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Location-allocation-routing for emergency shelters based on geographical information system (ArcGIS) by nsga-ii (case study: Earthquake occurrence in Tehran (District-1))

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SOCIO-ECONOMIC PLANNING SCIENCES 2022年 84卷

作者： Aghaie, Sepide Karimi, Behrooz Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran

The purpose of this study is to locate temporary shelters and assign injured people to the shelters. Routing people from destroyed areas to newly constructed shelters (in order to conduct rescue operations in the event of an earthquake) based on a case study of one of Tehran's densest areas (District 1) and the extent to which it is vulnerable to the occurrence of a possible earthquake is also considered. Some criteria influenced by the natural and physical characteristics of the desired area have been developed to accomplish this. The characteristics of this area are considered using Geographic Information Systems (GIS) software for determining the affected areas (as the demand points), and the ideal locations for shelters construction. Based on mathematical modeling, the optimal locations for shelters have been determined to allocate injured individuals to those shelters. Due to the existence of uncertainty regarding activated fault, their magnitude, severity, and the amount of demand for rescue services, multiple scenarios for earthquake occurrence have been considered, and the model is presented as a two-stage robust scenario-based optimization problem. The Non-Dominated Sorting Genetic algorithm (nsga-ii) is proposed to solve the problem. The computational results demonstrate the effectiveness of the proposed methodology. Suitable locations for construction of shelters, allocating shelters to affected areas, the optimal route for collecting people, the arrival time of each vehicle in each affected area, the total rescue time, the required budget, and the number of injured people collected from each area are the outputs of the model. This study aims to develop an adaptable and rational system for crisis relief in cities with varying characteristics.

关键词： Crisis management Location -allocation routing problem Geographic information system (GIS) Temporary shelter Earthquake nsga-ii algorithm

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Research on Interval Optimal Scheduling Strategy of Virtual Power Plants with Electric Vehicles

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WORLD ELECTRIC VEHICLE JOURNAL 2022年第12期13卷 235页

作者： Li, Taoyong An, Jinjin Zhang, Dongmei Diao, Xiaohong Liu, Changliang Liu, Weiliang China Elect Power Res Inst Beijing EV Charging & Battery Swapping Engn Techno Beijing 100192 Peoples R China North China Elect Power Univ Dept Automation Baoding Campus Baoding 071003 Peoples R China

The operation process of a virtual power plant is affected by many uncertainties, and how to ensure its comprehensive operation quality is a pressing challenge. For the virtual power plant incorporating electric vehicles, the interval number is used to describe the stochastic fluctuation of system uncertainties, and the optimization objectives are to (1) improve the operating economy, environmental protection, and grid load smoothing, (2) build a multi-objective interval optimal dispatching model considering the constraints of power balance and equipment operating characteristics, (3) solve the Pareto solution set by adopting the improved nsga-ii algorithm incorporating extreme scenario analysis, and (4) determine the optimal dispatching solution by the hierarchical analysis method. The median values of the determined optimal target intervals are 6456.11 yuan, 9860.01 kg, and 2402.56 kW. The algorithm shows that the proposed optimal dispatching strategy can effectively improve the economy of the virtual power plant and ensure that environmental protection and grid load smoothing requirements are met.

关键词： virtual power plant electric vehicle interval optimization nsga-ii algorithm

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A Time-Varying Incentive Optimization for Interactive Demand Response Based on Two-Step Clustering

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INFORMATION 2022年第9期13卷 421页

作者： Li, Fei Gao, Bo Shi, Lun Shen, Hongtao Tao, Peng Wang, Hongxi Mao, Yehua Zhao, Yiyi State Grid Hebei Elect Power Co Ltd Market Serv Ctr Shijiazhuang 050022 Hebei Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China

With the increasing marketization of electricity, residential users are gradually participating in various businesses of power utility companies, and there are more and more interactive adjustments between load, source, and grid. However, the participation of large-scale users has also brought challenges to the grid companies in carrying out demand-side dispatching work. The user load response is uneven, and users' behavioral characteristics are highly differentiated. It is necessary to consider the differences in users' electricity consumption demand in the design of the peak-valley load time-sharing incentives, and to adopt a more flexible incentive form. In this context, this paper first establishes a comprehensive clustering method integrating k-means and self-organizing networks (SONs) for the two-step clustering and a BP neural network for reverse adjustment and correction. Then, a time-varying incentive optimization for interactive demand response based on two-step clustering is introduced. Furthermore, based on the different clustering results of customers, the peak-valley load time-sharing incentives are formulated. The proposed method is validated through case studies, where the results indicate that our method can effectively improve the users' load characteristics and reduce the users' electricity costs compared to the existing methods.

