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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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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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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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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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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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stochastic optimization of hybrid renewable energy systems using sampling average method

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2015年 52卷 1668-1679页

作者： Sharafi, Masoud ElMekkawy, Tarek Y. Univ Manitoba Dept Mech Engn Winnipeg MB R3T 5V6 Canada Qatar Univ Dept Mech & Ind Engn Doha Qatar

The stochastic attribute of renewable energy sources and the variability of energy load is a preeminent barrier to design hybrid renewable energy systems. In this paper, a new methodology is advanced to incorporate the uncertainties associated with RE resources and load in sizing an HRES in the application of buildings with low to high renewable energy ratio (RER). Dynamic multi-objective particle swarm optimization (DMOPSO) algorithm, simulation module, and sampling average technique are used to approximate a Pareto front (PF) for an HRES design through a multi-objective optimization framework. The main aim of design is to simultaneously minimize total net present cost (NPC), maximize renewable energy ratio, and minimize fuel emission while satisfy a desirable level of loss of load probability (LLP). The existing randomness in wind speed, solar irradiation, ambient temperature, and energy load is considered using synthetically data generation and sampling average method. The performance of the model has been examined in a building located in Canada as the case study, in which RER of the building is increased by using renewable energy technologies. The generated PF by the stochastic approach is compared to a deterministic PF using well-known performance metrics. Finally, a sensitivity analysis is carried out where the economic characteristics of the model are varied. (c) 2015 Elsevier Ltd. All rights reserved.

关键词： stochastic multi-objective optimization Hybrid renewable energy system Near zero energy buildings

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REAL CODED GENETIC ALGORITHM FOR stochastic HYDROTHERMAL GENERATION SCHEDULING

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Journal of Systems Science and Systems Engineering 2011年第1期20卷 87-109页

作者： Jarnail S.DHILLON J.S.DHILLON D.P.KOTHARI G.Z.S.College of Engineering and Technology Sant Longowal Institute of Engineering and Technology Vindhya Institute of Technology and Science

The intent of this paper is to schedule short-term hydrothermal system probabilistically considering stochastic operating cost curves for thermal power generation units and uncertainties in load demand and reservoir water inflows. Therefore, the stochastic multi-objective hydrothermal generation scheduling problem is formulated with explicit recognition of uncertainties in the system production cost coefficients and system load, which are treated as random variable. Fuzzy methodology has been exploited for solving a decision making problem involving multiplicity of objectives and selection criterion for best compromised solution. A real-coded genetic algorithm with arithmetic-average-bound-blend crossover and wavelet mutation operator is applied to solve short-term variable-head hydrothermal scheduling problem. Initial feasible solution has been obtained by implementing the random heuristic search. The search is performed within the operating generation limits. Equality constraints that satisfy the demand during each time interval are considered by introducing a slack thermal generating unit for each time interval. Whereas the equality constraint which satisfies the consumption of available water to its full extent for the whole scheduling period is considered by introducing slack hydro generating unit for a particular time interval. Operating limit violation by slack hydro and slack thermal generating unit is taken care using exterior penalty method. The effectiveness of the proposed method is demonstrated on two sample systems.

关键词： stochastic multi-objective optimization real-coded genetic algorithm fuzzy set economicload dispatch

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Coordination of combined heat and power-thermal-wind-photovoltaic units in economic load dispatch using chance-constrained and jointly distributed random variables methods

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ENERGY 2015年第C期79卷 50-67页

作者： Azizipanah-Abarghooee, Rasoul Niknam, Taher Bina, Mohammad Amin Zare, Mohsen Shiraz Univ Technol Dept Elect & Elect Engn Shiraz 71555313 Iran Iran Fars Shiraz Fars Elect Power Distributed Co Shiraz Iran

CHP (Combined heat and power) generation or cogeneration has been considered worldwide as the major alternative to traditional systems in terms of significant energy saving and environmental conservation. Furthermore, the wind power generators and photovoltaic units have vastly speared over the power systems due to their free inputs. However, there are many challenges for power system operators because of uncertain characteristics of renewable units and load demands. Therefore, a new multi-objective stochastic framework based on chance constrained programming is developed to handle combined heat and power economic load dispatch considering the stochastic characteristics of wind and photovoltaic power outputs, customer's electrical and heat load demands. The proposed technique makes use of a jointly distributed random variables method to calculate chance of meeting the electrical and heat load requirement using the target decision variables while maintaining the electrical energy cost below a scheduled value. The framework benefits from a new method named hybrid modified cuckoo search algorithm and differential evolution to extract the Pareto optimal surface for minimum cost and maximum probability of meeting the target cost and applies them as the objective functions. Applying to 6 and 40 unit test systems, the ability of the suggested framework is confirmed. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Combined heat and power economic dispatch Chance constrained programming Cuckoo search algorithm Jointly distributed random variables method stochastic multi-objective optimization Photovoltaic and wind power

