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multi-objective Machining Parameters optimization for Chatter-Free Milling Process Considering Material Removal Rate and Surface Location Error

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IEEE ACCESS 2019年 7卷 183823页

作者： Deng, Congying Feng, Yi Miao, Jianguo Ma, Ying Wei, Bo Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing 400065 Peoples R China Sichuan Univ Sch Aeronaut & Astronaut Chengdu 610065 Peoples R China Chongqing Univ Coll Mech Engn Chongqing 400030 Peoples R China

In machining parameters optimization of a chatter-free milling process, the inevitable surface location error (SLE) reflecting the machined workpiece dimension accuracy has been barely considered as one objective representing the machining quality, lowering the optimization accuracy. Therefore, this paper provides an approach to establish a multi-objective optimization model, where the material removal rate (MRR) represents the machining efficiency and the SLE predicted in time-domain represents the machining quality. The non-dominated sorting genetic algorithm (NSGA-II) method is used to solve the multi-objective model and provide pareto optimal solutions to first determine some ideal optimal solutions. Then the analytic hierarchy process (AHP) and grey target decision (GTD) methods are combined to select one most satisfactory optimal solution which has a well balance between the MRR and SLE. A multi-objective model was established and taken as a case study to maximize the MRR and minimize the SLE. Comparison study was performed on this multi-objective model and two other mono-objective models for obtaining the optimal MRR and SLE respectively, which was combined with the influences of machining parameters on SLE to show the necessity of conducting a multi-objective optimization. Milling tests were conducted based on the solved optimal machining parameters, and the well consistence between the measured and predicted SLEs shows that the proposed multi-objective optimization method can provide an effective approach to balance the machining efficiency and quality when there are conflicts between different objectives.

关键词： Machining parameters optimization multi-objective optimization model surface location error NSGA-II

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Mitigating the risk of infection spread in manual order picking operations: A multi-objective approach

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APPLIED SOFT COMPUTING 2021年 100卷 106953-106953页

作者： Ardjmand, Ehsan Singh, Manjeet Shakeri, Heman Tavasoli, Ali Young, William A., II Ohio Univ Coll Business Dept Analyt & Informat Syst Athens OH 45701 USA DHL Supply Chain Solut Design Westerville OH USA Univ Virginia Sch Data Sci Charlottesville VA USA Payame Noor Univ Dept Mech Engn Tehran Iran

In the aftermath of the COVID-19 pandemic, supply chains experienced an unprecedented challenge to fulfill consumers' demand. As a vital operational component, manual order picking operations are highly prone to infection spread among the workers, and thus, susceptible to interruption. This study revisits the well-known order batching problem by considering a new overlap objective that measures the time pickers work in close vicinity of each other and acts as a proxy of infection spread risk. For this purpose, a multi-objective optimization model and three multi-objective metaheuristics with an effective seeding procedure are proposed and are tested on the data obtained from a major US-based logistics company. Through extensive numerical experiments and comparison with the company's current practices, the results are discussed, and some managerial insights are offered. It is found that the picking capacity can have a determining impact on reducing the risk of infection spread through minimizing the picking overlap. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Order picking Order batching Picker routing multi-objective optimization model multi-objective metaheuristics Evolutionary methods Physical distancing Supply chain disruption

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Dynamic Program Glen Canyon-Hoover Dam Cascade Reservoir Summary

International Core Journal of Engineering

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International Core Journal of Engineering 2022年第9期8卷 377-391页

