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A multi-objective emergency vehicle scheduling optimisation model

INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2020年第4期34卷 236-243页

作者： Yao, Jiao Shao, Chuwei Xia, Xiaomei Wang, Pincheng Wei, Yu Wang, Jin Univ Shanghai Sci & Technol Business Sch 334 Jungong Rd Shanghai Peoples R China Changsha Univ Sci & Technol Sch Comp & Commun Engn 960 South Rd Wangjiali Changsha 410114 Peoples R China

Various types of emergencies incidents occur frequently, which may cause casualties and huge economic losses. How to optimise the scheduling of emergency rescue vehicles and improve their rescue efficiency is of great significance. Based on the analysis of its influencing factors the emergency vehicle scheduling optimisation model was firstly established, which was oriented to achieve three objects, minimising total travel time, total cost of dispatching travel, and maximising the path reliability. Moreover, the model was solved by Non-dominated Sorting Genetic algorithm with elite strategy (nsga-ii algorithm). Finally, the local road network in Huangpu District, Shanghai, China was taken as to verify the model in this study. The results show that the optimal solution of the model can achieve the optimal total travel time, and total travel cost and path reliability of the scheduling scheme can also satisfy requirements, which means good validity and practicability.

关键词： emergency vehicle scheduling optimisation model multi-objective planning nsga-ii algorithm effectiveness coefficient comprehensive evaluation method influencing factors travel time cost of scheduling path reliability

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Predicting the Water Inflow Into the Dam Reservoir Using the Hybrid Intelligent GP-ANN- nsga-ii Method

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WATER RESOURCES MANAGEMENT 2024年第11期38卷 4137-4159页

作者： Moeini, Ramtin Nasiri, Kamran Hosseini, Seyed Hossein Univ Isfahan Fac Civil Engn & Transportat Dept Civil Engn Esfahan *** Iran

A key issue for effective management and operating of dam reservoirs is predicting the water inflow values into dam reservoir. To address this subject, here, genetic programming (GP) is used by proposing two cases. In the first case, water inflow values are predicted separately for each month. However, in the second case, these values are predicted simultaneously for all months. Furthermore, for each case, two approaches are proposed here. In the first approach, the hybrid method, called the ANN-NGSA-ii method, is proposed to find proper input data sets. However, in the second approach, the useful input data sets are found automatically using the GP method. For comparison purpose, the ANN and SARIMA models are also used, to predict the water inflow values. As a case study, in this research, the Zayandehroud dam reservoir is selected. The results indicate that the ANN model outperforms both results of the GP and SARIMA methods. In other words, correlation coefficient (R2), Nash Sutcliffe (NS), and root means square error (RMSE) values of ANN are 0.97, (0.88), 0.954 (0.87), and 17.19 (30.54) million cubic meters, respectively, for training (test) data set.

关键词： Water inflow prediction Genetic programming Artificial neural network nsga-ii algorithm Zayandehroud dam

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A scheduling optimization method for maintenance, repair and operations service resources of complex products

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JOURNAL OF INTELLIGENT MANUFACTURING 2020年第7期31卷 1673-1691页

作者： Li, Hao Mi, Shanghua Li, Qifeng Wen, Xiaoyu Qiao, Dongping Luo, Guofu Zhengzhou Univ Light Ind Henan Prov Key Lab Intelligent Mfg Mech Equipment Zhengzhou 450002 Peoples R China

Scheduling optimization of maintenance, repair and operations (MRO) service resources of complex product can help an enterprise improve customer service satisfaction, build value-added products, and enhance the enterprise's market competitiveness. This paper studies a scheduling optimization method for the MRO service resources of complex product. First, the scheduling problem in service resources is analyzed, a mathematical model for the service scheduling problem is given, and three objective functions to be optimized are proposed, which are to reduce customer waiting time, reduce excessive human resources, and maximize the cost performance index of the resources. Then, three separate objectives for optimizing service resource scheduling are analyzed based on three proposed methods which are improved genetic algorithm method, combined weight coefficient optimization method, and multi-objective optimization method based on the nondominated sorting genetic algorithm ii (nsga-ii). Finally, we use the three methods to carry out scheduling optimization of MRO service resources in the case of a large vertical mill. According to the analysis and comparison of results, the multi-objective optimization method based on the nsga-ii algorithm has an advantage in the scheduling optimization of complex product MRO service resources. In the engineering application, the service scheduling of complex products for managers provides theoretical basis, and can reduce the loss caused by subjective judgment.

