Download or read book The Edge of Large scale Optimization in Transportation and Machine Learning written by Sébastien Martin (Ph. D.) and published by . This book was released on 2019 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on impactful applications of large-scale optimization in transportation and machine learning. Using both theory and computational experiments, we introduce novel optimization algorithms to overcome the tractability issues that arise in real world applications. We work towards the implementation of these algorithms, through software contributions, public policy work, and a formal study of machine learning interpretability. Our implementation in Boston Public Schools generates millions of dollars in yearly transportation savings and led to important public policy consequences in the United States. This work is motivated by large-scale transportation problems that present significant optimization challenges. In particular, we study the problem of ride-sharing, the online routing of hundreds of thousands of customers every day in New York City. We also contribute to travel time estimation from origin-destination data, on city routing networks with tens of thousands of roads. We additionally consider the problem of school transportation, the scheduling of hundreds of buses to send tens of thousands of children to school everyday. This transportation problem is related to the choice of school start times, for which we also propose an optimization framework. Building on these applications, we present methodological contributions in large- scale optimization. We introduce state-of-the-art algorithms for scheduling problems with time-window (backbone) and for school bus routing (BiRD). Our work on travel time estimation tractably produces solutions to the inverse shortest path length problem, solving a sequence of second order cone problems. We also present a theoretical and empirical study of the stochastic proximal point algorithm, an alternative to stochastic gradient methods (the de-facto algorithm for large-scale learning). We also aim at the implementation of these algorithms, through software contributions, public policy work (together with stakeholders and journalists), and a collaboration with the city of Boston. Explaining complex algorithms to decision-makers is a difficult task, therefore we introduce an optimization framework to decomposes models into a sequence of simple building blocks. This allows us to introduce formal measure of the "interpretability" of a large class of machine learning models, and to study tradeoffs between this measure and model performance, the price of interpretability.

Download or read book Robust and Online Large Scale Optimization written by Ravindra K. Ahuja and published by Springer Science & Business Media. This book was released on 2009-10-26 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scheduled transportation networks give rise to very complex and large-scale networkoptimization problems requiring innovative solution techniques and ideas from mathematical optimization and theoretical computer science. Examples of scheduled transportation include bus, ferry, airline, and railway networks, with the latter being a prime application domain that provides a fair amount of the most complex and largest instances of such optimization problems. Scheduled transport optimization deals with planning and scheduling problems over several time horizons, and substantial progress has been made for strategic planning and scheduling problems in all transportation domains. This state-of-the-art survey presents the outcome of an open call for contributions asking for either research papers or state-of-the-art survey articles. We received 24 submissions that underwent two rounds of the standard peer-review process, out of which 18 were finally accepted for publication. The volume is organized in four parts: Robustness and Recoverability, Robust Timetabling and Route Planning, Robust Planning Under Scarce Resources, and Online Planning: Delay and Disruption Management.

Download or read book Evolutionary Large Scale Multi Objective Optimization and Applications written by Xingyi Zhang and published by John Wiley & Sons. This book was released on 2024-09-11 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tackle the most challenging problems in science and engineering with these cutting-edge algorithms Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and engineering projects, these problems are notoriously challenging. In recent years, evolutionary algorithms (EAs) have shown significant promise in their ability to solve MOPs, but challenges remain at the level of large-scale multi-objective optimization problems (LSMOPs), where the number of variables increases and the optimized solution is correspondingly harder to reach. Evolutionary Large-Scale Multi-Objective Optimization and Applications constitutes a systematic overview of EAs and their capacity to tackle LSMOPs. It offers an introduction to both the problem class and the algorithms before delving into some of the cutting-edge algorithms which have been specifically adapted to solving LSMOPs. Deeply engaged with specific applications and alert to the latest developments in the field, it’s a must-read for students and researchers facing these famously complex but crucial optimization problems. The book’s readers will also find: Analysis of multi-optimization problems in fields such as machine learning, network science, vehicle routing, and more Discussion of benchmark problems and performance indicators for LSMOPs Presentation of a new taxonomy of algorithms in the field Evolutionary Large-Scale Multi-Objective Optimization and Applications is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems.

