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IEEE International Conference on Communications (ICC)

作者： Yang, Jhe-Kai Huang, Kuan-Chun Chung, Cheng-Yang Chen, Yu-Chi Wu, Ting -Wei Yuan Ze Univ Dept Comp Sci & Engn Taoyuan Taiwan

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

The k-nearest neighbor (kNN) algorithm is a very simple manner in the area of machine learning. It is a supervised method to classify according to the distance between different instances and is also widely used in solving some classification problems. It is expected to obtain better training with a larger dataset. However, how to perform kNN algorithm efficiently is an issue with privacy-preserving. In this paper, we proposed a privacy-preserving k-nearest neighboring scheme by secret sharing and improve the kNN classification by preprocessing with tree structures. Finally, the applicability of our method is shown by experiments with real datasets.

关键词： Privacy nearest neighbor algorithm Secret sharing Tree structures

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A hybrid method of blockchain and case-based reasoning for remanufacturing process planning

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JOURNAL OF INTELLIGENT MANUFACTURING 2021年第5期32卷 1389-1399页

作者： Li, Shengqiang Zhang, Hua Yan, Wei Jiang, Zhigang Wuhan Univ Sci & Technol Acad Green Mfg Engn Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China

Remanufacturing plays a vital role in promoting the development of circular economy for its great advantages in energy saving, material saving and emission reduction. Remanufacturing process planning (RPP), which affects the performance of remanufacturing greatly, becomes increasingly important to the remanufacturing enterprises. In general, RPP is knowledge dependent. Some remanufacturing enterprises, especially small and middle-sized remanufacturing enterprises (SMREs) may have inadequate remanufacturing knowledge, which makes it difficult to implement a proper RPP. Therefore, how to share and make full use of the knowledge in different remanufacturing enterprises for RPP has become a bottleneck. To this end, a hybrid method integrating blockchain (BC) and case-based reasoning (CBR) for RPP, which can take full advantage of the remanufacturing knowledge by cross enterprises knowledge sharing, is presented in this paper. In this proposed method, a BC network was utilized to record the remanufacturing knowledge and its associated transactions to guarantee the security and reliability of knowledge sharing, and CBR was employed to retrieve and reuse the most suitable solution by analyzing the similarity between previous remanufacturing cases and new case with the nearest neighbor algorithm. Finally, a used lathe guideway was set as a case study to verify the feasibility and superiority of the proposed approach. The hybrid method has been applied in a prototype system written in HTML and JavaScript. The results indicated that the proposed approach can effectively help SMREs to obtain optimum solutions for RPP with comprehensive economic, environmental and social benefits.

关键词： Remanufacturing process planning Blockchain Case-based reasoning nearest neighbor algorithm

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Optimizing the Hub-and-Spoke Network with Drone-Based Traveling Salesman Problem

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DRONES 2023年第1期7卷 6-6页

作者： Gao, Chao-Feng Hu, Zhi-Hua Wang, Yao-Zong Shanghai Maritime Univ Sch Econ & Management Shanghai 201306 Peoples R China Shanghai Maritime Univ Logist Res Ctr Shanghai 201306 Peoples R China

The hub-and-spoke network (HSN) design generally assumes direct transportation between a spoke node and its assigned hub, while the spoke's demand may be far less than a truckload. Therefore, the total number of trucks on the network increases unnecessarily. We form a drone-based traveling salesman problem (TSP-D) for the cluster of spokes assigned to a hub. A truck starts from the hub, visiting each spoke node of the hub in turn and finally returning to the hub. We propose a three-stage decomposition model to solve the HSN with TSPD (HSNTSP-D). The corresponding three-stage decomposition algorithm is developed, including cooperation among variable neighborhood search (VNA) heuristics and nearest neighbor algorithm (NNA), and then the spoke-to-hub assignment algorithm through the reassignment strategy (RA) method. The performance of the three-stage decomposition algorithm is tested and compared on standard datasets (CAB, AP, and TR). The numerical analysis of the scenarios shows that whether it is trunk hub-level transportation or drone spoke-level transportation, it integrates resources to form a scale effect, which can reduce transport devices significantly, as well as decreasing the investment and operating costs.