关键词： Maintenance repair and operations Scheduling optimization Improved genetic algorithm nsga-ii algorithm

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Research on multi-objective planning of electric vehicle charging stations considering the condition of urban traffic network

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ENERGY REPORTS 2022年 8卷 11825-11839页

作者： Wang, Limeng Yang, Chao Zhang, Yi Bu, Fanjin Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin 132012 Peoples R China State Grid Harbin Power Supply Co Substn Secondary Harbin 150036 Peoples R China State Grid Harbin Power Supply Co Substat Secondar 128 Caiyi St Harbin 150036 Harbin Peoples R China

As an important supporting facility for electric vehicles, the reasonable planning and layout of charging stations are of great significance to the development of electric vehicles. However, the planning and layout of charging stations is affected by various complex factors such as policy economy, charging demand, user charging comfort, and road traffic conditions. How to weigh various factors to construct a reasonable model of charging station location and capacity has become a major difficulty in the field of electric vehicle charging facility planning. Firstly, this paper constructs the location and capacity optimization model of the charging station with the goal of maximizing the revenue of operators and minimizing the user's charging additional cost. At the same time, the road time-consuming index is introduced to quantify the impact of road congestion on the user's charging additional cost, so as to effectively improve the user's satisfaction during charging. Then, aiming at the charging station planning model, a non-dominated sorting genetic algorithm with an elite strategy (nsga-ii) based on chaos initialization and arithmetic crossover operator is proposed. Finally, taking the Haidian District of Beijing as the simulation object, the results show that compared with the situation of urban traffic networks not considered, the model proposed in this paper significantly reduces the cost of lost time of users by 11.4% and the total additional cost of users' charging by 7.6%. It not only ensures the economy of the system, but also effectively improves the charging satisfaction of users, which further verifies the feasibility and effectiveness of the model, and can provide a reference for the planning and layout of charging stations in the future.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Location and capacity of electric vehicle charging station Road time-consuming index User?s charging additional cost nsga-ii algorithm Chaotic mapping

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Bi-objective feature selection for discriminant analysis in two-class classification

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KNOWLEDGE-BASED SYSTEMS 2013年 44卷 57-64页

作者： Pacheco, Joaquin Casado, Silvia Angel-Bello, Francisco Alvarez, Ada Univ Burgos Dept Economia Aplicada Burgos Spain Inst Tecnol Estudios Super Monterrey Campus Monterrey Mexico Univ Autonoma Nuevo Leon Nuevo Leon Mexico

This works deals with the problem of selecting variables (features) that are subsequently used in discriminant analysis. The aim is to find, from a set of m variables, smaller subsets which enable an efficient classification of cases in two classes. We consider two objectives, each one associated with the misclassification error in each class (type I and type ii errors). Thus, we establish a bi-objective problem and develop an algorithm based on the nsga-ii strategy to this specific problem, in order to obtain a set of non-dominated solutions. Managing these two objectives separately (and not jointly) allows an enhanced analysis of the obtained solutions by observing the approach to efficient frontier. This is especially significant when each type of error has a different level of importance or when they cannot be compared. To illustrate these issues, several known databases from literature are used, as well as an additional database with several Spanish firms featured by financial variables and two classes: "creditworthy" and "non-creditworthy". Finally, we show that when solutions obtained by our nsga-ii implementation are evaluated from the classic mono-objective perspective (minimizing the ratio of both error types jointly) they are better than those obtained by classic methods for feature selection and similar than those provided by other recently published methods. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Classification Discriminant analysis Feature selection Bi-objective optimization nsga-ii algorithm

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Research on modelling and optimization of hot rolling scheduling

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第3期10卷 1201-1216页

作者： Liu, Li-Lan Wan, Xiang Gao, Zenggui Li, Xiaolong Feng, Bowen Shanghai Univ Shanghai Key Lab Intelligent Mfg & Robot Yanchang Str 149 Shanghai Peoples R China

