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A stochastic multi-objective optimization decision model for energy facility allocation: a case of liquefied petroleum gas station

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CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2020年第2期22卷 389-398页

作者： Yang, Ming-Der Chen, Yi-Ping Wang, Chien-Tsung Deng, Ming-Jay Lin, Yu-Hao Chen, Ho-Wen Natl Chung Hsing Univ Innovat & Dev Ctr Sustainable Agr Dept Civil Engn 145 Xingda Rd Taichung 402 Taiwan Pervas AI Res PAIR Labs 1001 Univ Rd Hsinchu 300 Taiwan Da Yeh Univ Dept Business Adm 168 Univ Rd Dacun 515 Changhua Taiwan Natl Yunlin Univ Sci & Technol Dept Chem & Mat Engn 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Natl Yunlin Univ Sci & Technol Bachelor Program Interdisciplinary Studies 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Tunghai Univ Dept Environm Sci & Engn 181 Sec 3Taichung Port Rd Taichung 407 Taiwan

To mitigate air pollution problem, the government has been planning to build more liquefied petroleum gas stations to motivate drivers to use liquefied petroleum gas vehicles in Taiwan. Such facility allocation problem is a multi-objective optimization process considering spatial variation in the need of refueling. This study presents a stochastic multi-objective optimization model for liquefied petroleum gas station allocation (SMOMLSA) that integrates a nondominated sorting genetic algorithm II with a Monte Carlo simulation to optimally allocate liquefied petroleum gas stations according to three trade-off objectives, including investment performance, energy conversion, and business opportunity. Monte Carlo simulation procedure generates the starting location of a taxicab car in need of refueling in the spatial grid based on a probability distribution. Nondominated sorting genetic algorithm II resolves the station location problem with these multi-objectives. The SMOMLSA was validated by conducting a real-world case study. Result depicts that the SMOMLSA can provide information on the optimal allocation of liquefied petroleum gas stations for minimizing construction costs, minimizing average refueling distance for vehicles, and maximizing potential customers. [GRAPHICS] .

关键词： Monte Carlo simulation (MCS) stochastic multi-objective optimization Nondominated sorting genetic algorithm II Alternative energy

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stochastic multi-objective modelling and optimization of an energy-conscious distributed permutation flow shop scheduling problem with the total tardiness constraint

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JOURNAL OF CLEANER PRODUCTION 2019年 226卷 515-525页

作者： Fu, Yaping Tian, Guangdong Fathollahi-Fard, Amir Mohammad Ahmadi, Abbas Zhang, Chaoyong Qingdao Univ Sch Business Qingdao 266071 Peoples R China Shandong Univ Sch Mech Engn Jinan 250061 Shandong Peoples R China Shandong Univ Minist Educ Key Lab High Efficiency & Clean Mech Manufacture Jinan 250061 Shandong Peoples R China Amirkabir Univ Technol Dept Ind Engn & Management Syst Hafez Ave 424 Tehran Iran Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

Recent years have seen a great deal of attention in energy conservation for production and manufacturing activities, particularly for energy-intensive industries. One of the useful strategies in reducing unnecessary energy consumption is to schedule these activities by considering both energy driven and time-oriented criteria. This scheduling model can make an interaction between the energy consumption and the production cost to realize an efficient and sustainable production process. In this regard, the customers' expectation for due date is another important factor for decision-makers to control the delay in delivery. Making these decisions is extremely difficult due to uncertain circumstances to extract the accurate information of facilities and jobs in advance. Aforementioned issues in the context of urgent need for energy-conservation as well as the advent of globalized and multi-factory manufacture motivate our attempts to address a stochastic multi-objective distributed permutation flow shop scheduling problem by considering total tardiness constraint via minimizing the makespan and the total energy consumption. Due to the uncertainty of the proposed problem, a chance-constrain approach is used to describe decision-makers' awareness for the total tardiness, and accordingly, a chance-constrained programming model is utilized to formulate this problem. As a complicated optimization problem, a new multi-objective brain storm optimization algorithm incorporating stochastic simulation approach is specifically designed to better solve problem. A comparative study based on a set of benchmark test problems as well as two classical and popular algorithms is provided. The experimental results demonstrate that the proposed algorithm shows a very competitive performance in dealing with the investigated problem. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Distributed permutation flow shop scheduling stochastic multi-objective optimization Energy consumption Brain storm optimization stochastic simulation

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stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling via fruit fly optimization algorithm

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JOURNAL OF CLEANER PRODUCTION 2021年 278卷 123364-123364页

