International Conference on Operations Research and Enterprise Systems最新文献_第8页

An Efficient Application of Goal Programming to Tackle Multiobjective Problems with Recurring Fitness Landscapes 目标规划在循环适应度景观多目标问题中的有效应用

International Conference on Operations Research and Enterprise Systems Pub Date : 2018-01-24 DOI: 10.1007/978-3-030-16035-7_8

Rodrigo Lankaites Pinheiro, Dario Landa Silva, W. Laesanklang, A. A. Constantino

引用次数: 2

Solving the Flight Radius Problem 解决飞行半径问题

International Conference on Operations Research and Enterprise Systems Pub Date : 2018-01-24 DOI: 10.5220/0006654003040311

A. Idrissi, Arnaud Malapert, R. Jolin

引用次数: 0

Data Clustering Method based on Mixed Similarity Measures 基于混合相似度量的数据聚类方法

International Conference on Operations Research and Enterprise Systems Pub Date : 2017-05-22 DOI: 10.5220/0006245601920199

Doaa S. Ali, Ayman Ghoneim, M. Saleh

{"title":"Data Clustering Method based on Mixed Similarity Measures","authors":"Doaa S. Ali, Ayman Ghoneim, M. Saleh","doi":"10.5220/0006245601920199","DOIUrl":"https://doi.org/10.5220/0006245601920199","url":null,"abstract":"Data clustering aims to organize data and concisely summarize it according to cluster prototypes. There are different types of data (e.g., ordinal, nominal, binary, continuous), and each has an appropriate similarity measure. However when dealing with mixed data set (i.e., a dataset that contains at least two types of data.), clustering methods use a unified similarity measure. In this study, we propose a novel clustering method for mixed datasets. The proposed mixed similarity measure (MSM) method uses a specific similarity measure for each type of data attribute. When computing distances and updating clusters’ centers, the MSM method merges between the advantages of k-modes and K-means algorithms. The proposed MSM method is tested using benchmark real life datasets obtained from the UCI Machine Learning Repository. The MSM method performance is compared against other similarity methods whether in a non-evolutionary clustering setting or an evolutionary clustering setting (using differential evolution). Based on the experimental results, the MSM method proved its efficiency in dealing with mixed datasets, and achieved significant improvement in the clustering performance in 80% of the tested datasets in the non-evolutionary clustering setting and in 90% of the tested datasets in the evolutionary clustering setting. The time and space complexity of our proposed method is analyzed, and the comparison with the other methods demonstrates the effectiveness of our method.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126731430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

K-modes and Entropy Cluster Centers Initialization Methods k模式和熵簇中心初始化方法

International Conference on Operations Research and Enterprise Systems Pub Date : 2017-04-21 DOI: 10.5220/0006245504470454

Doaa S. Ali, Ayman Ghoneim, M. Saleh

{"title":"K-modes and Entropy Cluster Centers Initialization Methods","authors":"Doaa S. Ali, Ayman Ghoneim, M. Saleh","doi":"10.5220/0006245504470454","DOIUrl":"https://doi.org/10.5220/0006245504470454","url":null,"abstract":"Data clustering is an important unsupervised technique in data mining which aims to extract the natural partitions in a dataset without a priori class information. Unfortunately, every clustering model is very sensitive to the set of randomly initialized centers, since such initial clusters directly influence the formation of final clusters. Thus, determining the initial cluster centers is an important issue in clustering models. Previous work has shown that using multiple clustering validity indices in a multiobjective clustering model (e.g., MODEK-Modes model) yields more accurate results than using a single validity index. In this study, we enhance the performance of MODEK-Modes model by introducing two new initialization methods. The two proposed methods are the K-Modes initialization method and the entropy initialization method. The two proposed methods are tested using ten benchmark real life datasets obtained from the UCI Machine Learning Repository. Experimental results show that the two initialization methods achieve significant improvement in the clustering performance compared to other existing initialization methods.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"58 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128009121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Integrated Production and Imperfect Preventive Maintenance Planning - An Effective MILP-based Relax-and-Fix/Fix-and-Optimize Method 集成生产和不完善预防性维修计划——一种有效的基于milp的松弛修复/修复优化方法