Multiple species and random change in batch order forms has become the core business of Steel Intelligent Production Enterprises, necessitating new requirements for scheduling real-time adaptability and precision in the steel scheduling model. In this paper, a digital twin intelligent agent with cyberspace physical space integration is proposed as the unified driving source. First, an Open Multiple Objective Travelling Salesmen Problem model was established. For constraints such as a minimum rolling unit plan, process specification and a minimum power consumption per ton of steel, the nsga-ii algorithm was used to obtain a Pareto front for these constraints. With the help of appropriate penalty coefficients for the Pareto front, the constraints were defined as 3D-coordinates among cities of the Travelling Salesmen Problem. Combining the simulated annealing (SA) algorithm and the multi-objective particle swarm optimization (PSO) algorithm, the PSO algorithm was redefined and modified by introducing the metropolis criterion of the SA algorithm twice. This was used to obtain two extremes of the particle swarm, including the individual optimal solution and the global optimal solution to avoid local extrema. A model based on SA-MOPSO was thus obtained. Then, with the help of real-time mapping between the steel production line and the information model, the double-flow digital twin agent was established. This agent can achieve production scheduling model dynamic development and maturation itself. Finally, taking the steel hot rolling production line as a case in point, training and trial application of the model were performed to verify its adaptive development ability. Simulation results show that the algorithm discussed in this paper can solve the problem of hot rolling scheduling and provide support for decision-makers.

关键词： Digital twin Hot rolling plan scheduling Multi-objective constrained optimization nsga-ii algorithm MOPSO algorithm

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DESIGNING A MULTI-ECHELON CLOSED-LOOP SUPPLY CHAIN WITH DISRUPTION IN THE DISTRIBUTION CENTERS UNDER UNCERTAINTY

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2023年第4期19卷 2582-2615页

作者： Zadeh, Kaveh Keshmiry Harsej, Fatemeh Sadeghpour, Mahboubeh Aghdam, Mohammad Molani Islamic Azad Univ Dept Ind Engn Nour Branch Nour Iran Islamic Azad Univ Qual Res Ctr Nour Branch Nour Iran Islamic Azad Univ Dept Ind Engn Qaemshahr Branch Qaemshahr Iran Islamic Azad Univ Innovat & Management Res Ctr Ayatollah Amoli Branch Amol Iran

According to the need for further cost reduction and improving the process of the organization in the direction of customer demand, the concept of the supply chain has become increasingly significant and the organizations seek to expand this concept within their organizational framework. In this regard, efficient planning of distribution of products in the supply chain by considering disruption has received more attention recently. In this study a multi-objective mixed-integer linear programming model is developed for a green multi-echelon closed-loop supply chain network design under uncertainty. Moreover, a partial disruption is considered for distribution centers where has not been studied enough in previous works. The fuzzy credibility constraint approach is applied to cover uncertainty. In the following, the -constraint method is presented to solve and validate the model in small-sized instances. Moreover, a Non-dominated Sorting Genetic algorithm is developed for solving the large-sized problems. Results show that uncertainty has a crucial impact on objective functions where the increase of objective functions by increasing the level of uncertainty, which was observed in all categories. Furthermore, the proposed nsga-ii is the best tool to deal with large-size problems where the EC method lacks the necessary efficiency.

关键词： Closed-loop supply chain green supply chain customer satisfaction -constraint method nsga-ii algorithm

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A method for the optimum selection of datacenters in geographically distributed clouds

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JOURNAL OF SUPERCOMPUTING 2017年第9期73卷 4042-4081页

