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The integration of resource allocation and time buffering for bi-objective robust project scheduling

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2020年第13期58卷 3839-3854页

作者： Liang, Yangyang Cui, Nanfang Hu, Xuejun Demeulemeester, Erik Hubei Univ Econ Res Ctr Hubei Logist Dev Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan Hubei Peoples R China Hunan Univ Business Sch Changsha Hunan Peoples R China Katholieke Univ Leuven Fac Econ & Business Dept Decis Sci & Informat Management Res Ctr Operat Management Leuven Belgium

In the recent decades, the recognition that uncertainty lies at the heart of modern project management has induced considerable research efforts on robust project scheduling for dealing with uncertainty in a scheduling environment. The literature generally provides two main strategies for the development of a robust predictive project schedule, namely robust resource allocation and time buffering. Yet, the previous studies seem to have neglected the potential benefits of an integration between the two. Besides, few efforts have been made to protect simultaneously the project due date and the activity start times against disruptions during execution, which is desperately demanded in practice. In this paper, we aim at constructing a proactive schedule that is not only short in time but also less vulnerable to disruptions. Firstly, a bi-objective optimisation model with a proper normalisation of the two components is proposed in the presence of activity duration variability. Then a two-stage heuristic algorithm is developed which deals with a robust resource allocation problem in the first stage and optimally determines the position and the size of time buffers using a simulated annealing algorithm in the second stage. Finally, an extensive computational experiment on the PSPLIB network instances demonstrates the superiority of the combination between resource allocation and time buffering as well as the effectiveness of the proposed two-stage algorithm for generating proactive project schedules with composite robustness.

关键词： robust project scheduling bi-objective optimisation robust resource allocation time buffering two-stage algorithm

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A two-stage Rapid Trajectory Optimization algorithm for Hypersonic Entry

A Two-Stage Rapid Trajectory Optimization Algorithm for Hype...

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IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)

作者： Wang, Jinbo Cui, Naigang Wei, Changzhu Harbin Inst Technol Aerosp Engn Dept Harbin 150001 Peoples R China

ISBN: (纸本)9781538611715

A novel two-stage rapid trajectory optimization algorithm is presented to solve the hypersonic entry trajectory optimization problem. To circumvent the issue of initial guess of nonlinear programming (NLP) based methods, and to increase the computation speed of the algorithm to accommodate online applications, a convex optimization based algorithm is designed as an initial trajectory generator, which is the first stage of the two-stage algorithm. The second stage is the NLP-based pseudospectral method. Numerical experiments show that the overall computation time of the two-stage algorithm is much less than that of single pseudospectral method.

关键词： Convex optimization Hypersonic entry Pseudospectral method Rapid trajectory optimization two-stage algorithm

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HCFS: A Density Peak Based Clustering algorithm Employing A Hierarchical Strategy

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IEEE ACCESS 2019年 7卷 74612-74624页

作者： Zhuo, Linlin Li, Kenli Liao, Bo Li, Hao Wei, Xiaohui Li, Keqin Hunan Univ Coll Comp Sci & Elect Engn Changsha 410008 Hunan Peoples R China Natl Supercomp Ctr Changsha 410082 Hunan Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Clustering, which explores the visualization and distribution of data, has recently been widely studied. Although current clustering algorithms such as DBSCAN, can detect the arbitrary-shape clusters and work well, the parameters involved in these methods are often difficult to determine. Clustering using a fast search of density peaks is a promising technique for solving this problem. However, the current methods suffer from the problem of uneven distribution within local clusters. To solve this problem, we propose a new density peak based clustering algorithm employing a hierarchical strategy, namely, HCFS, which consists mainly of two stages. In the first stage, the HCFS estimates the density and distance of each point. The points with higher density and distance are selected as candidate centers, and then subclusters centered on them are further obtained. In the second stage, considering that adjacent subclusters based on certain candidate centers are highly similar and connected within the same cluster, we propose a new mechanism for measuring dissimilarity and connectivity between the subclusters. Those highly similar and connected subclusters are merged to increase the dissimilarity between different clusters and to obtain the final clustering results. The experiments conducted on a large number of datasets show that our method can effectively identify unevenly distributed clusters and yield better or comparable performance for different datasets.

