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A stochastic programming model with endogenous uncertainty for selecting supplier development programs to proactively mitigate supplier risk

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2022年 107卷 102542-102542页

作者： Zhou, Rui Bhuiyan, Tanveer Hossain Medal, Hugh R. Sherwin, Michael D. Yang, Dong Donghua Univ Sch Business & Management Dept Informat Management Shanghai Peoples R China Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15260 USA Mississippi State Univ Dept Ind & Syst Engn Mississippi State MS 39762 USA

Poor supplier performance can result in delays that disrupt manufacturing operations. By proactively managing supplier performance, the likelihood and severity of supplier risk can be minimized. In this paper, we study the problem of selecting optimal supplier development programs (SDPs) to improve suppliers' performance with a limited budget to proactively reduce supplier risks for a manufacturer. A key feature of our research is that it incorporates the uncertainty in supplier performance in response to SDPs selection decisions. This uncertainty is endogenous (decision-dependent), as the probability of supplier performance depends on the selection of SDPs, which introduces modeling and algorithmic challenges. We formulate this problem as a two-stage stochastic program with decision-dependent uncertainty. We implement a sample-based greedy algorithm and an accelerated Benders' decomposition method to solve the developed model. We evaluate our methodology using the numerical cases of four low-volume, highvalue manufacturing firms. The results provide insights into the effects of the budget amount and of the number of SDPs on the firm's expected profit. Numerical experiments demonstrate that an increase in budget results in profit growth, e.g., 5.09% profit growth for one firm. At a lower budget level, increasing the number of available SDPs results in more profit growth. The results also demonstrate the significance of considering uncertainty in supplier performance and considering multiple supplier risks for the firm. In addition, computational experiments demonstrate that our algorithms, especially our greedy approximation algorithm, can solve large-sized problems in a reasonable time. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Supplier risk mitigation Stochastic programming Supplier development program Benders' decomposition greedy algorithm

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A new clustering algorithm based on a radar scanning strategy with applications to machine learning data

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期191卷 116143-116143页

作者： Ma, Lin Zhang, Yi Leiva, Victor Liu, Shuangzhe Ma, Tiefeng Southwestern Univ Finance & Econ Sch Stat Chengdu Peoples R China Fuzhou Univ Coll Elect Engn & Automat Fuzhou Peoples R China Pontificia Univ Catolica Valparaiso Sch Ind Engn Valparaiso Chile Univ Canberra Fac Sci & Technol Canberra ACT Australia

In this paper, we propose a novel density-based radar scanning clustering algorithm. Its main objective is to quickly discover and accurately extract individual clusters by employing the radar scanning strategy. By using this algorithm, the number of clusters does not need to be specified beforehand. Two techniques are utilized in our proposed method. First, we use a fast mean-shift algorithm with adaptive radius and active subsets to effectively locate the centers, reducing the computational time significantly. Second, we employ the shape of the probability density function of the distribution of distances between a selected point and the other points in the data set. This is performed to determine the critical parameters of the radiuses of the fast mean-shift algorithm and radiuses of clusters. The new algorithm has four merits. It reduces the computational complexity, overcomes problems caused by high dimensionality, is capable of dealing with heterogeneous spherical data sets, and lastly, is robust to noise and outliers. After applying our proposed method to several kinds of synthetic and real-world data sets, the results indicate that the density-based radar scanning algorithm is efficient and accurate.

关键词： Adaptive clustering Artificial intelligence greedy algorithm High dimensionality Probability density function

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Inter-Structure and Intra-Structure Similarity of Use Case Diagram using greedy Graph Edit Distance 2

Inter-Structure and Intra-Structure Similarity of Use Case D...

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2nd International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Zulfa, Fatimatus Siahaan, Daniel Oranova Fauzan, Reza Triandini, Evi Inst Teknol Sepuluh Nopember Dept Informat Surabaya Indonesia Politekn Negeri Banjarmasin Inst Teknol Sepuluh Nopember Dept Informat Surabaya Banjarmasin Indonesia Politekn Negeri Banjarmasin Inst Teknol Sepuluh Nopember Dept Elect Engn Surabaya Banjarmasin Indonesia Inst Teknol & Bisnis STIKOM Bali Dept Informat Syst Denpasar Indonesia

ISBN: (纸本)9781728172576

Use case diagram is one of the behaviour diagrams in UML diagrams. It models the basic needs of users and how users and systems interact to achieve them. Each use case diagram forms a structure which abides a set of standard notations and grammar. Therefore, we can measure the structural similarity between the two use case diagrams. Measuring the similarity of two use case diagram structures can help in the process of reusing diagrams, detecting clones, and assessing learning results. This study proposes an advanced method to measure the structural similarity between the two use case diagrams. Each use case diagram is translated into a graph. The graph represents the inter-structure and intra-structure of a diagram. The graph inter-structure models the interaction between the main use cases and their actors. In comparison, the graph intra-structure models the detailed structure descriptions of every main use case. The similarity between the two represented graph calculates using greedy Graph Edit Distance. Our experimental result shows that the proposed graph can measure the similarity of the use case structural diagram. The greedy Graph Edit Distance also can calculate the similarity other UML diagrams for the specific purpose.

