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Highly Reliable Transmission and Channel Assignment for CR-IoT Networks

IEEE INTERNET OF THINGS JOURNAL 2022年第5期9卷 3945-3953页

作者： Halloush, Rami Salameh, Haythem Bany Musa, Ahmed Halloush, Mohammed Abu Shunnar, Marwa Yarmouk Univ Dept Telecommun Engn Irbid 21163 Jordan Al Ain Univ Dept Network & Commun Engn Al Ain U Arab Emirates Amer Univ Middle East Coll Engn & Technol Egaila 15453 Kuwait Yarmouk Univ Dept Comp Engn Irbid 21163 Jordan

In this article, we propose a highly reliable transmission strategy based on exploiting channel diversity for Internet of Things (IoT) networks equipped with cognitive radio (CR) capabilities (referred to as CR-IoT networks). Specifically, we investigate the channel-assignment problem with the objective of minimizing the number of assigned channels for each CR transmission subject to link reliability, data rate, and success probability constraints. We use the theory of continuous-time Markov chains to derive mathematical expressions for channel availability and reliability. These expressions are employed in a binary linear program model to formulate the channel assignment optimization problem, which is suboptimally solved using a polynomial-time sequential fixing procedure. Theoretical performance analysis of the proposed channel assignment scheme is derived in terms of a lower bound on the achievable probability of successful packet transmission. Through simulation experiments, we demonstrate significant performance enhancement compared to existing reliability-unaware CR channel assignment algorithms.

关键词： NOMA System performance Reliability theory Channel allocation Performance gain mathematical models Internet of Things Cognitive radio (CR) Internet-of-Things (IoT) Markov chain mathematical programming reliability

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Automatic Scenario Generation for Robust Optimal Control Problems 22

Automatic Scenario Generation for Robust Optimal Control Pro...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Zagorowska, M. Falugi, P. O'Dwyer, E. Kerrigan, E. C. Swiss Fed Inst Technol Automat Control Lab Zurich Switzerland Imperial Coll London Dept Elect & Elect Engn London England Imperial Coll London Dept Chem Engn London England Imperial Coll London Dept Aeronaut London England Univ East London Dept Engn & Construct London England

ISBN: (纸本)9781713872344

Existing methods for nonlinear robust control often use scenario-based approaches to formulate the control problem as nonlinear optimization problems. Increasing the number of scenarios improves robustness, while increasing the size of the optimization problems. Mitigating the size of the problem by reducing the number of scenarios requires knowledge about how the uncertainty affects the system. This paper draws from local reduction methods used in semi-infinite optimization to solve robust optimal control problems with parametric uncertainty. We show that nonlinear robust optimal control problems are equivalent to semi-infinite optimization problems and can be solved by local reduction. By iteratively adding interim globally worst-case scenarios to the problem, methods based on local reduction provide a way to manage the total number of scenarios. In particular, we show that local reduction methods find worst case scenarios that are not on the boundary of the uncertainty set. The proposed approach is illustrated with a case study with both parametric and additive time-varying uncertainty. The number of scenarios obtained from local reduction is 101, smaller than in the case when all 2(14+3x192) boundary scenarios are considered. A validation with randomly drawn scenarios shows that our proposed approach reduces the number of scenarios and ensures robustness even if local solvers are used. Copyright (C) 2023 The Authors.

关键词： Optimization mathematical programming Trajectory optimization Uncertainty Iterative methods Numerical simulation Robust control

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Geotechnical Strain Localization Analysis Based on Micropolar Continuum Theory Considering Evolution of Internal Characteristic Length

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INTERNATIONAL JOURNAL OF GEOMECHANICS 2022年第8期22卷 06022016-06022016页

作者： Tang, Jianbin Wang, Xiangnan Chen, Xi Wang, Dongyong Yu, Yuzhen Beijing Jiaotong Univ Minist Educ Key Lab Urban Underground Engn Beijing 100044 Peoples R China Tsinghua Univ Dept Hydraul Engn State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China Beijing Univ Civil Engn & Architecture Sch Civil & Transportat Engn Beijing 100044 Peoples R China Tsinghua Univ State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China