作者： Yidan Ou Qiyue Wu Jiahuan Lu

Because of the drought condition in the Colorado River basin, officials from five states are negotiating to determine the best way to manage water and electricity production from the Glen Canyon Dam and the Hoover Dam. In this paper, we analyze the Glen Canyon Dam-Hoover Dam cascade reservoir operation and propose the corresponding water allocation plan. Specifically, we conduct nonlinear regression analysis on the water level and storage capacity of the two reservoirs, and the functional relationship between the water level and storage capacity was obtainedit with correlation coefficients R2 ＞0.99. Furthermore, we establish the Operate model of Cascade Reservoir (OMoCR) under constraint conditions. The Particle Swarm optimization (PSO) is used to simulate the variation of the water storage of different reservoirs. Finally, we establish a multi-objective optimization model based on water supply and power generation competition to achieve maximum economic and social benefits. The constraint method and interactive decision preference method are used to transform the multi-objective optimization model into single objective optimization model. Take the case of drought as an example for further analysis, and use Dynamic Programming (DP) to solve the model.

关键词： Water Allocation multi-objective optimization model Dynamic Programming

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Research on the low-carbon path of regional industrial structure optimization

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ENERGY STRATEGY REVIEWS 2024年 54卷

作者： Feng, Tingting Liu, Bin Wei, Yang Xu, Yawen Zheng, Huangyuying Ni, Zidong Zhu, Yide Fan, Xinyue Zhou, Zhongli Chengdu Univ Technol Geomath Key Lab Sichuan Prov Chengdu 610059 Peoples R China Chengdu Univ Technol Coll Management Sci Chengdu 610059 Peoples R China Chengdu Univ Technol Coll Math & Phys Chengdu 610059 Peoples R China Chengdu Univ Technol Coll Comp Sci & Cyber Secur Oxford Brookes Coll Chengdu 610059 Peoples R China State Grid Sichuan Elect Power Res Inst Chengdu 610095 Peoples R China Qingdao Univ Technol Coll Management Engn Qingdao 266520 Peoples R China

Establishing a sustainable industrial structure system is imperative for transforming the society to high-quality development and achieve the "carbon peak" and "carbon neutrality" goals. In this study, we explored a lowcarbon pathway of regional industrial structure optimization to achieve a balance among economic growth, social security, and environmental conservation. A multi-objective optimization model of industrial structure of economic-social-environmental system was constructed by minimizing carbon emissions and maximizing economic benefits and social employment. In this study, we explored the scientific allocation path for industrial structure and set various scenarios to meet the diverse preferences and development requirements of decisionmakers. Southwest China was used as a practical case to validate the model 's efficacy, offering a decisionmaking framework for the sustainable development of the regional economy. After optimization, carbon emissions reduced by 0.9 %-3.2 %, economic benefits increased by 2.5 %-4.8 %, and employment levels nearly doubled. The proportion of the transportation industry in southwest China decreased by 11.58 %. The raw coal was the energy source with the highest contribution to carbon emissions, 2494.2 x 10 8 kgCO 2 eq .

关键词： Economy-society-environment Carbon emissions Industrial structure multi-objective optimization model

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An innovative integrated framework for multi-level production planning and control systems in construction

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DEVELOPMENTS IN THE BUILT ENVIRONMENT 2024年 19卷

作者： Sheikhkhoshkar, Moslem El-Haouzi, Hind Bril Aubry, Alexis Hamzeh, Farook Sakikhales, Mohammad Univ Lorraine CNRS CRAN Epinal France Univ Greenwich London England Univ Lorraine CRAN CNRS Nancy France Univ Alberta Edmonton AB Canada Univ West London London England

The construction industry is plagued by chronic inefficiencies, such as low productivity, significant waste, cost overruns, and schedule delays. One of the most critical contributors to these issues is fragmented and ineffective planning and control methods. This research aims to address these challenges by developing an innovative integrated multi-level framework for production planning and control that harmonizes various planning methods and control metrics across different schedule levels. Employing the Design Science Research (DSR) methodology, this study systematically develops and evaluates an innovative framework that integrates internal and external project characteristics, utilizing the concept of "functionality" as a primary key for coherent integration. Furthermore, a multi-objective mathematical model is incorporated to clarify the framework's components and optimize the number of suggested planning methods and control metrics at each scheduling level, aiming to balance the functional requirements of project teams with the practicality of implementation. The application of the framework to a renovation case study demonstrates its practicality and strategic value, effectively addressing real-world project management challenges. Moreover, industry and academic evaluations highlight its potential to significantly improve construction project management practices.