Download or read book Large Scale and Distributed Optimization written by Pontus Giselsson and published by Springer. This book was released on 2018-11-11 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents tools and methods for large-scale and distributed optimization. Since many methods in "Big Data" fields rely on solving large-scale optimization problems, often in distributed fashion, this topic has over the last decade emerged to become very important. As well as specific coverage of this active research field, the book serves as a powerful source of information for practitioners as well as theoreticians. Large-Scale and Distributed Optimization is a unique combination of contributions from leading experts in the field, who were speakers at the LCCC Focus Period on Large-Scale and Distributed Optimization, held in Lund, 14th–16th June 2017. A source of information and innovative ideas for current and future research, this book will appeal to researchers, academics, and students who are interested in large-scale optimization.

Download or read book Computationally Efficient Simulation based Optimization Algorithms for Large scale Urban Transportation Problems written by Linsen Chong and published by . This book was released on 2017 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we propose novel computationally efficient optimization algorithms that derive effective traffic management strategies to reduce congestion and improve the efficiency of urban transportation systems. The proposed algorithms enable the use of high-resolution yet computationally inefficient urban traffic simulators to address large-scale urban transportation optimization problems in a computationally efficient manner. The first and the second part of this thesis focus on large-scale offline transportation optimization problems with stochastic simulation-based objective functions, analytical differentiable constraints and high-dimensional decision variables. We propose two optimization algorithms to solve these problems. In the first part, we propose a simulation-based metamodel algorithm that combines the use of an analytical stationary traffic network model and a dynamic microscopic traffic simulator. In the second part, we propose a metamodel algorithm that combines the use of an analytical transient traffic network model and the microscopic simulator. In the first part, we use the first metamodel algorithm to solve a large-scale fixed-time traffic signal control problem of the Swiss city of Lausanne with limited simulation runs, showing that the proposed algorithm can derive signal plans that outperform traditional simulation-based optimization algorithms and a commercial traffic signal optimization software. In the second part, we use both algorithms to solve a time-dependent traffic signal control problem of Lausanne, showing that the metamodel with the transient analytical traffic model outperforms that with the stationary traffic model. The third part of this thesis focuses on large-scale online transportation problems, which need to be solved with limited computational time. We propose a new optimization framework that combines the use of a problem-specific model-driven method, i.e., the method proposed in the first part, with a generic data-driven supervised machine learning method. We use this framework to address a traffic responsive control problem of Lausanne. We compare the performance of the proposed framework with the performance of an optimization framework with only the model-driven method and an optimization framework with only the data-driven method, showing that the proposed framework is able to derive signal plans that outperform the signal plans derived by the other two frameworks in most cases.

Download or read book Probabilistic Models and Optimization Algorithms for Large scale Transportation Problems written by Jing Lu (Ph.D.) and published by . This book was released on 2020 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis tackles two major challenges of urban transportation optimization problems: (i) high-dimensionality and (ii) uncertainty in both demand and supply. These challenges are addressed from both modeling and algorithm design perspectives. The first part of this thesis focuses on the formulation of analytical transient stochastic link transmission models (LTM) that are computationally tractable and suitable for largescale network analysis and optimization. We first formulate a stochastic LTM based on the model of Osorio and Flötteröd (2015). We propose a formulation with enhanced scalability. In particular, the dimension of the state space is linear, rather than cubic, in the link’s space capacity. We then propose a second formulation that has a state space of dimension two; it scales independently of the link’s space capacity. Both link models are validated versus benchmark models, both analytical and simulation-based. The proposed models are used to address a probabilistic formulation of a city-wide signal control problem and are benchmarked versus other existing network models. Compared to the benchmarks, both models derive signal plans that perform systematically better considering various performance metrics. The second model, compared to the first model, reduces the computational runtime by at least two orders of magnitude. The second part of this thesis proposes a technique to enhance the computational efficiency of simulation-based optimization (SO) algorithms for high-dimensional discrete SO problems. The technique is based on an adaptive partitioning strategy. It is embedded within the Empirical Stochastic Branch-and-Bound (ESB&B) algorithm of Xu and Nelson (2013). This combination leads to a discrete SO algorithm that is both globally convergent and has good small sample performance. The proposed algorithm is validated and used to address a high-dimensional car-sharing optimization problem.

