Orlin, James · DSpace@MIT (Massachusetts Institute of Technology) · 2003
15.082J/6.855J is an H-level graduate subject in the theory and practice of network flows and its extensions. Network flow problems form a subclass of linear programming problems with applications to transportation, logistics, manufacturing, computer science, project management, finance as well as a number of other domains. This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following: the shortest path problem, the maximum flow problem, the minimum cost flow problem, and the multi-commodity flow problem.
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Thomas L. Magnanti · 2001
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Tomas Fencl, Jan Bílek · 2007
Abstract:- This document deals with a design of a physical and a logical topology of communication networks that are applied in the control engineering. The design of the physical topology works with aspects of demands for redundant links between nodes. Thanks to knowledge of the physical topology, we can design the logical topology according to permitted delays of delivered communication frames. The whole procedure of a network design proceeds like an iterative task on the basis of an evolution algorithm.
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WORLD SCIENTIFIC eBooks · 2023
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Pardalos, Panos M., Hearn, Donald W., Hager, William W. · Lecture notes in economics and mathematical systems · 1997
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SpringerReference · 2011
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Pengcheng He, Yijia Tang, Fan Xu et al. · IEEE Transactions on Mobile Computing · 2025
The rapid development of wireless communications has driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model’s generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model’s applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks.
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VM Grout, PW Sanders · Computer Communications · 1988
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Hoang Nghia Le · Journal of Telecommunications and Information Technology · 2014
Fiber To The Home (FTTH) is the most ambitious among optical technologies applied in the access segment of telecommunications networks. The main issues of deploying FTTH are the device price and the installation cost. Whilst the costs of optical devices are gradually decreasing, the cost of optical cable installation remains challenging. In this paper, the problem of optimization that has practical application for FTTH networks is presented. Because the problem is Non-deterministic polynomial-time hard (NP-hard), an approximation algorithm to solve it is proposed. The author has developed the algorithm in a C# program in order to analyze its performance. The analysis confirms that the algorithm gains near-optimal results with acceptable time consumption. Therefore, the algorithm to be applied in a network design tool for FTTH network planning is proposed.
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Chen Chen, Ruiyue Peng, Lei Ying et al. · 2018
Network connectivity optimization, which aims to manipulate network connectivity by changing its underlying topology, is a fundamental task behind a wealth of high-impact data mining applications, ranging from immunization, critical infrastructure construction, social collaboration mining, bioinformatics analysis, to intelligent transportation system design. To tackle its exponential computation complexity, greedy algorithms have been extensively used for network connectivity optimization by exploiting its diminishing returns property. Despite the empirical success, two key challenges largely remain open. First, on the theoretic side, the hardness, as well as the approximability of the general network connectivity optimization problem are still nascent except for a few special instances. Second, on the algorithmic side, current algorithms are often hard to balance between the optimization quality and the computational efficiency. In this paper, we systematically address these two challenges for the network connectivity optimization problem. First, we reveal some fundamental limits by proving that, for a wide range of network connectivity optimization problems, (1) they are NP-hard and (2) (1 -1/e) is the optimal approximation ratio for any polynomial algorithms. Second, we propose an effective, scalable and general algorithm (CONTAIN) to carefully balance the optimization quality and the computational efficiency.
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Guanyong Liu (8499330), Shuai Zhang (115662), Lixin Wang (343011) et al. · Figshare · 2025
<div> With the rapid development of industrial automation, traditional manual detection methods are inefficient and error-prone, which cannot meet the needs of modern production for high efficiency and high precision. Therefore, it is particularly important to develop a mechanical automatic inspection system that can automatically identify food packaging defects. In this study, aiming at the limitations of existing technologies in identifying small targets and subtle defects, an enhanced YOLOv5-based model for detecting food packaging flaws is introduced. Firstly, we integrated a Convolutional Attention module (CBAM) to enhance the model’s attention on crucial image features. This mechanism prioritizes significant features by weighting the feature map in channel and spatial dimensions, which improves accuracy in detecting minor defects and small objects. Secondly, feature fusion across scales is achieved with pyramid and aggregation networks, so that the model can capture defects of different sizes at the same time, which enhances the recognition ability of diverse defects in food packaging. In addition, this study also optimizes the backbone network structure of YOLOv5. By integrating the streamlined YOLOv5s model and adding an Adaptive Spatial Feature Fusion module (ASFF), the model’s ability to blend features from different scales was enhanced. In this study, 7400 images with 512×512 resolutions were applied to develop the proposed model. The experimental results show that the improved model outperforms the original YOLOv5 model in terms of Accuracy (Ac), Recall (Re), and F1 score, with values of 0.96, 0.94, and 0.94, respectively, effectively improving the automation and accuracy of food packaging defect detection when compared with YOLOv5+ASFF (Ac=0.94, Re=0.95, and F1=0.94), original YOLOv5 (Ac=0.82, Re=0.85, and F1=0.88), and YOLOv5+CBAM (Ac=0.88, Re=0.9, and F1=0.89). Additionally, the present performance of an improved YOLOv5 model (CBAM+Fusion Pyramid Network (FPN)+Path Aggregation network (PANet)+ASFF) was significantly comparable to the related research works. </div>
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Lucia Fogelstaller · 2014
Traffic network optimization aims to improve a given road network by maximizing the utility for traffic participants. The present thesis investigates a solution-based approach for tackling the traffic network optimization problem by modifying an established metaheuristic - the genetic algorithm - in such a way that mutation does not appear at random but with the help of a local optimization procedure. In comparison to the standard genetic algorithm, the approach using local optimization in form of a neighbourhood search should result in increased solutions. Primarily, we have to make a statement regarding how well a network performs with respect to traffic participants. One opportunity might be to approximate the utility based on the travel time of road users. This evaluation forms the basis of the neighbourhood search. Finally, by means of practical data we compare and assess the aforementioned enhanced approach with the standard genetic algorithm in terms of quality and development.