关键词： Production planning and control multi-level framework Functionality concept multi-objective optimization model Design science research (DSR)

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Adaptive differential privacy preserving based on multi-objective optimization in deep neural networks

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第20期33卷 e6367-e6367页

作者： Fan, Tian Cui, Zhihua Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 003024 Peoples R China

Privacy data security has become an important bottleneck for the overall development of artificial intelligence and a key challenge that needs to be broken in the Internet era. The current research mainly considers differential privacy to effectively protect the private information in the data. However, as the noise increases, the precision of the training model will decrease. In order to solve above problem, an adaptive differential privacy (ADP) method is constructed and applied to deep neural networks. ADP adds noise adaptively in the training process according to the importance of features. We also build the differential privacy multi-objective optimization model (DPMOM). DPMOM adopts multi-objective optimization characteristics, takes accuracy and privacy protection as the optimization objectives. It optimizes the super parameters of deep neural networks and the noise of differential privacy. In addition, to better solve the ADP model, with the NSGA-II algorithm as the basic framework, a multi-objective optimization algorithm based on differential privacy protection (DPPMOA) is designed. Simulation experiments show that compared with other machine learning methods and differentially private stochastic gradient descent, the accuracy of ADP is higher under the same amount of noise. Through comparison with NSGA-II, IBEA, PESA-II, and AGE-II, DPPMOA is proved that the solution set of this algorithm is better.

关键词： deep neural networks differential privacy protection multi-objective optimization algorithm multi-objective optimization model

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Planning and Expansion of the Transmission Network in the Presence of Wind Power Plants

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第12期14卷 884-891页

作者： Sun, Hui Hebei Vocat Univ Ind & Technol Dept Automot Engn Shijiazhuang 050091 Hebei Peoples R China

The proliferation of renewable energy sources, particularly wind farms, is rapidly gaining momentum owing to their numerous benefits. Consequently, it is imperative to account for the impact of wind farms on transmission expansion planning (TEP), which is a crucial aspect of power system planning. This article presents a multi -objective optimization model that utilizes DC load flow to address the TEP challenge while also incorporating wind farm uncertainties into the model. The present study aims to optimize the expansion and planning of the TEP in the power system by considering investment and maintenance costs as objective functions. To achieve this, a multiobjective approach utilizing the shuffled frog leaping algorithm (SFLA) is proposed and implemented. The proposed objectives are simulated on the RTS-IEEE 24 -bus test network. The results obtained from the proposed algorithm are compared with those of the Genetic Algorithm (GA) to assess and validate the proposed approach.

关键词： Wind farms Transmission Expansion Planning (TEP) multi-objective optimization model Shuffled Frog Leaping Algorithm (SFLA)

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RESOURCE ALLOCATION optimization STRATEGY USING IMPROVED DIFFERENTIAL EVOLUTION ALGORITHM IN multi-RADIO multi-CHANNEL WSNS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2020年第3期16卷 923-937页

作者： Qiu, Bin Shantou Polytech Dept Comp Haojiang Dist 515078 Shantou Peoples R China

In order to improve the performance of multi-radio and multi-channel wireless sensor networks (WSNs), a resource allocation optimization strategy using improved differential evolution algorithm in multi-radio and multi-channel WSNs is proposed. Firstly, considering the interaction among WSNs node energy consumption, channel allocation, power control and slot allocation, the channel model of multi-node communication and the interference model of the system are constructed. Then, taking the interference and conflict of links as constraints, and taking the objects that reduce energy consumption, increase network capacity and improve the balance of resource allocation as objective functions, a multi-objective optimization model of resource allocation is constructed. The establishment of multi-objective optimization model highlights the trade-off among different objectives. Two-population differential evolution algorithm is employed to solve the model constructed in this paper, and the results are compared with those of standard differential evolution algorithms. The experimental results show that the performance of the network in all aspects has been greatly improved by the use of the proposed algorithm. The simulation results show that the link collisions can be effectively avoided and the network interference is reduced with the utilization of the proposed algorithm. Compared with the existing algorithms, the capacity of key links can be raised by two to three times, and the network capacity and resource allocation balance are improved as a result of application of the proposed algorithm.