关键词： Cluster candidate center density peak based hierarchical merge subclusters two-stage algorithm uneven distribution within local clusters

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Modeling and Solution of Joint Storage Space Allocation and Handling Operation for Outbound Containers in Rail-Water Intermodal Container Terminals

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IEEE ACCESS 2019年 7卷 55142-55158页

作者： Chang, Yimei Zhu, Xiaoning Haghani, Ali Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA

Storage space allocation and handling operation problems are two main crucial problems in container terminals. Early research efforts, however, are seldom devoted to studying them together. Therefore, this paper considers these two problems simultaneously for outbound containers in rail-water intermodal container terminals (RWICTs), where rail-mounted gantry cranes, inner trucks, and quay cranes are involved. A two-stage problem is proposed: stage 1 is to determine locations of the containers and reduce the overlapping amount, considering container weight, departure time, destination ports, and containers left from earlier planning periods in railway container yards, according to the locations of containers from stage 1;and stage 2 aims at obtaining optimal job sequences of different types of equipment and minimizing makespans of handling operations, considering some operational constraints, particularly rehandling time and inner truck congestion. To solve the problem, a two-stage heuristic algorithm is proposed, where the rolling planning horizon and a new update strategy are introduced. A heuristic algorithm is introduced in stage 1 and a novel two-layer genetic algorithm is proposed in stage 2, which introduces proximity principles and the reselection operation. Afterward, the results from stage 2 are used to resolve the first stage problem, while the problem in stage 2 is also resolved using the new results from stage 1. This iterative process continues until there are no more improvements in stage 1. Finally, the results of the computational experiments indicate that the proposed model and solution approaches are effective and efficient in solving the two-stage problem for outbound containers in RWICTs.

关键词： Rail-water intermodal transportation storage space allocation handling operation two-stage algorithm container terminal

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A two-stage optimization approach for subscription bus services network design: the China case

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PUBLIC TRANSPORT 2019年第3期11卷 589-616页

作者： Huang, Wencheng Shuai, Bin Antwi, Eric Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Natl United Engn Lab Integrated & Intelligent Tra Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data App Chengdu 611756 Sichuan Peoples R China

Subscription bus services (SBS) is a convenient and low-carbon rapid transport mode for passengers' daily commute. The network design becomes a vital problem because it closely relates to both operators' profit and passengers' daily convenient traveling. In this paper, a two-stage model is formulated to optimize the subscription bus services network design (SBSND). During the first stage we minimize the total service distance and the number of vehicles as a single target objective function;vehicle capacity utilization rate, service time, ratio of service distance and linear distance between origin and destination are limited by means of constraints. During the second stage we share the same objective function and similar constraints used during the first stage, but the parameter settings of the constraints are different. Correspondingly, a two-stage algorithm is also designed to solve the SBSND problem. Firstly, we obtain the possible service lines, match the passengers and bus capacity. Next, we use Dijkstra to obtain the shortest SBS operation lines. Finally, we form the SBS network. The three phases are the main processes about the algorithm during both stages. The comparison between existing SBS in Chengdu city and the optimized SBS shows the high efficiency of the optimization model and algorithm formulated in this paper: the operation line length increases from 250.6 to 300.9 km;only 40 passengers have no SBS after optimization;the average operation time is reduced from 56.8 to 50.2 min.

关键词： Subscription bus service Network design optimization two-stage optimization model two-stage algorithm

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Data center supply chain configuration design: A two-stage decision approach

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SOCIO-ECONOMIC PLANNING SCIENCES 2019年 66卷 119-135页

作者： Ibn Faiz, Tasnim Noor-E-Alam, Md Northeastern Univ Dept Mech & Ind Engn 360 Huntington Ave Boston MA 02115 USA

Data centers are special-purpose facilities that enable customers to perform cloud based real-time online transactions and rigorous computing operations. Service levels of data center facilities are characterized by response time between query and action, which to a large extent depends on data center location and data travel distance. Another aspect of service level is resource up-time availability, which is determined by data center configuration. Data center location and configuration decisions are, therefore, of great significance to ensure uninterrupted operations in customers of manufacturing and service industries relying on cloud-based computing resources. In this study, following a grid-based location approach, we present two mixed integer linear programming models for capacitated single-source data center location-allocation problems. The first model provides optimal locations, capacities and configurations of data centers, and allocation of demands to open facilities when there is no existing facilities in the region. Our second model considers the decision problem of meeting new demand when the existing demand is met by the already opened facilities. We term these newly arrived demand as replication demand, which results either from emergence of new users of existing customers at distant locations in the future, or as a means of increasing data resilience by creating data replication as a backup. To solve the decision problem for meeting primary and replication demand optimally, we propose a two-stage decision algorithm. The algorithm provides optimal locations, capacities and configurations for new data centers, capacity addition decisions to the existing facilities and subsequent allocation of demands. Both models and solution algorithm are implemented using AMPL programming language and solved with CPLEX solver. The models are found to be scalable and capable to provide high quality solutions in reasonable time.

