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Author: Joan Morris
Publisher:
ISBN:
Category :
Languages : en
Pages : 75
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Book Description
Author: Joan Morris
Publisher:
ISBN:
Category :
Languages : en
Pages : 75
Get Book Here
Book Description
Author: Tessaleno Devezas
Publisher: Springer Nature
ISBN: 3030599590
Category : Business & Economics
Languages : en
Pages : 277
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Book Description
This book takes an in-depth look at the economics of digital transformation. Presenting a variety of perspectives from experts, it deals with the socioeconomic changes associated with the digital transformation of production systems. The chapters also address the impacts of digital transformation on the sustainable functioning of socioeconomic and environmental systems. Select chapters also investigate the consequences of adopting intelligent learning systems, both in terms of replacing the human labor force. and their effects on the smart digital management and security of cities, places, and people. Lastly, chapters discuss important questions regarding innovations leading to sustainable change.
Author: Lorna Uden
Publisher: Springer Science & Business Media
ISBN: 3642308678
Category : Technology & Engineering
Languages : en
Pages : 593
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Book Description
The seventh International Conference on Knowledge Management in Organizations (KMO) brings together researchers and developers from industry and the academic world to report on the latest scientific and technical advances on knowledge management in organisations. KMO 2012 provides an international forum for authors to present and discuss research focused on the role of knowledge management for innovative services in industries, to shed light on recent advances in cloud computing for KM as well as to identify future directions for researching the role of knowledge management in service innovation and how cloud computing can be used to address many of the issues currently facing KM in academia and industrial sectors. The conference took place at Salamanca in Spain on the 11th-13th July in 2012.
Author: Steffen Christ
Publisher: Springer Science & Business Media
ISBN: 3834961841
Category : Business & Economics
Languages : en
Pages : 363
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Book Description
Steffen Christ shows how theoretic optimization models can be operationalized by employing self-learning strategies to construct relevant input variables, such as latent demand and customer price sensitivity.
Author: Andreas Horni
Publisher: Ubiquity Press
ISBN: 190918876X
Category : Technology & Engineering
Languages : en
Pages : 620
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Book Description
The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations. The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.
Author: Abhijit Gosavi
Publisher: Springer
ISBN: 1489974911
Category : Business & Economics
Languages : en
Pages : 530
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Book Description
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.
Author: Martin F. G. Schaffernicht
Publisher: John Wiley & Sons
ISBN: 1119118220
Category : Business & Economics
Languages : en
Pages : 475
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Book Description
An innovative simulation-based approach for strategic decision making when launching new products Growth Dynamics in New Markets contains a dynamic case study and simulations that reveal what it takes to successfully introduce a product into a new market. Written by experts in the field, the text and companion website include a compelling simulation game and a variety of simulation models. Using the simulation game and computer models, readers are challenged to design and put in place a strategy about product introduction and competitive behavior. The simulation models build on each other to help to arrive at a comprehensive understanding of product uptake as well as market development and competitive dynamics. The authors present different approaches for enhancing the models and offer guidance for applying them to real-world problems. This groundbreaking text clearly shows how to develop maps of dynamic systems, formulate candidate policies and evaluate them based on the simulations. It also reveals how to use computer simulations to understand what decisions could and should be made, when to make them and how intensive they should be. The authors present an interactive approach that: Contains an innovative combination of a case study, simulation game and simulation models for developing the skills to introduce a product to the marketplace Offers targeted questions that help to enhance the understanding of the material presented Presents detailed answers and solutions to a number or real-world business challenges Features video tutorials that explain how the simulation experiments are implemented and interpreted Aids in the development an action-oriented, pragmatic understanding of the underlying forces in business Designed for students of business administration, management, industrial engineering, informatics, engineering, and public policy, Growth Dynamics in New Markets offers an innovative approach that combines the practice of dynamic reasoning and the use of simulation to design and test possible policies.
Author: Robert Costanza
Publisher: Springer Science & Business Media
ISBN: 0387215557
Category : Science
Languages : en
Pages : 337
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Book Description
The world consists of many complex systems, ranging from our own bodies to ecosystems to economic systems. Despite their diversity, complex systems have many structural and functional features in common that can be effectively si- lated using powerful, user-friendly software. As a result, virtually anyone can - plore the nature of complex systems and their dynamical behavior under a range of assumptions and conditions. This ability to model dynamic systems is already having a powerful influence on teaching and studying complexity. The books in this series will promote this revolution in “systems thinking” by integrating computational skills of numeracy and techniques of dynamic mod- ing into a variety of disciplines. The unifying theme across the series will be the power and simplicity of the model-building process, and all books are designed to engage the reader in developing their own models for exploration of the dyn- ics of systems that are of interest to them. Modeling Dynamic Systems does not endorse any particular modeling paradigm or software. Rather, the volumes in the series will emphasize simplicity of lea- ing, expressive power, and the speed of execution as priorities that will facilitate deeper system understanding.
Author: Luis Camarinha-Matos
Publisher: Springer Science & Business Media
ISBN: 0387737979
Category : Computers
Languages : en
Pages : 1359
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Book Description
This book includes a number of selected papers from the PRO-VE '07 Conference, providing a comprehensive overview of recent advances in various Collaborative Networks domains. It covers trust aspects, performance and value systems, VO breeding environments, VO creation, e-contracting, collaborative architectures and frameworks, professional virtual communities, interoperability issues, business benefits, and case studies and applications in industry and services.
Author: Ruben Lobel
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In this paper we develop an approach to dynamic pricing that combines ideas from data-driven and robust optimization to address the uncertain and dynamic aspects of the problem. In our setting, a firm offers multiple products to be sold over a fixed discrete time horizon. Each product sold consumes one or more resources, possibly sharing the same resources among different products. The firm is given a fixed initial inventory of these resources and cannot replenish this inventory during the selling season. We assume there is uncertainty about the demand seen by the firm for each product and seek to determine a robust and dynamic pricing strategy that maximizes revenue over the time horizon. While the traditional robust optimization models are tractable, they give rise to static policies and are often too conservative. The main contribution of this paper is the exploration of closed-loop pricing policies for different robust objectives, such as MaxMin, MinMax Regret and MaxMin Ratio. We introduce a sampling based optimization approach that can solve this problem in a tractable way, with a confidence level and a robustness level based on the number of samples used. We will show how this methodology can be used for data-driven pricing or adapted for a random sampling optimization approach when limited information is known about the demand uncertainty. Finally, we compare the revenue performance of the different models using numerical simulations, exploring the behavior of each model under different sample sizes and sampling distributions.