关键词： graph edit distance graph similarity greedy algorithm structural similarity use case diagram

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greedy copula segmentation of multivariate non-stationary time series for climate change adaptation

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PROGRESS IN DISASTER SCIENCE 2022年 14卷

作者： Heo, Taemin Manuel, Lance Univ Texas Austin Dept Civil Architectural & Environm Engn 301 E Dean Keeton St C1700 Austin TX 78712 USA

Non-stationary climate data are often encountered in dealing with natural hazards, climate change and disaster reduction. With drought, for instance, it is common to encounter such non-stationary data sets (time series). The objectives of this work are to formulate a rational data-driven approach that can consider non-stationary and time series on multiple random variables that can have generalized underlying probability distributions and dependence structures. 'the methodology proposed seeks to divide up the data into non-overlapping segments, each of which is treated as stationary with some underlying probability and dependence structure, while the long time series yields multiple such segments that are mutually independent. The greedy Copula Segmentation (GCS) algorithm developed employs bast-fit probability distributions and copula functions after data-driven time series segmentation. Validation of the proposed methodology is demonstrated using a benchmark problem as well as a single-site realistic drought example. The proposed GCS . approach has potential use in climate change adaptation (CCA) and disaster risk reduction (DRR) for any climate-related hazards involving non-stationary time series data.

关键词： Time series segmentation greedy algorithm Non-stationary stochastic process

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Optimal resource allocation in HIV self-testing secondary distribution among Chinese MSM: data-driven integer programming models

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PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES 2022年第2214期380卷 20210128-20210128页

作者： Jing, Fengshi Zhang, Qingpeng Ong, Jason J. Xie, Yewei Ni, Yuxin Cheng, Mengyuan Huang, Shanzi Zhou, Yi Tang, Weiming Guangdong Second Prov Gen Hosp Inst Healthcare Artificial Intelligence Guangzhou 510317 Peoples R China Univ North Carolina Project China Guangzhou Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China London Sch Hyg & Trop Med Fac Infect & Trop Dis London Peoples R China Monash Univ Cent Clin Sch Melbourne Vic Australia Duke Univ Duke Global Hlth Inst Durham NC USA Zhuhai Ctr Dis Control & Prevent Zhuhai Peoples R China Macau Univ Sci & Technol Fac Med Macau Peoples R China Univ N Carolina UNC Sch Med Dept Med Div Infect Dis Chapel Hill NC 27515 USA

Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several innovative implementations of HIVST have been developed and piloted among some HIV high-risk populations like men who have sex with men (MSM) to meet the global testing target. One innovative strategy is the secondary distribution of HIVST, in which individuals (defined as indexes) were given multiple testing kits for both self-use (***-testing) and distribution to other people in their MSM social network (defined as alters). Studies about secondary HIVST distribution have mainly concentrated on developing new intervention approaches to further increase the effectiveness of this relatively new strategy from the perspective of traditional public health discipline. There are many points of HIVST secondary distribution in which mathematical modelling can play an important role. In this study, we considered secondary HIVST kits distribution in a resource-constrained situation and proposed two data-driven integer linear programming models to maximize the overall economic benefits of secondary HIVST kits distribution based on our present implementation data from Chinese MSM. The objective function took expansion of normal alters and detection of positive and newly-tested 'alters' into account. Based on solutions from solvers, we developed greedy algorithms to find final solutions for our linear programming models. Results showed that our proposed data-driven approach could improve the total health economic benefit of HIVST secondary distribution. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

关键词： HIV self-testing secondary distribution integer programming greedy algorithm mathematical optimization

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MIMO Radar Super-Resolution Imaging Based on Reconstruction of the Measurement Matrix of Compressed Sensing

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Ding, Jieru Wang, Min Kang, Hailong Wang, Zhiyi Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

This letter proposes a novel sparse recovery method of multiple-input and multiple-output (MIMO) radar compressed sensing (CS) imaging algorithms. This method leverages the prior structure of the measurement matrix to judge targets' locations and to estimate the sparsity level in the grid roughly and finally inhabits the emergence of false targets in the imaging figure. Explicitly, the algorithm we propose is inspired by the orthogonal matching pursuit (OMP) algorithm. First, the measurement matrix can be divided into some submatrices by column. Then, we estimate which submatrices do not contain signal components by the algorithms we propose in this literature to achieve the reconstruction of the measurement matrix. Finally, we use the sparse Bayesian learning algorithm and the sparsity adaptive matching pursuit algorithm to recover the target location and scattering intensity. Experiments validate that the reconstruction error of the algorithm we propose is much lower than other sparse recovery algorithms, and targets in the imaging are more obvious than other algorithms.

