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作者： Duy Nguyen Duc Narameth Nananukul School of Management Technology Sirindhorn International Institute of TechnologyThammasat University

This paper presents an advanced methodology that integrates a machine learning methodology into an optimization process. The framework of an interactive machine learning algorithm was developed to meet the challenges in solving large-scale optimization problems. An artificial neural network(ANN) is used with the knowledge gained from solving previous problems with different scenarios to define a good starting point for a solution searching process. By using an initial solution, known as "warm start", the search space can be reduced to get more opportunity to find an optimal solution. The applicability of the proposed method was evaluated by using it to determine the optimal facility locations for a biomass supply chain problem using a real case study from Central Vietnam. The supply chain planning model is based on an optimization model, where the goal is to maximize the benefits from meeting the electricity demand minus the total cost from facility cost, penalty cost from lost demand, and operational costs form the supply chain. The structure of the ANN, the number of intermediate layers and the number of processing nodes, was determined by comparing the accuracy from different configurations. The ANN with two intermediate layers possesses the best performance from the training and testing datasets. The proposed model succeeded in predicting the facility location with more than 98% prediction accuracy. The results from our framework provide optimal solutions while saving runtime.

关键词： Machine learning Optimization mixed integer programming Supply chain planning Artificial neural network(ANN) Relaxation Induced Neighbourhood Search(RINS)

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Hybrid Predictive Control Design based on Particle Swarm Optimization and Genetic Algorithm

Hybrid Predictive Control Design based on Particle Swarm Opt...

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2011 3rd International Conference on Computer Research and Development(ICCRD 2011)

作者： Yaser Mohammad Nezhad Mehdi Shahbazian Department of Instrumentation and Automation Petroleum University of Technology

This paper discusses a model predictive control approach to hybrid systems with continuous and discrete *** algorithm,which takes into account a model of a hybrid system,described as Hybrid ***,to avoid computational complexity and computation time,the nonlinear optimization problem is solved by evolutionary algorithms(EA)such as Genetic Algorithms(GA)and Particle Swarm Optimization(PSO).We have applied both GA and PSO algorithms for nonlinear optimization in Hybrid Predictive Control(HPC)for the start-up of a Continuous Stirred-Tank Reactor(CSTR).The simulation results show the good performance of approaches and their capability to use in online application.

关键词： component Hybrid Systems mixed integer programming Particle Swarm Optimization Genetic Algorithm

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Nonlinear Optimization Subject to a System of Fuzzy Relational Equations with Max-min Composition

Nonlinear Optimization Subject to a System of Fuzzy Relation...

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第七届运筹学及其应用国际研讨会

作者： Pingke Li Shu-Cherng Fang Xingzhou Zhang Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University Raleigh NC 27695-7906 USA Department of Mathematical Sciences Tsinghua University College of Management Dalian University of Technology

In this paper we show that the problem of minimizing a nonlinear objective function subject to a system of fuzzy relational equations with max-min composition can be reduced to a 0-1 mixed integer programming problem. The reduction method can be extended to the case of fuzzy relational equations with max-T composition as well as those with more general composition.

关键词： Fuzzy relational equations fuzzy optimization mixed integer programming

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The Optimal Searcher Path Problem with a Visibility Criterion in Discrete Time and Space

The Optimal Searcher Path Problem with a Visibility Criterio...

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International Conference on Information Fusion

作者： Michael Morin Irene Abi-Zeid Pascal Lang Luc Lamontagne Patrick Maupin Department of Computer Science and Software Engineering Universite Laval Department of Operations and Decision Systems Universite Laval Defence Research and Development Canada

ISBN: (纸本)9780982443804

In this paper, the problem of path planning for a ground search unit looking for an object of unknown location is considered. As in the classical optimal searcher path problem, the probability of finding the search object is the main criterion of optimality and the search unit is constrained by the environment topology that influences its choices for a navigable path as well as its detection capabilities. This paper proposes an extension to the classical optimal searcher path problem in discrete time and space by integrating inter-region visibility as an additional criterion. This new formulation allows a refinement in the discretization of the space in which a ground search unit evolves. A general mixed-integer programming model is proposed, and experimental results with a moving object in grid environments are discussed.

关键词： Optimal searcher path problem Visibility Path planning Search theory mixed integer programming

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Research on Optimization Model for Urban Underground Logistics System

Research on Optimization Model for Urban Underground Logisti...