关键词： hub and spoke network traveling salesman problem drone routing problem variable neighborhood search nearest neighbor algorithm

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Evolving nearest neighbor time series forecasters

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SOFT COMPUTING 2019年第3期23卷 1039-1048页

作者： Flores, Juan J. Cedeno Gonzalez, Jose R. Lopez Farias, Rodrigo Calderon, Felix Univ Michoacana Morelia Michoacan Mexico Ing Jorge L Tamayo AC Ctr Invest & Geog & Geomat Mexico City DF Mexico

This article proposes a nearest neighborsdifferential evolution (NNDE) short-term forecasting technique. The values for the parameters time delay , embedding dimension m, and neighborhood size E, for nearest neighbors forecasting, are optimized using differential evolution. The advantages of nearest neighbors with respect to popular approaches such as ARIMA and artificial neural networks are the capability of dealing properly with nonlinear and chaotic time series. We propose an optimization scheme based on differential evolution for finding a good approximation to the optimal parameter values. Our optimized nearest neighbors method is compared with its deterministic version, demonstrating superior performance with respect to it and the classical algorithms;this comparison is performed using a set of four synthetic chaotic time series and four market stocks time series. We also tested NNDE in noisy scenarios, where deterministic methods are not capable to produce well-approximated models. NNDE outperforms the other approaches.

关键词： Chaotic time series Forecasting nearest neighbor algorithm Evolutionary algorithms

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A two-stage algorithm for bi-objective logistics model of cash-in-transit vehicle routing problems with economic and environmental optimization based on real-time traffic data

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2022年 26卷

作者： Jin, Yuanzhi Ge, Xianlong Zhang, Long Ren, Jingzheng Chongqing Jiaotong Univ Sch Econ & Management Chongqing 400074 Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China

Traffic congestion problems are very common in large municipalities, especially in areas with karst features. Traffic jams happen in many key traffic nodes (such as the bridges across the river and the tunnels through the mountains) frequently, which may lead to severe challenges for the vehicle routing optimization. To effectively solve the routing problem of Cash-in-Transit (CIT) sectors, this study aims to establish a novel bi-objective Cash in-Transit Vehicle Routing Problem (CTVRP) model, including both the economic and environmental objectives, and designs a nearest neighbor-first Iterated Local Search-second algorithm (NN-ILS) with the consideration of the special terrain. Then, a case study of a CIT company is performed to demonstrate the model and algorithm and a vivid solution is presented in real road network after the optimization by using the route fitting procedure. Meanwhile, the accuracy and effectiveness of the algorithm is verified by comparing it with several classical algorithms and OR-Tools solver. The experimental results show that the developed algorithm can help the decision-makers to obtain the solutions with high quality compared with the classical algorithms. Finally, the uncertainty of the developed algorithm is analyzed empirically and the Multi-Attribute Decision Making (MADM) combined with Principal Component Analysis (PCA) is utilized to support decision-makers to select the best satisfying solution instead of choosing the solution with minimum objective value(s).

关键词： Cash-in-transit Vehicle Routing Problem (VRP) nearest neighbor algorithm Iterated local search algorithm Multi-attribute decision making

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Kriging-KNN Hybrid Analysis Method for Structural Reliability Analysis

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JOURNAL OF BRIDGE ENGINEERING 2022年第4期27卷

作者： Lu, Pengzhen Hong, Tao Wu, Ying Xu, Zijie Li, Dengguo Ma, Yiheng Shao, Limin Zhejiang Univ Technol Coll Civil Engn Hangzhou 310014 Zhejiang Peoples R China Jiaxing Nanhu Univ Jiaxing 314001 Zhejiang Peoples R China

When the conventional response surface method is used to solve the reliability problem in complex structures, the response surface fitting accuracy is low, and the reliability accuracy does not satisfy the requirements of the design specifications owing to the complex structure and highly nonlinear implicit functional function. Therefore, the kriging proxy model is used to construct the response surface of the implicit functional function. In addition, the kriging proxy model is combined with the k-nearest neighbor (KNN) algorithm. By improving the optimization efficiency of the model parameters, the constructed implicit function can be used to simulate the structural limit state function. Therefore, kriging-KNN hybrid analysis to calculate structure failure probability will be proposed. A numerical example will be provided to demonstrate the effectiveness of the proposed method. The results show that the proposed method utilized the kriging proxy model to construct a response surface with high fitting accuracy that used a few samples. In addition, the KNN will be used to address the inadequate accuracy and efficiency of the kriging agent model for classification;therefore, effectively improving the accuracy and efficiency of the structure reliability calculation. Compared with the traditional response surface method, the kriging-KNN hybrid analysis method reduced the error rate and improved the prediction accuracy and calculation efficiency significantly. Furthermore, the model could be easily combined with the existing general finite-element analysis software to analyze the reliability of complex structures.