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Simulation-based model for the optimization of machining parameters in a metal-cutting operation

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SIMULATION MODELLING PRACTICE AND THEORY 2018年 84卷 204-221页

作者： Chavez-Garcia, Hernan Castillo-Villar, Krystel K. Univ Texas San Antonio Dept Mech Engn Texas Sustainable Energy Res Inst San Antonio TX 78249 USA

To achieve a certain measurable performance in cutting machines, the machine parameters need to be optimized. Several constraints determine the possible values that these parameters can take. Although parameters are usually assumed to be deterministic, in practice, it is common to find variations on the characteristics of the products or the processes. Modeling machining parameters as stochastic factors provides a more realistic representation of cutting operations. Moreover, multiple operational objectives are of interest, in many real situations, these multiple objectives are conflicting. Consequently, the problem of setting the parameters becomes a trade-offsituation. This paper presents a novel Simulation-based multi-objective optimization (SimMOpt) solution procedure. The procedure is divided into two phases: (1) finding initial solutions and, (2) using a simulated annealing-based method for finding neighboring solutions. In the first phase, non-linear goal programming is used for finding high quality initial solutions. The second phase incorporates Pareto Archive Evolution Strategy (PAES) and hypotheses testing for searching near-optimal solutions for a set of stochastic parameters (i.e., cutting speed, feed rate, and depth of cut) in metal cutting operations. Three objectives are optimized (i.e., operation time, operation cost, and quality of the product). The results from implementing this procedure are analyzed and compared to a baseline methodology based on the multi-objective Simulated Annealing (MOSA) algorithm. The analysis demonstrates that the proposed method outperforms the Genetic Algorithm (GA), which was the benchmark algorithm, in terms of the solution quality of all the objectives. More importantly, the solutions from using the SimMOpt procedure outperform those obtained from using an enhanced MOSA-based approach (i.e., 4.71% improvement in the hypervolume approximation). (C) 2018 Elsevier B.V. All rights reserved.

关键词： stochastic multi-objective optimization Machining parameters Simulated annealing Manufacturing

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stochastic multi-objective generation dispatch

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2004年第11期32卷 1083-1103页

作者： Bath, SK Dhillon, JS Kothari, DP GZS Coll Engn & Technol Dept Elect Engn Bathinda 151001 Punjab India Sant Longowal Inst Engn & Technol Elect & Instrumentat Engn Dept Longowal Punjab India Indian Inst Technol Ctr Energy Studies New Delhi 110016 India

This article explores the use of genetic algorithm to search for the assigned weightage pattern of objectives to obtain the best compromised thermal power generation schedule in the multi-objective framework. The multi-objective problem is formulated considering non-commensurable objectives viz. operating cost, NO emission, and variance of real, as well as reactive, power generation mismatch with explicit recognition of statistical uncertainties in the thermal power generation cost curves, emission curves, and power demands, which are considered random variables. The solution set of such formulated problems is non-inferior due to contradictions among the objectives taken. The generation of non-inferior solutions requires an enormous amount of computation time when the objectives are more than two. In this article, the weighting method is used to convert the multi-objective optimization problem into a scalar optimization problem. Scalar optimization problem is solved many times for a different set of weight pattern to generate non-inferior solutions. The weighting patterns are either presumed on the basis of the decision maker's intuition or simulated with suitable step size. Such a simulation procedure may fail to provide the decision maker with the non-inferior solution that actually corresponds to the best compromised solution by virtue of the step size chosen. Thus, scalar weights are searched by genetic algorithms in the non-inferior domain. Among the generated population of scalar weights that generate non-inferior solutions, the system operator chooses the population of weighting pattern that provides maximum satisfaction level from the membership function of participating objectives and is termed as fitness function. The goals/objectives of fuzzy nature can be quantified by defining their membership functions. The validity of the proposed method has been demonstrated on an 11 node IEEE system comprising of five generators. The result of the proposed method is

关键词： economic-emission load dispatch fuzzy goals genetic algorithm membership function stochastic multi-objective optimization

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