Download or read book Large Scale Optimization Methods for Machine Learning written by Shuai Zheng and published by . This book was released on 2019 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning Model Optimization Deployment and Improvement Techniques for Edge native Applications written by Pethuru Raj and published by Cambridge Scholars Publishing. This book was released on 2024-08-22 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: The edge AI implementation technologies are fast maturing and stabilizing. Edge AI digitally transforms retail, manufacturing, healthcare, financial services, transportation, telecommunication, and energy. The transformative potential of Edge AI, a pivotal force in driving the evolution from Industry 4.0’s smart manufacturing and automation to Industry 5.0’s human-centric, sustainable innovation. The exploration of the cutting-edge technologies, tools, and applications that enable real-time data processing and intelligent decision-making at the network’s edge, addressing the increasing demand for efficiency, resilience, and personalization in industrial systems. Our book aims to provide readers with a comprehensive understanding of how Edge AI integrates with existing infrastructures, enhances operational capabilities, and fosters a symbiotic relationship between human expertise and machine intelligence. Through detailed case studies, technical insights, and practical guidelines, this book serves as an essential resource for professionals, researchers, and enthusiasts poised to harness the full potential of Edge AI in the rapidly advancing industrial landscape.

Download or read book A Large scale Optimization Algorithm to Support Cross assets Long term Planning in Transportation Asset Management written by Shabani Geofrey Kachua and published by . This book was released on 2011 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Emerging Cutting Edge Developments in Intelligent Traffic and Transportation Systems written by M. Shafik and published by IOS Press. This book was released on 2024-03-05 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent and development of AI and other new technologies, traffic and transportation have changed enormously in recent years, and the need for more environmentally-friendly solutions is also driving innovation in these fields. This book presents the proceedings of ICITT 2023, the 7th International Conference on Intelligent Traffic and Transportation, held from 18-20 September 2023 in Madrid, Spain. This annual conference is becoming one of the leading international conferences for presenting novel and fundamental advances in the fields of intelligent traffic and transportation. It also serves to foster communication among researchers and practitioners working in a wide variety of scientific areas with a common interest in intelligent traffic and transportation and related techniques. ICITT welcomes scholars and researchers from all over the world to share experiences and lessons with other enthusiasts, and develop opportunities for cooperation. The 27 papers included here represent an acceptance rate of 64% of submissions received, and were selected following a rigorous review process. Topics covered include autonomous technology; industrial automation; artificial intelligence; machine, deep and cognitive learning; distributed networking; transportation in future smart cities; hybrid vehicle technology; mobility; cyber-physical systems; design and cost engineering; enterprise information management; product design; intelligent automation; ICT-enabled collaborative global manufacturing; knowledge management; product-service systems; optimization; product lifecycle management; sustainable systems; machine vision; Industry 4.0; and navigation systems. Offering an overview of recent research and current practice, the book will be of interest to all those working in the field.

Download or read book Large scale Optimization Methods written by Nuri Denizcan Vanli and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large-scale optimization problems appear quite frequently in data science and machine learning applications. In this thesis, we show the efficiency of coordinate descent (CD) and mirror descent (MD) methods in solving large-scale optimization problems.

Download or read book Optimization Methods for Large Scale Problems and Applications to Machine Learning written by Luca Bravi and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book International Conference on Advanced Intelligent Systems for Sustainable Development written by Janusz Kacprzyk and published by Springer Nature. This book was released on 2023-06-09 with total page 995 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the potential contributions of emerging technologies in different fields as well as the opportunities and challenges related to the integration of these technologies in the socio-economic sector. In this book, many latest technologies are addressed, particularly in the fields of computer science and engineering. The expected scientific papers covered state-of-the-art technologies, theoretical concepts, standards, product implementation, ongoing research projects, and innovative applications of Sustainable Development. This new technology highlights, the guiding principle of innovation for harnessing frontier technologies and taking full profit from the current technological revolution to reduce gaps that hold back truly inclusive and sustainable development. The fundamental and specific topics are Big Data Analytics, Wireless sensors, IoT, Geospatial technology, Engineering and Mechanization, Modeling Tools, Risk analytics, and preventive systems.

