal approach and an SSP approach via the epsilon-constraint (EC) and augmented epsilon-constraint (AEC) methods to obtain optimal Pareto solutions and compare the methods. Finally, the optimal results of the two approaches were evaluated. The results indicated that the SSP approach using the AEC method had better outcomes.

关键词： Reverse logistics Waste electrical and electronic equipment scenario-based stochastic programming Augmented epsilon-constraint method

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Model predictive control to two-stage stochastic dynamic economic dispatch problem

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CONTROL ENGINEERING PRACTICE 2017年 69卷 112-121页

作者： Alqurashi, Amru Etemadi, Amir H. Khodaei, Amin George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA Univ Denver Dept Elect & Comp Engn Denver CO 80210 USA

A multi-stage model predictive control approach is proposed to compensate the forecast error in a scenario based two-stage stochastic dynamic economic dispatch problem through a feedback mechanism. Reformulating the problem as a finite moving-horizon optimal control problem, the proposed approach decelerates the growth of the number of scenarios by updating the system as uncertainties are gradually realized. Consequently, the computation time is reduced, and the problem is solved without the need for using scenario reduction techniques that compromise the accuracy of the solution. To exhibit the computational efficiency of the proposed approach, numerical experiments are conducted on the IEEE 118-bus system. (C) 2017 Published by Elsevier Ltd.

关键词： Dynamic economic dispatch stochastic model predictive control scenario-based stochastic programming Wind Demand uncertainty

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A Solution Compatible with Cost-Reliability for Multi-Stage Feeder Routing Problem with Considering Uncertainties

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IETE JOURNAL OF RESEARCH 2019年第4期65卷 435-445页

作者： Taghizadegan, Navid Ravadanegh, Sajad Najafi Karimi, Masoumeh Oskuee, Mohammad Reza Jannati Azarbaijan Shahid Madani Univ Elect Engn Dept Smart Distribut Grid Res Lab Tabriz Iran Islamic Azad Univ Tabriz Branch Young Researchers & Elite Club Tabriz Iran

This paper proposes a stochastic multistage expansion planning method in order to solve the mid-term and long-term optimal feeder routing problem. Pseudo dynamic behaviour of the network parameters and geographical constraints besides the associated uncertainties of future load demand and market price are incorporated into the method. The proposed method is solved from the DisCo viewpoints using particle swarm optimization algorithm which finally converges to a solution with minimum costs. The proposed cost function is the sum of feeder's installation costs, power losses cost, cost of active purchased power from power market, and reliability costs. Meanwhile, the final solution must satisfy all the operation aspects of power system in acceptable levels. On the other hand, the implementation of the final strategy obtained from the proposed method of this paper, can increase the responsibility of the power system in lower costs. In this regard, distribution system can power its customers with higher power quality in acceptable reliability level and also in lower costs. The proposed method is applied on a large-scale distribution network and its practicability and also its effectiveness are analysed due to the simulation results.

关键词： Load demand forecast Market price errors Multistage planning Optimal feeder routing (OFR) PSO algorithm scenario-based stochastic programming Uncertainty modelling

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Reliability-Oriented Reconfiguration of Power Distribution Systems Considering Load and RES Production scenarios

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IEEE TRANSACTIONS ON POWER DELIVERY 2022年第6期37卷 4668-4678页

作者： Karafotis, Panagiotis Evangelopoulos, Vasileios Georgilakis, Pavlos Natl Tech Univ Athens Sch Elect & Comp Engn Athens 15780 Greece

The ever-increasing integration of non-dispatchable distributed generation, i.e., renewable energy sources (RES), arises new challenges in the field of power system's reliability. Distribution network reconfiguration (DNR) is a cost-effective approach for the distribution system operator (DSO) that wishes to enhance system's reliability without infrastructure upgrades. This paper introduces a novel path-basedmixed-integer second-order cone programming model to optimally solve the reliability-oriented DNR problem. The DSO's objectives that are optimized are: a) improvement of distribution system's reliability indices and b) minimization of power losses. The proposed model is enriched with a scenario-based stochastic programming formulation that considers multiple levels of load and RES production. The standard 33-nodes distribution system and a real-world 83-nodes distribution system are employed to prove the efficiency and applicability of the model. Firstly, the multi-objective nature of the reliability-oriented DNR problem is investigated by conducting a sensitivity analysis, which reveals a trade-off region between reliability indices and power losses. Moreover, the obtained results show different global optimal solutions when the variability of load and RES production is considered. This highlights the importance of considering a scenario-based approach for load and RES production when solving the reliability-oriented DNR problem.

关键词： Distribution network reconfiguration mixed-integer second-order cone programming scenario-based stochastic programming reliability improvement

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