作者： Ziafat, Hassan Babamir, Seyed Morteza Univ Kashan Dept Comp Kashan Iran

The optimal selection of a datacenter is one of the most important challenges in the structure of a network for the wide distribution of resources in the environment of a geographically distributed cloud. This is due to the variety of datacenters with different quality-of-service (QoS) attributes. The user's requests and the conditions of the service-level agreements (SLAs) should be considered in the selection of datacenters. In terms of the frequency of datacenters and the range of QoS attributes, the selection of the optimal datacenter is an NP-hard problem. A method is therefore required that can suggest the best datacenter, based on the user's request and SLAs. Various attributes are considered in the SLA;in the current research, the focus is on the four important attributes of cost, response time, availability, and reliability. In a geo-distributed cloud environment, the nearest datacenter should be suggested after receiving the user's request, and according to its conditions, SLA violations can be minimized. In the approach proposed here, datacenters are clustered according to these four important attributes, so that the user can access these quickly based on specific need. In addition, in this method, cost and response time are taken as negative criteria, while accessibility and reliability are taken as positive, and the multi-objective nsga-ii algorithm is used for the selection of the optimal datacenter according to these positive and negative attributes. In this paper, the proposed method, known as nsgaii_Cluster, is implemented with the Random, Greedy and MOPSO algorithms;the extent of SLA violation of each of the above-mentioned attributes are compared using four methods. The simulation results indicate that compared to the Random, Greedy and MOPSO methods, the proposed approach has fewer SLA violations in terms of the cost, response time, availability, and reliability of the selected datacenters.

关键词： Geo-distributed datacenters Clustering K-means algorithm Multi-objective optimization algorithm nsga-ii algorithm Service-level agreement (SLA)

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Multiobjective formulation of the integration of storage systems within distribution networks for improving reliability

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ELECTRIC POWER SYSTEMS RESEARCH 2017年 148卷 87-96页

作者： Vieira Pombo, A. Murta-Pina, J. Fernao Pires, V. ESTSetubal Inst Politecn Setubal DEE Setubal Portugal Univ Nova Lisboa Ctr Technol & Syst Lisbon Portugal Univ Nova Lisboa Fac Ciencias & Tecnol Lisbon Portugal INESC ID Lisboa Lisbon Portugal

A new approach to improve reliability in distribution networks using energy storage systems is presented in this paper. Electric utilities have been using switching devices in distribution networks mainly to improve network reliability considering sustained interruptions. Temporary interruptions have been relegated to second place. However in modern societies appliances with electronic components are becoming more sensitive to temporary interruptions. Because of this, reliability indexes like the Momentary Average Interruption Frequency Index (MAIFI) that considers temporary interruptions are receiving more attention by electric utilities. The integration of storage systems into the multiobjective planning of distribution networks is proposed in this paper, to improve the reliability index MAIFI. This approach considers the optimization of storage systems integrated in a problem with multiple objective functions. The reliability index MAIFI is the first objective function, minimized by optimally choosing the size and location of battery banks in the distribution network. The second objective function, System Average Interruption Frequency Index (SAIDI) is optimized by finding the optimal number and location of switching devices. The third objective function consists on the minimization of the equipment cost. The optimization of these conflicting functions is achieved by the use of the Non-Dominated Sorting Genetic algorithm ii (nsga-ii). (C) 2017 Elsevier B.V. All rights reserved.

关键词： Distribution network planning Multiobjective optimization Switching devices Storage systems nsga-ii algorithm Reliability indexes SAIDI and MAIFI

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Gradient-aware trade-off control strategy dynamic optimization for a fuel cell hybrid electric vehicle

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 81卷 1107-1120页

作者： Lin, Xinyou Huang, Hao Xie, Liping Zou, Songchun Fuzhou Univ Coll Mech Engn & Automat Fuzhou 350108 Peoples R China

Road gradients not only affect the actual performance of control strategies but also impact battery life due to the drastic changes in power demands. To balance battery degradation with fuel economy using gradient information, this study proposes a gradient-aware trade-off control strategy. Initially, a vehicle dynamics model and a battery degradation model are established. Based on the characteristics of known road information and remaining driving distance, state of charge planning of the battery is conducted. Subsequently, the Nondominated Sorting Genetic algorithm-ii is applied for bi-objective optimization, yielding a set of Pareto solutions that represent different levels of energy consumption and battery degradation. Thereafter, by introducing a real-time battery degradation severity factor, an optimized bias coefficient is obtained, which adjusts in accordance with the gradient changes. Through the optimization of the bias line, the optimal bias solution set under different working conditions is determined, achieving the optimal control for power system. The fuel economy of the proposed strategy is improved by 6.8% relative to the mileage adaptive Equivalent Consumption Minimization Strategy, and the battery degradation inhibition is improved by 9.3%. After real-world conditions validation, the proposed strategy demonstrates good performance in both economic efficiency and battery life.

关键词： Fuel cell electric vehicle Energy management strategy nsga-ii algorithm Gradient-aware dynamic optimization

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