作者： Fu, Yaping Zhou, MengChu Guo, Xiwang Qi, Liang Qingdao Univ Sch Business Qingdao 266071 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China Liaoning Shihua Univ Comp & Commun Engn Coll Fushun 113001 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

Remanufacturing end-of-life (EOL) products is an important approach to yield great economic and environmental benefits. A remanufacturing process usually contains three shops, i.e., disassembly, processing and assembly shops. EOL products are dissembled into multiple components in a disassembly shop. Reusable components are reprocessed in a processing shop, and reassembled into their corresponding products in an assembly shop. To realize an overall optimization, we have to integrate them together when making decisions. In practice, a decision-maker usually has to optimize multiple criteria such as cost-related and service-oriented objectives. Additionally, we cannot accurately acquire the detail of EOL products due to their various usage processes. Therefore, multi-objective and uncertainty need to be considered simultaneously in an integrated disassembly-reprocessing-reassembly scheduling process. This work investigates a stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling problem to achieve the expected makespan and total tardiness minimization. To handle this problem, this work develops a multi-objective discrete fruit fly optimization algorithm incorporating a stochastic simulation approach. Its search techniques are designed according to this problem's features from five aspects, i.e., solution representation, heuristic decoding rules, smell-searching, vision-searching, and genetic-searching. Simulation experiments are conducted by adopting twenty-five instances to verify the performance of the proposed approach. Nondominated sorting genetic algorithm II, biobjective multi-start simulated annealing method, and hybrid multi-objective discrete artificial bee colony are chosen for comparisons. By analyzing the results with three performance metrics, we can find that the proposed approach performs better on all the twenty-five instances than its peers. Specifically, it outperforms them by 6.45%-9.82%, 6.91%-17.64% and 1.19%-2.76% in

关键词： Remanufacturing Integrated disassembly-reprocessing-reassembly scheduling stochastic multi-objective optimization Fruit fly optimization stochastic simulation

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Anticipatory stochastic multi-objective optimization for Uncertainty Handling in Portfolio Selection

Anticipatory Stochastic Multi-Objective Optimization for Unc...

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IEEE Congress on Evolutionary Computation

作者： Azevedo, Carlos R. B. Von Zuben, Fernando J. Univ Estadual Campinas Sch Elect & Comp Engn Sao Paulo Brazil

ISBN: (纸本)9781479904549;9781479904532

An anticipatory stochastic multi-objective model based on S-Metric maximization is proposed. The environment is assumed to be noisy and time-varying. This raises the question of how to incorporate anticipation in metaheuristics such that the Pareto optimal solutions can reflect the uncertainty about the subsequent environments. A principled anticipatory learning method for tracking the dynamics of the objective vectors is then proposed so that the estimated S-Metric contributions of each solution can integrate the underlying stochastic uncertainty. The proposal is assessed for minimum holding, cardinality constrained portfolio selection, using real-world stock data. Preliminary results suggest that, by taking into account the underlying uncertainty in the predictive knowledge provided by a Kalman filter, we were able to reduce the sum of squared errors prediction of the portfolios ex-post return and risk estimation in out-of-sample investment environments.

关键词： Anticipatory learning stochastic multi-objective optimization indicator-based search portfolio selection Kalman filter dynamic environments

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A stochastic decision-making framework for optimal multi-objective reservoir operation accounting for the tracking of uncertainty propagation and evolution from multiple sources

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JOURNAL OF HYDROLOGY 2025年 654卷

作者： Zhu, Feilin Zeng, Yurou Wang, Yaqin Han, Mingyu Liu, Weifeng Fan, Yukun Ben, Mengxue Zhong, Ping-an Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China Gen Inst Water Resources & Hydropower Planning & D Beijing 100120 Peoples R China

The operation of reservoirs involves real-time forecasting, decision-making, and operational processes, all of which are heavily influenced by interconnected uncertainties. These uncertainties pose significant challenges in formulating optimal decisions and introduce risks throughout the "Forecast-Operation-Decision-Making" (FODM) chain. To address multi-objective decision-making under uncertain conditions, a comprehensive modeling framework is proposed. This framework captures the propagation and evolution of uncertainties in the FODM chain through three interconnected sub-modules. Firstly, recognizing the dynamic nature of hydrological forecasting errors, we employ a robust martingale model to stochastically simulate runoff, providing crucial input conditions for reservoir operation. Secondly, a multi-objective stochastic optimization model is developed to account for uncertainties in forecasting, deriving the Pareto frontier under stochastic environments. A novel quantification method is introduced to address the dual uncertainties in performance values (PVs) and criterion weights (CWs). Additionally, a risk-based group decision-making model (MC-EDAS) is proposed, integrating Monte Carlo simulation to evaluate the distance from the average solution and address dual uncertainties. Two risk indicators are introduced to assess decision reliability. The framework also incorporates a two-stage decision-making process, facilitating the integration of preference information through iterative interactions between the decision group and the model. Numerical experiments using predefined risk indicators are conducted to analyze uncertainty propagation within the FODM chain. A case study in the Dadu River Basin, China, yields several key findings: (1) Forecasting errors transform non-inferior solutions on the Pareto frontier from discrete points into random variables with specific distributions, introducing uncertainty into PVs. (2) In the deterministic EDAS model, the top t