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Alexandre Blais · Physical Review A · 2001
In many candidate designs of solid-state quantum computers, interactions between qubits are limited to a small number of neighboring qubits. Taking into account this limitation we describe how quantum algorithms can be executed efficiently on these designs. We illustrate our results with the quantum Fourier transform for which linear depth networks are obtained. The concepts presented in this work can be applied to all quantum algorithms to reduce considerably the coherence time needed for their execution.
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Computer-Aided Design · 1977
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Physics Subject Headings (PhySH) · 2024
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Fang Zhao, Ike Ubaka, Albert Gan · Transportation Research Record Journal of the Transportation Research Board · 2005
This paper presents a mathematical methodology for transit route network optimization. The goal is to provide an effective computational tool for optimization of a large-scale transit route network. The objectives are to minimize transfers and maximize service coverage. Formulation of the method consists of three parts: representation of transit route network solution search spaces, representation of transit route and network constraints, and a stochastic search scheme capable of finding the expected global optimal result on the basis of an integrated simulated annealing, tabu, and greedy search algorithm. The methodology has been tested with published solutions to benchmark problems and applied to a large-scale realistic network optimization problem in Miami–Dade County, Florida.
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Jouian Zavari · TSpace (University of Toronto) · 2010
Abstract: This project report emphasizes the importance and challenges of facility location decisions for an efficient and effective movement of products from distributing facilities to the customers. A model formulation for a deterministic facility location problem is developed in order to find the optimal distribution centre locations to minimize the overall costs. Demand uncertainty is then applied to the model to examine how this optimal solution would change as the demand changes.
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Raluca Fonoage · University of Vienna · 2014
Aufgrund von steigenden Bedürfnissen der Kunden und anspruchsvolleren Anforderungen im Bereich betriebswirtschaftlicher Prozesse werden Lieferkettenmanagement und Logistik zunehmend wichtiger für Unternehmen. Immer mehr Unternehmen produzieren und vertreiben ihre Güter auf dem globalen Markt. Globalisierung und Wettbewerb zwingen die Unternehmen die Geschäftsprozesse besser aufeinander abzustimmen. Kostenoptimierung ist das große Ziel eines jeden Unternehmens. Dieses Ziel kann im Bereich der täglichen Einkaufspolitik und Massenproduktion erreicht werden. Um einen optimalen Erfolg zu erzielen und eine langfristige Senkung der Kosten zu erreichen bedarf es einer strategischen Planung im Hinblick auf die Entscheidungsprozesse. Taktische und operative Entscheidungen in Bezug auf die Produktverfügbarkeit und den Transportwesen müssen bei der Standortwahl und der Kundezuordnung berücksichtigt werden. Das Ziel dieser Abhandlung ist es zu erörtern, dass Lagerkosten und alle in der Beschaffungskette eines Betriebes fallenden Kosten die Struktur des Optimal Network Design stark beeinflussen. Die traditionelle Geschäftspolitik berücksichtigt lediglich Lager – und Transportkosten. Das neue Modell kalkuliert aber auch den Sicherheitsbestand im Lager ein. Croxton und Zinn (2005) lösten dieses Problem, doch sie präsentierten nur den Aufbau des neuen Models aber nicht die Methode der Kalkulation des Sicherheitsbestandes im Lager eines Betriebes. Diese Probleme der Optimierung kann mit Hilfe des linear formulierten Programmes Xpress gelöst werden. Kosten für Transport und Lagerung werden somit in dem Modell integriert. Kosten für den Mindestbestand (Sicherheitsbestand) können abhängig von der Art der Lagerung verschiedene Werte aufweisen. Unter Verwendung der Quadratwurzel Regel wird die Näherungsrechnung der Kosten für den Mindestbestand durchgeführt. Die Quadratwurzel erlaubt es nicht diesen Kostenfaktor in der objektiven Funktion als eine Entscheidungsvariable zu integrieren. Deshalb ist der Wert des Mindestbestandes individuell für jedes mögliche Szenario zu kalkulieren. Die Korrektur des Näherungswertes für den Mindestbestand (Sicherheitsbestand) wurde erfolgreich durch die Berechnung der effektiven Kosten für den Mindestbestand aller Güterverteilzentren durchgeführt. Im Gegensatz zum traditionellen Modell, das 4 offene Verteilzentren berücksichtigt, empfiehlt das neue Modell nur zwei Lagerhäuser. Deshalb bietet die Einbeziehung der Kosten für die Menge des Sicherheitsbestandes in die Kalkulation eine realistischere Aufzeichnung der Kosten für die gesamte Lieferkette und beeinflusst somit die gesamte Netzwerk-Design Struktur.
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Physics Subject Headings (PhySH) · 2018
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Haruo Nagase · Applied Mathematical Modelling · 1981
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