Within the framework of second-order cone programming optimized micropolar continuum finite-element method (CosFEM-SOCP), the geotechnical strain localization can be adequately modeled. In most existing literatures, however, the constant internal characteristic length l(c) has been adopted and less attention has been paid to the evolution of l(c). To more accurately predict the strain localization, response, and stability of a geotechnical system, one relationship for evolving l(v) that relies on the equivalent plastic strain is implemented and investigated. Based on one homogeneous slope example and one rigid strip footing example, it was found that geotechnical stability may not be significantly affected by evolving l(v), indicating that constant l(c) can be simply applied to geotechnical stability analysis. For the rigid strip footing problem, nevertheless, the effects of evolving l(v) on the pressure-displacement response curves should not be ignored, and the influence range of the shear band predicted by CosFEM-SOCP with evolving l(v) is generally smaller than that predicted by CosFEM-SOCP with constant l(c). Consequently, in order to more accurately predict the pressure-displacement response curves and the failure zone, the evolving l(v) will be adequately assessed and modeled.

关键词： Geotechnical stability Strain localization Shear band Micropolar continuum Evolving internal characteristic length Second-order cone programming mathematical programming Optimization

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Learning Nonlinear Feature Mapping via Constrained Non-Convex Optimization for Unsupervised Salient Object Detection

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IEEE ACCESS 2022年 10卷 40743-40752页

作者： Wang, Shigang Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

Mimicking the biological visual attention mechanism to detect salient objects in images has been widely studied in recent years. Most of the existing computational models rely on external learning for saliency prediction, which however lack robustness in diversified scenes. In this paper, an unsupervised learning model is proposed to detect salient objects by fully exploiting the internal information of the scene. Specifically, we formulate saliency detection as a mathematical programming problem with which to learn a nonlinear feature mapping from multi-view features to saliency scores. The optimization objective is to maximize the between-class variance of the attended and background regions in the resulting saliency maps, which is statistically optimal. Moreover, to solve the non-convex constrained mathematical programming problem, a hybrid external point method based particle swarm optimization algorithm is developed to find the optimal solution in a rapid manner. Finally, extensive experiments are conducted on four classical saliency benchmark datasets to test the effectiveness of the proposed method and it shows superior qualitative and quantitative performance than the other 16 state-of-the-art unsupervised saliency models.

关键词： Feature extraction Optimization Biological system modeling Saliency detection Object detection Visualization Computational modeling mathematical programming external point method particle swarm optimization (PSO) salient object detection unsupervised learning

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Scheduling trucks and storage operations in a multiple-door cross-docking terminal considering multiple storage zones

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第4期60卷 1153-1177页

作者： Chargui, Tarik Bekrar, Abdelghani Reghioui, Mohamed Trentesaux, Damien Univ Polytech Hauts France UMR CNRS 8201 LAMIH F-59313 Le Mt Houy Valenciennes France Univ Abdelmalek Essaadi Natl Sch Appl Sci RSAID Tetouan 93000 Morocco

Cross-docking is a logistics process in which products are unloaded through receiving docks and then transferred to shipping docks with almost no storage in between. In this paper, a mixed integer linear programming model (MILP) is proposed to optimise the scheduling, storage, assignment and sequencing of trucks at receiving and shipping docks for a problem inspired from a multiple-door cross-dock facility of an industrial partner with multiple temporary storage zones. The multiple storage zones are separated and located in the centre of the cross-dock handling different types of products. The objective is to minimise the total tardiness of inbound and outbound trucks. A heuristic (H) is proposed to find an initial solution. Then, three meta-heuristics are developed, namely Random Search (RS), Tabu Search (TS) and Simulated Annealing (SA) to improve the scheduling of trucks in order to minimise the tardiness of inbound and outbound trucks. Experimental results indicate that the three meta-heuristics (RS, TS and SA) are able to find good quality results within reasonable computational times. Finally, since SA showed the best performance compared to RS and TS, it was chosen to be compared to the current manual method using discrete event simulation.