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第一届交通运输工程国际学术会议

作者： ZHU Jianjun1, HUANG Ke1, PU Yun2, and ZHANG Jin3 1College of Traffic & Transportation, Southwest Jiaotong University, Chengdu, P.R. China, 610031 2Graduate School, Southwest Jiaotong University, Chengdu, P.R. China, 610031 3College of Logistics, Southwest Jiaotong University, Chengdu, P.R. China, 610031

Underground logistics system (ULS) is one of the important freight transportation modes for many cities especially for the metropolis in the future, which will be a new way to solve the urban traffic congestion problem. So in this paper, the planning principles and methods of urban underground logistics system is presented. Some problems in ULS, such as the location methods and capacity determination of ULS network nodes, the route choice and traffic assignment of ULS network, are optimally analyzed and investigated. An integrated optimization model of urban underground logistics system is presented, which is solved by means of mixed integer programming and software LINDO. The freight transportation problem, the nodes location problem and the capacity determination problem are all solved with the integrated optimization model, which eliminates the limitation of partial optimization. Then the proposed model is applied to a case, which is worked out with the mixed integer programming algorithm and the software LINDO. Finally, the rationality of the model and the effectiveness of the algorithm are proved by the case.

关键词： Urban underground logistics system mixed integer programming Optimization model

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MAXIMUM VOLUME OUTLIER DETECTION AND ITS APPLICATIONS IN CREDIT RISK ANALYSIS

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2013年第5期22卷 1360012-1360012页

作者： Li, Shukai Ng, Wee Keong Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

Because of the scarcity and diversity of outliers, it is very difficult to design a robust outlier detector. In this paper, we first propose to use the maximum margin criterion to sift unknown outliers, which demonstrates superior performance. However, the resultant learning task is formulated as a mixed integer programming (MIP) problem, which is computationally hard. Therefore, we alter the recently developed label generating technique, which efficiently solves a convex relaxation of the MIP problem of outlier detection. Specifically, we propose an effective procedure to find a largely violated labeling vector for identifying rare outliers from abundant normal patterns, and its convergence is also presented. Then, a set of largely violated labeling vectors are combined by multiple kernel learning methods to robustly detect outliers. Besides these, in order to further enhance the efficacy of our outlier detector, we also explore the use of maximum volume criterion to measure the quality of separation between outliers and normal patterns. This criterion can be easily incorporated into our proposed framework by introducing an additional regularization. Comprehensive experiments on toy and real-world data sets verify that the outlier detectors using the two proposed criteria outperform existing outlier detection methods. Furthermore, our models are employed to detect corporate credit risk and demonstrate excellent performance.

关键词： Anomaly detection novelty detection credit risk analysis mixed integer programming maximum margin criterion maximum volume criterion

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Cyclic Scheduling of Cluster Tools with Equipment Front-End Module and Multifunctional Load Locks

Cyclic Scheduling of Cluster Tools with Equipment Front-End ...

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第26届中国控制与决策会议

作者： WANG Hao WANG Huan-gang XU Wen-li Department of Automation Tsinghua University

ISBN: (纸本)9781479937097

This paper proposes a cyclic scheduling method for cluster tools in semiconductor manufacturing with equipment front-end module and multifunctional load locks. In this special configured cluster tools, the equipment front-end module consists of an aligner, a signal-arm robot, and two load locks integrated with coolers. Hence, the two load locks both have multifunction of filling, pumping, and cooling wafers. The multifunctional load locks may become scheduling bottleneck for a certain specified recipe. To solve this problem, the Petri net models of the above configured cluster tool is developed first. Then, based on the Petri net models and state equations, a mixed integer programming model is presented, which can efficiently determine the optimal scheduling sequence in steady state. Through experiments, the effectiveness of the cyclic scheduling method proposed in this paper is verified.

关键词： Cluster Tools Cyclic Scheduling Petri Net mixed integer programming

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A Meta-Heuristic Computation Offloading Strategy for IoT Applications in an Edge-Cloud Framework 2019

A Meta-Heuristic Computation Offloading Strategy for IoT App...