International Conference on Operations Research and Enterprise Systems Pub Date : 2017-03-31 DOI: 10.5220/0006285504830490

P. L. Tam, E. Aghezzaf, A. Khatab, C. Le

{"title":"Integrated Production and Imperfect Preventive Maintenance Planning - An Effective MILP-based Relax-and-Fix/Fix-and-Optimize Method","authors":"P. L. Tam, E. Aghezzaf, A. Khatab, C. Le","doi":"10.5220/0006285504830490","DOIUrl":"https://doi.org/10.5220/0006285504830490","url":null,"abstract":"This paper investigates the integrated production and imperfect preventive maintenance planning problem. The main objective is to determine an optimal combined production and maintenance strategy that concurrently minimizes production as well as maintenance costs during a given finite planning horizon. To enhance the quality of the solution and improve the computational time, we reconsider the reformulation of the problem proposed in (Aghezzaf et al., 2016) and then solved it with an effective MILP-based Relax-and-Fix/Fix-andOptimize method (RFFO). The results of this Relax-and-Fix/Fix-and-Optimize technique were also compared to those obtained by a Dantzig-Wolfe Decomposition (DWD) technique applied to this same reformulation of the problem. The results of this analysis show that the RFFO technique provides quite good solutions to the test problems with a noticeable improvement in computational time. DWD on the other hand exhibits a good improvement in terms of computational times, however, the quality of the solution still requires some more improvements.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126949871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Selecting Genetic Operators to Maximise Preference Satisfaction in a Workforce Scheduling and Routing Problem 在劳动力调度和路由问题中选择遗传算子使偏好满意度最大化

International Conference on Operations Research and Enterprise Systems Pub Date : 2017-02-23 DOI: 10.5220/0006203304160423

H. Algethami, Dario Landa Silva, A. Martínez-Gavara

{"title":"Selecting Genetic Operators to Maximise Preference Satisfaction in a Workforce Scheduling and Routing Problem","authors":"H. Algethami, Dario Landa Silva, A. Martínez-Gavara","doi":"10.5220/0006203304160423","DOIUrl":"https://doi.org/10.5220/0006203304160423","url":null,"abstract":"The Workforce Scheduling and Routing Problem (WSRP) is a combinatorial optimisation problem that involves scheduling and routing of workforce. Tackling this type of problem often requires handling a considerable number of requirements, including customers and workers preferences while minimising both operational costs and travelling distance. This study seeks to determine effective combinations of genetic operators combined with heuristics that help to find good solutions for this constrained combinatorial optimisation problem. In particular, it aims to identify the best set of operators that help to maximise customers and workers preferences satisfaction. This paper advances the understanding of how to effectively employ different operators within two variants of genetic algorithms to tackle WSRPs. To tackle infeasibility, an initialisation heuristic is used to generate a conflict-free initial plan and a repair heuristic is used to ensure the satisfaction of constraints. Experiments are conducted using three sets of real-world Home Health Care (HHC) planning problem instances.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131037620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Operations Research and Voting Theory 运筹学与投票理论

International Conference on Operations Research and Enterprise Systems Pub Date : 2017-02-23 DOI: 10.1007/978-3-319-94767-9_2

O. Hudry

引用次数: 0

Application of the Six Sigma Method for Improving Maintenance Processes - Case Study 六西格玛方法在改进维修过程中的应用-案例研究

International Conference on Operations Research and Enterprise Systems Pub Date : 2017-02-23 DOI: 10.5220/0006184703140320

M. Zasadzień

引用次数: 18

Mathematical Modeling Approaches to Solve the Line Balancing Problem 求解线路平衡问题的数学建模方法

International Conference on Operations Research and Enterprise Systems Pub Date : 2017-02-23 DOI: 10.5220/0006199404010408

S. Salama, Alyaa Abdelhalim, A. Eltawil

引用次数: 4

Towards Collaborative Optimisation in a Shared-logistics Environment for Pickup and Delivery Operations 在共享物流环境中实现取货和交付操作的协同优化

International Conference on Operations Research and Enterprise Systems Pub Date : 2017-02-23 DOI: 10.5220/0006291004770482

Timothy Curtois, W. Laesanklang, Dario Landa Silva, M. Mesgarpour, Yi Qu

引用次数: 1