关键词： Reservoir operation stochastic multi-objective optimization Risk-based group decision-making Dual uncertainties Uncertainty propagation Decision reliability analysis

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multi-objective Sizing optimization Method of Microgrid Considering Cost and Carbon Emissions

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第4期60卷 5565-5576页

作者： Zhu, Xiang Ruan, Guangchun Geng, Hua Liu, Honghai Bai, Mingfei Peng, Chao Tsinghua Univ Dept Automat Beijing 10084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 10084 Peoples R China MIT Lab Informat & Decis Syst LIDS Boston MA 02139 USA North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

Microgrid serves as a promising solution to integrate and manage distributed renewable energy resources. In this paper, we establish a stochastic multi-objective sizing optimization (SMOS) model for microgrid planning, which fully captures the battery degradation characteristics and the total carbon emissions. The microgrid operator aims to simultaneously maximize the economic benefits and minimize carbon emissions, and the degradation of the battery energy storage system (BESS) is modeled as a nonlinear function of power throughput. A self-adaptive multi-objective genetic algorithm (SAMOGA) is proposed to solve the SMOSO model, and this algorithm is enhanced by pre-grouped hierarchical selection and self-adaptive probabilities of crossover and mutation. Several case studies are conducted to determine the microgrid size by analyzing Pareto frontiers, and the simulation results validate that the proposed method has superior performance over other algorithms on the solution quality of optimum and diversity.

关键词： Microgrids Batteries Degradation optimization stochastic processes Costs Genetic algorithms Distributed energy resource low carbon battery energy storage system renewable energy stochastic multi-objective optimization genetic algorithm

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A multi-objective stochastic programming model to configure a sustainable humanitarian logistics considering deprivation cost and patient severity

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ANNALS OF OPERATIONS RESEARCH 2022年第1期319卷 1265-1300页

作者： Jamali, Amir Ranjbar, Amirhossein Heydari, Jafar Nayeri, Sina Univ Tehran Sch Ind Engn Coll Engn Tehran Iran

As the occurrence of disasters has increased frequently and has resulted in growing concern about their adverse effects on the environment, Sustainable Humanitarian Logistics (SHL) has received great attention recently. SHL aims to reduce disaster damages in an environmentally-friendly manner in the shortest possible time. The terms including 'environmentally-friendly' and 'shortest possible time' refer to the environmental and social aspects of sustainability. This research proposes a stochastic multi-objective mixed-integer programming model to configure an SHL network during the response phase. Having compared to the research literature, this is the first study that considers economic, social, and environmental aspects of sustainability by incorporating relief cost, deprivation cost, and carbon emissions, respectively. Then, the improved multi-choice goal programming approach is applied to solve the proposed multi-objective model. To indicate the validity of the proposed model, an earthquake that occurred in a region of Kermanshah, Iran, in 2017 is investigated as a real case study. Finally, sensitivity analysis is performed and several managerial and theoretical insights are provided. The results show that exerting environmental issues in humanitarian logistics does not necessarily increase the relief costs, but can be in contrast with the social aspect. Furthermore, a minor increase in the budget of the preparedness phase drastically decreases the response costs.

关键词： Humanitarian logistics Deprivation cost Sustainable disaster management stochastic multi-objective optimization

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stochastic multi-objectives optimal scheduling of energy hubs with responsive demands in smart microgrids

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JOURNAL OF ENERGY STORAGE 2022年 55卷

作者： Abdulnasser, Ghada Ali, Abdelfatah Shaaban, Mostafa F. Mohamed, Essam E. M. South Valley Univ Dept Elect Engn Qena 83523 Egypt Amer Univ Sharjah Dept Elect Engn Sharjah 26666 U Arab Emirates