关键词： consumer behavior two-step clustering demand response nsga-ii algorithm multi-objective optimization

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Multi-fuzzy-objective graph pattern matching in big graph environments with reliability, trust and social relationship

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2020年第1期23卷 649-669页

作者： Li, Lei Zhang, Fang Zhang, Zan Li, Peipei Bu, Chenyang Hefei Univ Technol Key Lab Knowledge Engn Big Data Hefei Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China

With the advent of the era of big data, the scale of data has grown dramatically, and there is a close correlation between massive multi-source heterogeneous data, which can be visually depicted by a big graph. Big graph, especially from Web data, social networks, or biometric data, has attracted more and more attention from researchers, which usually contains complex relationships and multiple attributes. How to perform efficient query and matching on big graph data is the basic problem on analyzing big graph. Using multi-constrained graph pattern matching, we can design patterns that meet our specific requirements, and find matched subgraphs to locate the required patterns to accomplish specific tasks. So how to find matched subgraphs with good attributes in big graph becomes the key problem on big graph research. Considering the possibility that a node in a subgraph may fail due to reliability, in order to select more and better matched subgraphs, in this paper, we introduce fuzziness and reliability into multi-objective graph pattern matching, and then use a multi-objective genetic algorithm nsga-ii to find the subgraphs with higher reliability and better attributes including social trust and social relationship. Finally, a reliability-based multi-fuzzy-objective graph pattern matching method (named as RMFO-GPM) is proposed. The experimental results on real data sets show the effectiveness of the proposed RMFO-GPM method comparing with other state-of-art methods.

关键词： Reliability Graph Pattern Matching Multi-Fuzzy-Objective nsga-ii algorithm

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Multi-objective optimization of customized bus routes based on full operation process

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MODERN PHYSICS LETTERS B 2020年第25期34卷

作者： Wang, Chao Ma, Changxi Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China

In order to rationally formulate a customized bus route plan to improve the operation efficiency of customized buses, in view of the problem of ignoring the distance between the boarding area and the alighting area, and setting it as a fixed value when performing custom bus route optimization modeling, as well as solving the problem of multiple custom bus parking lots. This paper proposes a method based on the nsga-ii algorithm using a three-stage hybrid coding method to solve. First, according to real life, the entire operation process of customized buses is divided into four phases. Second, based on the four stages, a multi-objective optimization model of customized bus routes that satisfies multiple parking lots, multiple cars, and multiple boarding and alighting stations is constructed to pursue the minimum total travel time of passengers and minimizes the operating costs of customized bus companies. Third, the nsga-ii algorithm is employed to solve the model, including three-stage hybrid coding, segmented crossover, and segmented mutation operators. Finally, the local road network in Lanzhou City is adopted for simulation research. The results show that it is feasible to use nsga-ii algorithm to solve the problem of customizing bus routes for multiple parking lots, multiple vehicles, and multiple boarding and alighting stations. The research results are of great value for exploring the optimization methods of customized bus routes and improving the operating efficiency of custom buses.

关键词： Customized bus multi-objective route optimization model full operation process nsga-ii algorithm three-stage hybrid coding

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Application of Multi-objective Genetic algorithm in Personnel Scheduling among Warehouses 2021

Application of Multi-objective Genetic Algorithm in Personne...

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2021 3rd International Conference on E-Business and E-commerce Engineering

作者： Tianwei Zhou Jiayao Chen Yizhen Xu Baoyu Xiao Shenzhen University College of Management China

ISBN: (纸本)9781450387392

With the fast development in e-commence and logistics, how to guarantee reliable supplement with lowest cost becomes an important problem. However, warehouses often have the problem of seasonal fluctuations, resulting in staff insufficient in peak season and human resources redundancy in slack season. To save the wasted human resources and prevent the revenue drop, it is important to dispatch staff across warehouses. Focus on this problem, we establish a multi-objective warehouse staff scheduling model, and solve it based on nsga-ii with the designed coding schemes. Simulation shows the obtained scheduling scheme and the superiority of our proposed framework has been verified by comparing it with a single-objective model.

关键词： Warehouse personnel scheduling assignment problem Bi-objective and multi-objective programming nsga-ii algorithm Working satisfaction

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Simulation and multi-objective optimization of a radial flow gas-cooled membrane reactor, considering reduction of CO2 emissions in methanol synthesis

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2021年第1期9卷

作者： Dehghani, Z. Rahimpour, M. R. Shariati, A. Shiraz Univ Sch Chem & Petr Engn Dept Chem Engn Shiraz 71345 Iran

This paper deals with a steady-state simulation of a Radial Flow Gas-Cooled Reactor (RF-GCR) and a Radial Flow Gas-Cooled Membrane Reactor (RF-GCMR) for enhancing CO2 removal in the methanol synthesis process. Reactors consist of annular packed-beds (subsections) and annular cooling-chambers. This configuration creates radial flows for reacting and synthesis gases and results in a negligible pressure drop along the beds. Each subsection is in contact with two cooling-chambers from top and bottom that offers an excellent heat transfer area and contributes to a noteworthy reduction in reaction phase temperature. Besides, hydrogen permeation through Pd-Ag membrane layer in upper and lower walls of RF-GCMR cooling-chambers shifts the reaction toward more products. Simultaneous effects of pressure, temperature, and H-2 permeation increases CO2 removal and methanol production rates by 592% and 6.84%, respectively in RF-GCMR compared to Conventional Gas Cooled Reactor (CGCR). Afterward, a two-objective optimization of RF-GCMR is implemented using Non dominated Sorting Genetic algorithm-ii (nsga-ii) to maximize methanol production rate and selectivity. Final Pareto-optimal solution is found by TOPSIS, LINMAP, and Shannon's Entropy decision-making methods. Shannon's Entropy, with the lowest deviation index, improves CO2 removal and methanol production rates 78.3% and 10.77%, respectively in Optimized RF-GCMR (ORF-GCMR) compared to non-optimized RF-GCMR.