关键词： Cross-dock scheduling mathematical programming multiple storage zones multiple products types Tabu search simulated annealing

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Use of multi-objective examination model to sensitivity analysis for sustainability with three new practices

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JOURNAL OF MODELLING IN MANAGEMENT 2023年第3期18卷 637-658页

作者： Simsek, Ahmet Bahadir Gumushane Univ Fac Hlth Sci Dept Hlth Care Management Gumushane Turkey

Purpose The study has two main objectives. The first is to introduce three new examination policy practices: overbooked exam rooms, a substitute invigilator and an inheritance of the invigilation distribution. The second is the use of the examination model to determine the sustainability of the system against expected changes and to obtain long-term strategic information. This study aims to obtain managerial insights on the sustainability of the examination system with the newly introduced practices;it does not claim to propose solutions for large-scale problems. Design/methodology/approach This study introduces a multi-objective mathematical model that contains a couple of constraints that belong to new practices. To check the sustainability of the system, this study gets help from sensitivity analysis, which informs the decision-maker about how the results will react to parameter changes. The authors perform all experiments in the Python/Gurobi modeling environment. Findings The results demonstrate that the proposed practices are effective in the efficient use of resources. Also, this study shows that the examination model can be used as a stress test so that the weaknesses of the system can be identified and long-term strategic information can be obtained. Originality/value The contribution of this study is twofold: the introduction of examination policy practices that are overbooked exam rooms, a substitute invigilator and an inheritance of the invigilation distribution. Overbooked exam rooms aim to use resources effectively, taking into account the possibility of temporarily increasing the exam room capacity. The substitute invigilator protects the examination process against sudden events. The inheritance of the invigilation distribution helps to ensure fairness;and the use of the examination model to obtain managerial insights on expectations. This allows testing the validity of existing policies and making necessary changes, taking into account possible chan

关键词： mathematical programming Management Education Sensitivity Exam and invigilator schedule Examination policy practices Sustainability of the examination system Managerial insights

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Optimization Strategies for Resource-Constrained Project Scheduling Problems in Underground Mining

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INFORMS JOURNAL ON COMPUTING 2022年第6期34卷 3042-3058页

作者： Hill, Alessandro Brickey, Andrea J. Cipriano, Italo Goycoolea, Marcos Newman, Alexandra Calif Polytech State Univ San Luis Obispo Dept Ind & Mfg Engn San Luis Obispo CA 93407 USA South Dakota Sch Mines & Technol Min Engn & Management Rapid City SD 57701 USA Univ Adolfo Ibanez Alicanto Labs Santiago 7941169 Chile Univ Adolfo Ibanez Sch Business Santiago 7941169 Chile Colorado Sch Mines Dept Mech Engn Golden CO 80401 USA

Effective computational methods are important for practitioners and researchers working in strategic underground mine planning. We consider a class of problems that can be modeled as a resource-constrained project scheduling problem with optional activities;the objective maximizes net present value. We provide a computational review of math programming and constraint programming techniques for this problem, describe and implement novel problem-size reductions, and introduce an aggregated linear program that guides a list scheduling algorithm running over unaggregated instances. Practical, large-scale planning problems cannot be processed using standard optimization approaches. However, our strategies allow us to solve them to within about 5% of optimality in several hours, even for the most difficult instances.

关键词： underground mine planning resource-constrained project scheduling constraint programming mathematical programming

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Optimizing Job/Task Granularity for Metagenomic Workflows in Heterogeneous Cluster Infrastructures

Optimizing Job/Task Granularity for Metagenomic Workflows in...