Energy hubs (EHs) are progressively being implemented in microgrids for local generation, transfer, and storage of various forms of energy. However, issues have emerged around developing an operating schedule for the different energy resources incorporated in the EHs to ensure the operation at minimum possible costs and emissions. This paper proposes a stochastic-based multi-objectives optimization model for optimal day-ahead scheduling of microgrids based on EHs. The proposed model simultaneously manages the non-dispatchable distributed generator units, such as wind turbines and photovoltaic systems, and energy storage systems, such as compressed air energy storage (CAES) and battery energy storage systems. Moreover, the thermal network is also established by introducing solar heat collectors, heat generated during the discharge of CAES, and thermal energy storage. Furthermore, the proposed model also considers the uncertainties of wind speed, solar radiation, and residential loads. A demand response program (DRP) is also employed to the residential and plug-in electric vehicles load to flatten the load curve and to perform more cost-effectively. A total reduction of 9.87 % and 21.41 % is achieved in the cost and emission amount, respectively, after applying the DRP. Furthermore, a decrease of 13.41 % and 45.04 % is also achieved for the costs of imported power and operation of the CAES, respectively. Different case studies are performed, and a comparative study with other existing research is conducted to demonstrate the effectiveness of the proposed approach. The simulation results show the efficacy of the proposed stochastic approach for optimal day-ahead scheduling.

关键词： Energy hub Compressed air energy storage (CAES) Plug-in electric vehicle (PEV) stochastic multi-objective optimization Responsive demands

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stochastic multi-objective scheduling of a wind farm integrated with high-temperature heat and power storage in energy market

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 132卷 107194-107194页

作者： Lasemi, Mohammad Ali Arabkoohsar, Ahmad Hajizadeh, Amin Aalborg Univ Dept Energy Technol Aalborg Denmark

In spite of significant opportunities created by wind energy, the high uncertainty of wind farms is problematic for their operators, making their participation in the energy market quite challenging. The combination of Air-based High-Temperature Heat and Power Storage (HTHPS), which is known as a novel energy storage technology, with a wind farm as an integrated system can make new opportunities for a Generation Company (GenCo) to overcome the wind generation challenges. This paper proposes a novel stochastic multi-objective optimization (SMOO) problem to determine the optimal charging and discharging scheduling and the best bidding strategy in the day-ahead energy market for an integrated energy system including a wind farm and HTHPS unit. The proposed model presents a comprehensive and coherent approach from historical data analysis as input to final decision making as output, that it can maximize Genco's profit by avoiding incorrect commitment and energy offering. In the phase of uncertainty handling, via a scenario-based approach, the wind uncertainty is modeled by Monte-Carlo Simulation (MCS) method based on wind speed forecast errors. In the optimization phase, the Pareto optimal set is obtained by Non-dominated Sorting Genetic Algorithm-II (NSGA-II), and then, a new method is also provided to choose the best possible solution based on the entropy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method and the minimax regret criterion. The historical data related to wind generation and energy prices provided by the Danish energy market in West Denmark is used in simulation analysis, and the results demonstrate pleasantly the effectiveness of the proposed model.

关键词： Wind farm Energy storage system Energy market Uncertainty analysis stochastic multi-objective optimization multi-criteria decision making

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A Proposed multi-objective, multi-Stage stochastic Programming With Recourse Model for Reservoir Management and Operation

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WATER RESOURCES RESEARCH 2021年第10期57卷 e2020WR029200-e2020WR029200页

作者： Li, Jinshu Zhang, Wei Yeh, William W-G Univ Calif Los Angeles Dept Civil & Environm Engn Los Angeles CA 90095 USA China Three Gorges Corp Beijing Peoples R China Wuhan Univ Wuhan Peoples R China

We present a multi-objective, multi-stage stochastic programming with recourse model for reservoir management and operation, where we use utility theory to select the best compromise solution from the Pareto front. A multi-stage streamflow scenario tree is generated first by the neural gas method. Then the Pareto front at each stage is produced by a modified constrained NSGA-II. A single best compromise solution on the Pareto front must be selected for the immediate stage and the model moves forward one stage and is re-optimized over a moving planning horizon of fixed duration. A key contribution of this study is the selection of the best compromise solution, which is achieved by a proposed linear spline utility function allied with regression. Our proposed utility function has the following advantages: (a) It satisfies the law of diminishing marginal rate of substitution, (b) it does not rely on the pre-specified weight or goal, and (c) it selects the best compromise solution that is likely to fall in the "knee regions" of the Pareto front. We apply the proposed optimization model to the Three Gorges Reservoir in China. The two conflicting objectives are (a) maximizing the total expected energy output in the planning horizon, and (b) maximizing the average expected ecological benefits in the planning horizon. The results show that the proposed model produces the optimal water release policy successfully under different hydrological scenarios, considering both the inflow uncertainty and the tradeoff between the two conflicting objectives.

关键词： stochastic multi-objective optimization stochastic programming with recourse reservoir operation utility function Three Gorges Reservoir

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