关键词： CO2 reduction Methanol synthesis Radial-flow Pd-Ag membrane nsga-ii algorithm Shannon's Entropy decision-making method

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Multi-objective constructal design for compound heat dissipation channels in a three-dimensional trapezoidal heat generation body

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2021年 127卷

作者： Zhang, Zhiming Feng, Huijun Chen, Lingen Ge, Yanlin Naval Univ Engn Coll Power Engn Wuhan 430033 Peoples R China Wuhan Inst Technol Inst Thermal Sci & Power Engn Wuhan 430205 Peoples R China Hubei Prov Engn Technol Res Ctr Green Chem Equipm Wuhan 430205 Peoples R China Wuhan Inst Technol Sch Mech & Elect Engn Wuhan 430205 Peoples R China

A three-dimensional compound heat dissipation channel model with both heat conduction and fluid channels in a trapezoidal heat generation body is established in this paper. Firstly, constructal design of the model is conducted by taking the composite function of maximum temperature difference and pumping power consumption as the optimization objective based on the weighted sum method. The optimization results with compound heat dissipation channel and pure fluid channel are compared. Secondly, the Pareto optimal set also obtained by the multi-objective optimization to satisfy different design requirements based on the nsga-ii algorithm. The results show that there exist the optimal fluid channel radius, ratio of trapezoidal height to total length and length ratio of elemental channels to make the composite function of the trapezoidal heat generation body approach its minimum. Compared with the initial design, the composite function is reduced by 25.7% after constructal optimization with three design variables. The result obtained by the weighted sum method is also located at the Pareto optimal set, which simultaneously verifies the results obtained by the weighted sum method and multiobjective optimization method. The optimization results may provide a new theoretical guideline for the designs of various electronic devices.

关键词： Constructal theory Compound heat dissipation channels Trapezoidal heat generation body Composite function nsga-ii algorithm Generalized thermodynamic optimization

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An approach to optimize bed sill design using multi-objective evolutionary algorithms

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ENVIRONMENTAL EARTH SCIENCES 2021年第15期80卷 480-480页

作者： Tabatabai, Mohammad Reza Majdzadeh Adineh, Saeedeh Alimohammadi, Saeed Ghoreishi, Seyed Hosein Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran Iran

Bed sills are a kind of grade control structure built at the same level as the river bed to protect it against erosion and to reduce the longitudinal slope of the channel. The aim of the present research was to determine the optimal dimensions of the structure by devising a new methodology to minimize cost and maximize stability, based on the optimization model of the multi-objective non-dominated sorting genetic algorithm (nsga-ii). This is achieved by calculating the width, height and distance between bed sills with these stability and cost objectives in mind. The nsga-ii algorithm is applied to solve the problem, and a set of Pareto points is obtained. The results are verified by considering the bed sills of the River Madarsoo in Iran and the River Plima in Italy, and comparing the results with those obtained using the Branch-and-Bound (B-and-B) algorithm. The results indicate that the cost and stability could respectively be improved by up to 18 and 20% in the River Madarsoo, and up to 18 and 26% in the River Plima, if the nsga-ii algorithm is used.

关键词： Multi-objective optimization nsga-ii algorithm Pareto front Bed sill River Madarsoo River Plima

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Research on Optimization of Multi-Objective Regional Public Transportation Scheduling

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algorithmS 2021年第4期14卷 108页

作者： Yang, Xinfeng Qi, Yicheng Lanzhou Jiaotong Univ Sch Traff & Transportat Engn Lanzhou 730070 Peoples R China

The optimization of bus scheduling is a key method to improve bus service. So, the purpose of this paper is to address the regional public transportation dispatching problem, while taking into account the association between the departure time of buses and the waiting time of passengers. A bi-objective optimization model for regional public transportation scheduling is established to minimize the total waiting cost of passengers and to maximize the comprehensive service rate of buses. Moreover, a nsga-ii algorithm with adaptive adjusted model for crossover and mutation probability is designed to obtain the Pareto solution set of this problem, and the entropy weight-TOPSIS method is utilized to make a decision. Then the algorithms are compared with examples, and the results show that the model is feasible, and the proposed algorithms are achievable in solving the regional public transportation scheduling problem.

关键词： regional public transportation bus scheduling nsga-ii algorithm easy transfer multi-objective

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