关键词： Data center location Supply chain configuration design Mixed integer linear program Grid-based location problem two-stage algorithm

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Variable selection methods for identifying predictor interactions in data with repeatedly measured binary outcomes

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JOURNAL OF CLINICAL AND TRANSLATIONAL SCIENCE 2021年第1期5卷 e59页

作者： Wolf, Bethany J. Jiang, Yunyun Wilson, Sylvia H. Oates, Jim C. Med Univ South Carolina Dept Publ Hlth Sci 135 Cannon St Charleston SC 29425 USA George Washington Univ Biostat Ctr Milken Inst Sch Publ Hlth Rockville MD USA Med Univ South Carolina Dept Anesthesia & Perioperat Med Charleston SC 29425 USA Med Univ South Carolina Dept Med Div Rheumatol & Immunol Charleston SC 29425 USA

Introduction: Identifying predictors of patient outcomes evaluated over time may require modeling interactions among variables while addressing within-subject correlation. Generalized linear mixed models (GLMMs) and generalized estimating equations (GEEs) address within-subject correlation, but identifying interactions can be difficult if not hypothesized a priori. We evaluate the performance of several variable selection approaches for clustered binary outcomes to provide guidance for choosing between the methods. Methods: We conducted simulations comparing stepwise selection, penalized GLMM, boosted GLMM, and boosted GEE for variable selection considering main effects and two-way interactions in data with repeatedly measured binary outcomes and evaluate a two-stage approach to reduce bias and error in parameter estimates. We compared these approaches in real data applications: hypothermia during surgery and treatment response in lupus nephritis. Results: Penalized and boosted approaches recovered correct predictors and interactions more frequently than stepwise selection. Penalized GLMM recovered correct predictors more often than boosting, but included many spurious predictors. Boosted GLMM yielded parsimonious models and identified correct predictors well at large sample and effect sizes, but required excessive computation time. Boosted GEE was computationally efficient and selected relatively parsimonious models, offering a compromise between computation and parsimony. The two-stage approach reduced the bias and error in regression parameters in all approaches. Conclusion: Penalized and boosted approaches are effective for variable selection in data with clustered binary outcomes. The two-stage approach reduces bias and error and should be applied regardless of method. We provide guidance for choosing the most appropriate method in real applications.

关键词： Variable selection interactions penalized regression boosting two-stage algorithm

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Mode-based energy storage control approach for residential photovoltaic systems

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IET SMART GRID 2019年第1期2卷 69-76页

作者： Henri, Gonzague Lu, Ning Carrejo, Carlos North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA Total Energy Management Syst Dept Paris France

This study presents a novel mode-based energy storage control approach. Assuming that an energy storage device (ESD) is equipped with a set of predetermined real-time control modes, the dispatch objective is to select an optimal mode instead of a continuous charging or discharging power value. A two-stage algorithm is developed for mode selection. In the first stage, a sliding 24h economic model predictive control algorithm is used to determine the power outputs of the ESD for the next 24h. In the second stage, the output sign for the next time step determines the class of modes to be elected (charging or discharging). The information from the first stage is used to compute the total cost for each selected mode. The mode with the lowest day-ahead cost is chosen. The residential electricity consumption data collected in the PECAN Street Project is used in the simulation to validate the performance of the proposed algorithm. Simulation results show that using a mode-based approach reduces the sensitivity to forecasting errors along with load and solar variability. The algorithm performance is consistent across different load patterns.

关键词： photovoltaic power systems energy storage power consumption predictive control load dispatching continuous charging two-stage algorithm mode selection economic model predictive control algorithm mode-based energy storage control approach energy storage device real-time control modes optimal mode residential electricity consumption data PECAN Street Project forecasting errors solar variability load patterns time 24 0 hour

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two-stage Segmentation of SAR Images Distorted by Additive Noise with Uncorrelated Samples 39

Two-Stage Segmentation of SAR Images Distorted by Additive N...

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39th IEEE International Conference on Electronics and Nanotechnology (ELNANO)

作者： Vyshnevyi, Serhii Natl Tech Univ Ukraine Dept Radio Engn Igor Sikorsky Kyiv Polytech Inst Kiev Ukraine

ISBN: (纸本)9781728120652

In this paper the algorithm of SAR images segmentation is investigated, which is developed on the basis of two-stage approach the essence of which is in conducting of one-dimensional processing independently along rows and columns followed by combining the obtained results and forming of segmented image. Analysis of the algorithm is performed using simulation while processing of test synthesized image.

关键词： image segmentation two-stage algorithm SAR image a posteriori probability

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A two-stage Rapid Trajectory Optimization algorithm for Hypersonic Entry

A Two-Stage Rapid Trajectory Optimization Algorithm for Hype...

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IEEE Chinese Guidance, Navigation and Control Conference (CGNCC)

作者： Jinbo Wang Naigang Cui Changzhu Wei Aerospace Engineering Department Harbin Institute of Technology Harbin China

ISBN: (数字)9781538611715

ISBN: (纸本)9781538611722

关键词： Hypersonic entry two-stage algorithm Rapid trajectory optimization Convex optimization Pseudospectral method trajectory optimization algorithms Convex function double-stage hypersonic speed computational time entry

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