关键词： Resource allocation optimization Wireless sensor networks multi-radio multi-channel Improved differential evolution algorithms multi-objective optimization model Slot allocation Link capacity

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The optimal allocation and the evaluation of water resources carrying capacity in Shendong mining area

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RESOURCES POLICY 2022年 77卷

作者： Wang, Yifeng Sun, Ken Li, Li Lei, Yalin Wu, Sanmang Wang, Fang Luo, Jingyi China Univ Geosci Sch Econ & Management Beijing 100083 Peoples R China North China Univ Water Resources & Elect Power Coll Water Resources Zhengzhou 450046 Peoples R China Minist Nat Resources Peoples Republ China Key Lab Carrying Capac Assessment Resource & Envir Beijing 100083 Peoples R China State Key Lab Water Resource Protect & Utilizat Co Beijing 100011 Peoples R China Hubei Acad Social Sci Wuhan 430077 Peoples R China China Univ Geosci Beijing Rm302Bldg 4 Beijing 100083 Peoples R China

Coal mining has consumed a large amount of water resources in China, and the entire industrial chain of coal production has a larger consumption of water resources than the past especially in Shendong mining area. The water resources carrying capacity determines the scale of coal industry development. The previous research had been conducted at the national, provincial and municipal levels, but there were few studies on the water resources carrying capacity of coal mining areas. The Shendong mining area is located in the coal-rich and waterscarce western region, where precipitation is low and evaporation is high. As a water-scarce area, the development of Shendong mining area is inseparable from the support of coal resources and the rational use of water resources. Therefore, firstly, taking the Shendong mining area as the research object, this paper set high, medium and low scenarios according to the development goals of Shendong mining area, Shenhua Group and the plans of China. The gray forecast model was used to predict the water demand of the economic-social-ecological system of the mining area from 2020 to 2030 under different scenarios. Secondly, a multi-objective optimization model was established with the objective function to optimize the water resources allocation in Shendong mining area. Thirdly, based on the forecast and optimization results, a water resources carrying capacity evaluation model was constructed to evaluate the water resources carrying capacity of the Shendong mining area. Finally, based on the results, the suggestions for rational development and the utilization of water resources in the Shendong mining area were proposed.

关键词： Optimal allocation Water resources carrying capacity Gray forecast model multi-objective optimization model Fuzzy integrated evaluation model

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A data-driven air cargo redistribution model based on multiple programming

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INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2019年第17期33卷

作者： Peng, Yang Wang, Pu Zhao, Xiaolong Chen, Meilin Zhang, Jun Zhang, Fan Cent S Univ Sch Traff & Transportat Engn Changsha 410000 Hunan Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China

Air cargo transportation is an essential mode of cargo transportation. How to distribute air cargo into flights better is an important operational problem. In this paper, air cargo data collected by CAAC (Civil Aviation Administration of China) during the first three months of 2018 are analyzed. We find that the available capacity for cargo transportation shows great variations, and the cargo compartment utilization rates of flights are heterogeneously distributed. Next, a data-driven air cargo redistribution model is developed based on multiple programming (MP). The proposed model can effectively transport high-priority goods and balance cargo compartment utilization rates of flights. In addition, the proposed model framework can provide a new solution to multi-objective or multi-stage optimization problems.

关键词： Data mining and data analysis air cargo transportation multi-objective optimization model

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