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Workshops of the EDBT/ICDT 2024 Joint Conference, EDBT/ICDT-WS 2024

作者： Mohammadi, Somayeh PourKarimi, Latif Zschäbitz, Manuel Aretz, Tristan De Mecquenem, Ninon Leser, Ulf Reinert, Knut Department of Mathematics and Computer Science Freie Universität Berlin Germany Department of Mathematics and Computer Science Razi University Kermanshah Iran Department of Mathematics and Computer Science Humboldt-Universität zu Berlin Berlin Germany

Data analysis workflows are popular for sequencing activities in large-scale and complex scientific processes. Scheduling approaches attempt to find an appropriate assignment of workflow tasks to the computing nodes for minimizing the makespan in heterogeneous cluster infrastructures. A common feature of these approaches is that they already know the structure of the workflow. However, for many workflows, a high degree of parallelization can be achieved by splitting the large input data of a single task into chunks and processing them independently. We call this problem task granularity, which involves finding an assignment of tasks to computing nodes and simultaneously optimizing the structure of a bag of tasks. Accordingly, this paper addresses the problem of task granularity for metagenomic workflows. To this end, we first formulated the problem as a mathematical model. We then solved the proposed model using the genetic algorithm. To overcome the challenge of not knowing the number of tasks, we adjusted the number of tasks as a factor of the number of computing nodes. The procedure of increasing the number of tasks is performed interactively and evolutionarily. Experimental results showed that a desirable makespan value can be achieved after a few steps of the increase. © 2024 Copyright © 2024 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： mathematical programming

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Auction-based network slicing for 5G RAN 9

Auction-based network slicing for 5G RAN

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9th IEEE International Conference on Network Softwarization (IEEE NetSoft) - Boosting Future Networks through Advanced Softwarization

作者： Zorello, Ligia M. Moreira Eradatmand, Kazem Troia, Sebastian Pattavina, Achille Zhang, Yingqian Maier, Guido Politecn Milan Milan Italy TU Eindhoven Eindhoven Netherlands

ISBN: (纸本)9798350399806

Network slicing is an important characteristic of 5G/6G networks that increases flexibility and enables different applications over a single infrastructure. The physical resources are partitioned to create virtualized networks, each dedicated to services with specific requirements. Several entities participate in network slicing, including Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and users. An MNO owns the physical network infrastructure and the resources. MVNOs lease resources from the MNO and operate as service providers towards their subscribers. The goal of this work is to optimize the end-to-end network slicing process to provide services to users with a fair sharing of resources. We model this problem as a hierarchical combinatorial auction with a modified Vickrey-Clarke-Groves pricing mechanism. In the upper-level auction, an MNO is the seller supplying Network Slice to several MVNOs, who act as the bidders. In the lower-level auction, each MVNO holds an auction as a seller delivering services to their subscribed end-users, who play the role of bidders. We formulate and solve theWinner Determination Problem using mathematical programming and heuristic algorithms. The simulations show that the model can achieve fair sharing of resources, and it enables improving the MNO and MVNO revenue.

关键词： mathematical programming

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Metaphor-based algorithms for learning the preference model parameters of FlowSort from large sets of assignment examples

Metaphor-based algorithms for learning the preference model ...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Handke, Adam Jozwiak, Damian Kaminski, Piotr Wisniewski, Michal Kadzinski, Milosz Poznan Univ Tech Inst Comp Sci Poznan Poland

ISBN: (纸本)9798400701207

We consider the problem of deriving parameters of the preference model employed in the multiple criteria sorting method called Flow-Sort. We propose a suite of preference learning algorithms based on differential evolution and simulated annealing, their combinations with mathematical programming, and a dedicated heuristic. They are tested on various monotonic benchmark datasets and compared in terms of 0/1 loss. The evolutionary algorithm and the dedicated heuristic prove competitive against state-of-the-art preference learning methods. The former attains better results when coupled with boundary profiles for all considered datasets. For other methods, there is no clear indication that using the class limits is more advantageous than class prototypes.

关键词： Multiple Criteria Decision Analysis Preference learning Evolutionary algorithm mathematical programming Assignment examples

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