关键词： Bridge engineering Reliability analysis Kriging model nearest neighbor algorithm Response surface

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Dynamic Enterprise Architecture planning using case-based reasoning and blockchain

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Procedia Computer Science 2022年 204卷 714-721页

作者： Imane ETTAHIRI Karim DOUMI ENSIAS Mohamed V University in Rabat Rabat Morocco

Nowadays, Enterprises are aware that the only existing certainty, is a future of great uncertainty (Allen Paulos). Therefore, the potential drivers of change should be detected and the solutions or path to solutions should be prepared in advance. In our work we adopt, the concept of Enterprise architecture, as the tool ensuring the alignment strategy//business//IT. So, to implement any change, we propose using CBR for case-based reasoning to store a set of problems with their solutions, and then to use algorithms of similarity and adaptation to propose the most adapted solution to the problem. Our goal in this paper is to maximize experiences and enrich the knowledge base so that the reasoning mechanism and similarity process is as reliable as possible. Thus, we suggest, a sharing of knowledge bases between allied or contracted companies or subsidiaries. (Example national strategy to be implemented regionally on a case-by-case basis) to ensure reliable sharing, we suggest the use of blockchain technology.

关键词： Enterprise Architecture Dynamic aspect EA planning Blockchain Case-based reasoning nearest neighbor algorithm

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The Multi-period Immediate Delivery Problem with a Comprehensive Indicator for Order Selection

The Multi-period Immediate Delivery Problem with a Comprehen...

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2021智慧城市与智能建造高端论坛

作者： Xiaosong Ding Hu Liu Xi Chen International Business School Beijing Foreign Studies University DECIDE Group Department of Computer and Systems SciencesStockholm University

The immediate delivery problem is a variant of vehicle routing problem with stochastic demand,in which not all customers are known in advance,but are revealed while the system *** this paper,we investigate a multi-period immediate delivery problem with a comprehensive indicator for order *** particular,we model the order selection as a sequential decision-making *** the immediacy of delivery tasks,the mismatch between demand and capacity,and the autonomy of the delivery staff,we propose herein a new comprehensive indicator consisting of distance,time pressure,and order revenue as the order selection criterion to help drivers choose the right *** the nearest neighbor algorithm is adopted to optimize routes ***,extensive simulation experiments show that the proposed indicator performs stable and better than the other single indicators.

关键词： Vehicle Routing Problem Dynamic Requests Multi Period Order Selection nearest neighbor algorithm

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Active and Passive nearest neighbor algorithm: A Newly-Developed Supervised Classifier

Active and Passive Nearest Neighbor Algorithm: A Newly-Devel...

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7th International Conference on Intelligent Computing (ICIC)

作者： Feng, KaiYan Gao, JunHui Feng, KaiRui Lin, Lei Li, YiXue Shanghai Ctr Bioinformat Technol 100 Qinzhou Rd Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Biolog Sci Key Lab Syst Biol Shanghai Peoples R China Simcyp Ltd Blades Enterprise Ctr Sheffield S2 4SU S Yorkshire England

ISBN: (纸本)9783642259432

K nearest neighbor algorithm (k-NN) is an instance-based lazy classifier that does not need to delineate the entire boundaries between classes. Thus some classification tasks that constantly need a training procedure may favor k-NN if high efficiency is needed. However, k-NN is prone to be affected by the underlying data distribution. In this paper, we define a new neighborhood relationship, called passive nearest neighbors, which is deemed to be able to counteract with the variation of data densities. Based on which we develop a new classifier called active and passive nearest neighbor algorithm (APNNA). The classifier is evaluated by 10-fold cross-validation on 10 randomly chosen benchmark datasets. The experimental results show that APNNA performs better than other classifiers on some datasets and worse on some other datasets, indicating that APNNA is a good complement to the current state-of-the-art of classification.

关键词： Machine Learning Lazy Classifier nearest neighbor algorithm Active and Passive nearest neighbor algorithm

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The Multi-period Immediate Delivery Problem with a Comprehensive Indicator for Order Selection

The Multi-period Immediate Delivery Problem with a Comprehen...

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2021智慧城市与智能建造高端论坛

作者： Xiaosong Ding Hu Liu Xi Chen International Business School Beijing Foreign Studies University 100089 BeijingChina DECIDE Group D International Business School Beijing Foreign Studies University 100089 BeijingChina

The immediate delivery problem is a variant of vehicle routing problem with stochastic demand,in which not all customers are known in advance,but are revealed while the system *** this paper,we investigate a multi-period immediate delivery problem with a comprehensive indicator for order *** particular,we model the order selection as a sequential decision-making *** the immediacy of delivery tasks,the mismatch between demand and capacity,and the autonomy of the delivery staff,we propose herein a new comprehensive indicator consisting of dis-tance,time pressure,and order revenue as the order selection criterion to help drivers choose the right *** the nearest neighbor algorithm is adopted to optimize routes ***,extensive simulation ex-periments show that the proposed indicator performs stable and better than the other single indicators.

关键词： Vehicle Routing Problem Dynamic Requests Multi Period Order Selec-tion nearest neighbor algorithm

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