Download or read book Large scale Transportation Routing and Mode Optimization written by Yibo Dang and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the logistics industry, many resources are spent on handling the challenges in complex transportation planning problems. The growth of e-commerce demand scalable and hybrid delivery solutions that are in-time, cost-efficient, and clean. Increased workloads demand sophisticated scheduling and routing timelines. Therefore, governments and major logistics companies are launching different programs to promote sustainable distribution processes that leverage multiple transportation modes. In this way, the transportation network costs will be decreased and the carbon efficiency will be improved. Such increasing need for detailed and multimodal plans to larger transportation problems makes the current decision support tools indispensable. The low-quality solutions and the lack of big-data capability have concerned the practitioners for a long time. The problem of routing and scheduling with different types of carriers and modes have drawn limited attentions in previous research. There is no comprehensive research that discusses implementable models and algorithms with most real-world constraints on such transportation mode decision problem. The problem implies the design of long-haul transport, efficient last-mile deliveries to fulfill the demands within in strict time windows. Because it may take several days to cover a long-haul route, it is mandatory to consider breaks or layovers during the trip. Due to the capacity limit, some non-profitable deliveries are subcontracted to third-party carriers. This strategy allows the company to fill their vehicle fleets to the maximum. To further optimize the cost and carbon efficiencies of the vehicle fleets, different types of vehicles are to be evaluated. While the traditional diesel trailers can efficiently handle large amount of cargo, the travel costs and the greenhouse gas emissions cannot be negligible. While the technologies of electric vehicles are becoming mature, the driving-range issue and the high purchase cost still worry the industry. These challenges inhibit the success of a multimodal transportation network. In this dissertation, we investigate the three related optimization problems via meta-heuristics and mathematical programming – 1) a vehicle routing problem with common carriers and time regulation (VRPCCTR), in which the working hour limit is specified and third-party common carriers’ services are considered for unprofitable deliveries; 2) an extended version of the first problem where heterogeneous-sized vehicle fleets are involved (called HVRPCCTR); 3) the electric vehicles are introduced to the problem so that the mode decision becomes determining optimal routes between mixed vehicle types and sizes (called MVRPC) observing the time windows, recharging needs, service of hours regulations, and other constraints. In short, we study a new variant of the vehicle routing problem with three shipping modes – heterogeneous internal combustion vehicles, electric vehicles, and common carriers. Our main contributions are three-fold: We propose a novel meta-heuristic algorithm called Red-Black Ant Colony System for the problems, where the black ants are searching for regular routes using different sizes of vehicles whereas the red ants re-optimize within the common carrier deliveries to build efficient dedicated fleet routes. A developed tool can solve the mode decision problem for 150,000 deliveries. And its implementation has conducted over $5 million of dollars of savings for DHL and its customers annually. For each of the three problems, we give mixed integer linear programming (MILP) models and introduce strong valid inequalities. We prove the strengths of each set of valid inequalities by analyzing the linear relaxation bounds of the models. The endeavors of reducing the symmetry in the MILP models can be seen as the complexities grow from VRPCCTR through HVRPCCTR to MVRPC. We extend our work to incorporate green transportation plans. The recharges for electric vehicles and layover needs are efficiently modeled as renewable resources. This dissertation presents the first exact method based on a branch-and-cut- and-pricing algorithm for variants of MVRPC. The algorithm is tested on real data set from small sizes to medium sizes. Its performance and sensitivity are analyzed. The results show that the our algorithm can solve the problems up to 120 customers to exact optimality. Our results provide analytical insights and implementable large-scale algorithms for multimodal transportation networks of the next-generation logistic operations. The algorithms can also be utilized to study other types of scaling networks such as social networks, electrical grid, 3D printing models, etc. Other industries, such as manufacturing, digital communication, shared economy, etc., can also benefit from our results.