关键词： Radar imaging Imaging Matching pursuit algorithms MIMO radar Signal processing algorithms Receivers MIMO communication Compressed sensing (CS) greedy algorithm multiple-input and multiple-output (MIMO) radar sparse Bayesian learning (SBL) sparsity adaptive matching pursuit (SAMP)

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Overview of definition,evaluation,and algorithms of serendipity in recommender systems

Overview of definition,evaluation,and algorithms of serendip...

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作者： Ning Sun University of Alberta

Over time,recommendation systems are playing an important role in an increasingly wide range of areas,such as paper retrieval sites that can recommend papers or books to users,and shopping sites that can recommend products to *** the development of recommendation systems,there are many different metrics to measure a good recommendation system,including *** paper summarizes the definition of serendipity,a review of the metrics for measuring serendipity,and several major serendipity-oriented algorithms and presents conjectures for future research on *** the research of some papers,for how to delimit and evaluate recommender systems,experts have mostly focused on the unexpected,and most of them use and optimize collaborative filtering algorithms to achieve and improve serendipity.

关键词： recommender system serendipity systematic literature review content-based filtering collaborative filtering greedy algorithm

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Optimization of wind farm layout with optimum coordination of turbine cooperations

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 164卷

作者： Tang, Xiao-Yu Yang, Qinmin Stoevesandt, Bernhard Sun, Youxian Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Fraunhofer Inst Wind Energy & Syst Technol CFD Grp D-26129 Oldenburg Germany

Traditional methods for micro-siting of wind farms usually assume that each turbine is controlled to obtain its individual maximum power generation. However, it is now common practice to implement farm level control actions during the daily operations in order to improve the economic profit of an entire wind plant. This study is then proposed to investigate the wind farm layout optimization to achieve the minimum cost per unit of energy (CoE), when the farm level control operations, the coordination of turbine cooperations (CTC), is considered preliminary farm planning stage. It is found that, by optimizing the turbine number and layout with CTC involved, the efficiency of a wind farm can be further improved. A single-objective optimization problem is firstly established by modelling all aforementioned factors of interest. Subsequently, a hybrid optimization algorithm is proposed, with a greedy algorithm to optimize the turbine number and a particle swarm optimization (PSO) algorithm to refine the turbine layout scheme. Eventually, extensive simulation studies with various scenarios are provided to substantiate the feasibility of the proposed scheme.

关键词： Wind farm layout optimization Cooperative turbine control greedy algorithm Wake interference Particle swarm optimization (PSO) Wind farm micro-siting

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An Energy-aware greedy Heuristic for Multi-objective Optimization in Fog-Cloud Computing System

An Energy-aware Greedy Heuristic for Multi-objective Optimiz...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Jia, Mengying Chen, Wenjie Zhu, Jie Tan, Hexiang Huang, Haiping Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China

ISBN: (纸本)9781728185262

As an complement of cloud computing, fog computing provides computing services with closer geographic distance and focuses on distributed computing. In this paper, we consider the bi-objective task scheduling problem with heterogeneous resources in a fog-cloud computing system. There are two minimization objectives: energy consumption and delay. We formulate a workload allocation problem model involving fog devices (FDs) and the cloud servers (CSs). The computing resources are heterogeneous on the energy consumption, processing capability and delay. An energy-aware greedy heuristic algorithm (EG) is developed to search for Pareto Front solutions. For the problem under discussed, Experimental results indicate that the proposal algorithm is effective and robust compared with the comparison algorithm.

关键词： fog computing heterogeneous resources task scheduling cloud computing multi-objective Pareto Front greedy algorithm

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A parking space allocation algorithm based on distributed computing

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2020年第4期33卷 250-258页

作者： Chen, Guanlin Pang, Huajian Xu, Huang Yang, Wujian Chen, Yong Zhejiang Univ City Coll Sch Comp & Comp Sci Hangzhou 310015 Peoples R China Changzhou Univ Sch Informat Sci & Engn Changzhou 213164 Jiangsu Peoples R China Zhejiang Univ City Coll Sch Business Hangzhou 310015 Peoples R China

In order to make it easier for drivers to find a parking slot, optimise the resources of urban parking slots, and alleviate the problem of parking slot shortage, a distributed parking allocation algorithm was proposed. The algorithm collects the parking requests of user, this parking requests including the current position coordinate information of the users and destination coordinate information, the algorithm allocates parking spaces to users by analysing the available state of parking spaces, then return the parking route planning to the client. Compared with the traditional algorithm, the distributed parking algorithm has a higher ability to withstand pressure and global search capability, and it can ensure the real-time and validity of the parking information, so it can reduce the problem of the parking space shortage and unavailable parking space. The simulation results show that this algorithm can find the solution set more quickly and accurately under the circumstance of high demand. It also has application value and practicality.

关键词： city traffic parking allocation distributed computing matching algorithm smart city genetic algorithm parking slot urban management parking requests greedy algorithm

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