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Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control

作者： Xuezhen Huang Yang Yang Xinglu Wu The First Research Institution of Ministry of Public Security of PRC Beijing China School of Computer and Information Technology BJTU Beijing China

ISBN: (纸本)9781450376617

Edge-cloud computing provides performance guarantees for IoT applications which are real-time or security sensitive. The new placement of edge-cloud services leverages resources both in Cloud Data Centers (CDC) and at the edge of the network. A computation task can be divided into subtasks and offloaded to different edge/cloud servers, which are donated as offloading destinations. Offloading destination heterogeneity and different architecture of Edge Data Center (EDC) and CDC bring challenges to computation offloading. One critical issue in edge-cloud computing is energy consumption in computation offloading. The existing computation offloading strategies either ignored energy consumption or ignored delay and/or security ***-heuristic strategies have been used widely to design heuristic resource allocation algorithms in CDC. This paper aims to explore meta-heuristic energy-efficient computation offloading (EE-CO) approaches with the objective to meet the delay and security constraints, while minimizing energy consumption. To achieve the goal, we investigated the performance of the Ant-Colony-Optimization (ACO) strategies combining with mixed integer programming (MIP). We propose an ACO-based computation offloading strategy, which including two algorithms, called EA-OMIP and EA-RMIP, respectively. The only difference of them is the construction method of integer programming models. Simulations are carried out to value the performance of proposed two algorithms. We also give an analysis of the experimental results in terms of the subtask acceptance ratio, revenue of the cloud service provider (CSP), and the resource utilization.

关键词： Computation offloading DelayK@Security Edge-cloud computing Energy-efficient IoT mixed integer programming

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Circle-Clustering: A New Heuristic Partitioning Method for the Clustering Problem

Circle-Clustering: A New Heuristic Partitioning Method for t...

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International Joint Conference on Neural Networks

作者： Gonzalo E. Paredes Luis S. Vargas Department of Electrical Engineering University of Chile

ISBN: (纸本)9781467314886

This paper present a novel method to perform clustering of time-series and static data. The method, named Circle-Clustering (CirCle), could be classified as a partition method that uses criteria from SVM and hierarchical methods to perform a better clustering. Different heuristic clustering techniques were tested against the CirCle method by using data sets from UCI Machine Learning Repository. In all tests, CirCle obtained good results and outperformed most of clustering techniques considered in this work. In addition, CirCle was tested against others heuristic techniques considering time-series data from electric feeders in Santiago, Chile's capital city. The optimal solution of the min-cut clustering optimization problem was solved in order to identify the optimal solution for 883 datasets. The results show that the proposed method obtains an average of 81% of well-classified samples in all datasets. Also, as compared to other algorithms, CirCle made a better classification in 98.7% of the datasets as compared to the Model-Base Best BIC. As compared to K-means, Robust K-means and Ward's methods the new algorithm classified better in nearly 68% of the datasets.

关键词： Component Heuristic clustering techniques mixed integer programming partitioning method Dataset Circles Cluster Analysis Heuristics dividing and cutting machine optimal solution time series data

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RLC_ACS: AN IMPROVED ANT COLONY ALGORITHM FOR VRPSDP

RLC_ACS: AN IMPROVED ANT COLONY ALGORITHM FOR VRPSDP

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： XUE-CHAO ZHENG TAO ZHANG WEN-XIN TIAN YUE-JIE ZHANG School of Information Management and Engineering Shanghai University of Finance and Economics Shan Information Office in Center of Modem Education Technology of Shanghai Finance University Shanghai Department of Computer Science and Engineering Fudan University Shanghai Key Laboratory of Intelli

This paper studies the reverse logistics vehicle routing problem with simultaneous distribution of the goods and collection of the ones as same as the initial state by a homogeneous fleet of vehicles with capacities constraint and maximum distance constraint under a single *** presents a mixed integer programming *** the complex feature of the fluctuating vehicle load, this paper uses an Ant Colony System (ACS) approach combining with the pheromone updating strategy of ASRank and MM AS ant *** a heuristic factor based on residual loading capacity is designed to improve the vehicle loading ***, the paper proposes a candidate list based on saving-ant, and uses a local search with sweeping in the process of tour improvement to accelerate the *** making Comparisons with different algorithms of other researches, the experimental study indicates that RLC_ACS could obtain the satisfactory solution in the acceptable time.

关键词： VRPSDP Ant colony system mixed integer programming

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