Download or read book The Adventurous and Practical Journey to a Large Scale Enterprise Solution written by Vahid Hajipour and published by CRC Press. This book was released on 2023-03-16 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: The high failure rate of enterprise resource planning (ERP) projects is a pressing concern for both academic researchers and industrial practitioners. The challenges of an ERP implementation are particularly high when the project involves designing and developing a system from scratch. Organizations often turn to vendors and consultants for handling such projects but, every aspect of an ERP project is opaque for both customers and vendors. Unlocking the mysteries of building a large-scale ERP system, The Adventurous and Practical Journey to a Large-Scale Enterprise Solution tells the story of implementing an applied enterprise solution. The book covers the field of enterprise resource planning by examining state-of-the-art concepts in software project management methodology, design and development integration policy, and deployment framework, including: A hybrid project management methodology using waterfall as well as a customized Scrum-based approach A novel multi-tiered software architecture featuring an enhanced flowable process engine A unique platform for coding business processes efficiently Integration to embed ERP modules in physical devices A heuristic-based framework to successfully step into the Go-live period Written to help ERP project professionals, the book charts the path that they should travel from project ideation to systems implementation. It presents a detailed, real-life case study of implementing a large-scale ERP and uses storytelling to demonstrate incorrect and correct decisions frequently made by vendors and customers. Filled with practical lessons learned, the book explains the ins and outs of adopting project methodologies. It weaves a tale that features both real-world and scholarly aspects of an ERP implementation.

Download or read book Efficient and Robust Machine Learning Methods for Challenging Traffic Video Sensing Applications written by Yifan Zhuang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of economics and technologies has promoted urbanization worldwide. Urbanization has brought great convenience to daily life. The fast construction of transportation facilities provides various means of transportation for everyday commuting. However, the growing traffic volume has threatened the existing transportation system by raising more traffic safety and congestion issues. Therefore, it is urgent and necessary to implement ITS with dynamic sensing and adjustment abilities. ITS shows great potential to improve traffic safety and efficiency, empowered by advanced IoT and AI. Within this system, the urban sensing and data analysis modules play an essential role in providing primary traffic information for follow-up works, including traffic prediction, operation optimization, and urban planning. Cameras and computer vision algorithms are the most popular toolkit in traffic sensing and analysis tasks. Deep learning-based computer vision algorithms have succeeded in multiple traffic sensing and analysis tasks, e.g., vehicle counting and crowd motion detection. The large-scale deployment of the sensor network and applications of deep learning algorithms significantly magnify previous methods' flaws, which hinder the further expansion of ITS. Firstly, the large-scale sensors and various tasks bring massive data and high workloads for data analysis on central servers. In contrast, annotated data for deep learning training in different tasks is insufficient, which leads to poor generalization when transferring to another application scenario. Additionally, traffic sensing faces adverse conditions with insufficient data and analysis qualities. This dissertation works on proposing efficient and robust machine learning methods for challenging traffic video sensing applications by presenting a systematic and practical workflow to optimize algorithm accuracy and efficiency. This dissertation first considers the high data volume challenge by designing a compression and knowledge distillation pipeline to reduce the model complexity and maintain accuracy. After applying the proposed pipeline, it is possible to further use the optimized algorithm on edge devices. This pipeline also works as the optimization foundation in the remaining works of this dissertation. Besides high data volume for analysis, insufficient training data is a considerable problem when deploying deep learning in practice. This dissertation has focused on two representative scenarios related to public safety – detecting and tracking small-scale persons in crowds and detecting rare objects in autonomous driving. Data augmentation and FSL strategies have been applied to increase the robustness of the machine learning system with limited training data. Finally, traffic sensing targets 24/7 stable operation, even in adverse conditions that reduce visibility and increase image noise with the RGB camera. Sensor fusion by combining RGB and infrared cameras is studied to improve accuracy in all light conditions. In conclusion, urbanization has simultaneously brought opportunities and challenges to the transportation system. ITS shows great potential to take this development chance and handle these challenges. This dissertation works on three data-oriented challenges and improves the accuracy and efficiency of vision-based traffic sensing algorithms. Several ITS applications are explored to demonstrate the effectiveness of the proposed methods, which achieve state-of-the-art accuracy and are far more efficient. In the future, additional research works can be explored based on this dissertation. With the continuing expansion of the sensor network, edge computing will be a more suitable system framework than cloud computing. Binary quantization and hardware-specific operator optimization can contribute to edge computing. Since data insufficiency is common in other transportation applications besides traffic detection, FSL will elevate traffic pattern forecasting and event analysis with a sequence model. For crowd monitoring, the next step will be motion prediction in bird's-eye view based on motion detection results.

Download or read book Advanced Computing and Intelligent Technologies written by Rabindra Nath Shaw and published by Springer Nature. This book was released on with total page 649 pages. Available in PDF, EPUB and Kindle. Book excerpt: