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This book is dedicated to operations research of broad applications, such as improving informational bases of performance measurement with grey relational analysis, application of lean methodologies in a neurosurgery high dependency unit, iteration algorithms in Markov decision processes with state-action-dependent discount factors and unbounded costs, financial feasibility analysis of Natura Rab business case study, and mathematical modeling of isothermal drying and its potential application in the design of the industrial drying regimes of clay products.Operations research is an important topic. In addition to its obvious benefits of winning a war, making most profit in a business endeavor, and constructing a correct mathematical model, it also provides a tool for efficient use of natural resources. Furthermore, both theory and practice of operations research and its related concepts are covered in the book, and a reader can benefit from this balanced coverage.
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Los encuentros RSME-SMM empiezan a consolidarse como uno de los más importantes congresos de investigación para la comunidad matemática mexicana y esta tercera edición continuará con la tradición de alto nivel académico y excelencia matemática que han establecido los anteriores. Se estrechan así los lazos, no sólo de amistad y compañerismo entre colegas con un interés común, sino de cooperación científica del más alto nivel y en el más noble espíritu internacional de solidaridad y colaboración entre nuestras Sociedades y comunidades.
Forest resources are an ideal starting point for economic analysis of sustainability. In this book, leading economists discuss key aspects of sustainability and sustainable forest management including complexity, ethical issues, consumer choice theory, intergenerational equity, non-convexities, and multiple equilibria. This systematic critique of neoclassical economic approaches is followed by a companion work, Institutions, Sustainability, and Natural Resources: Institutions for Sustainable Forest Management, Volume 2 in the series.
Lists for 19 include the Mathematical Association of America, and 1955- also the Society for Industrial and Applied Mathematics.
This SpringerBrief deals with a class of discrete-time zero-sum Markov games with Borel state and action spaces, and possibly unbounded payoffs, under discounted and average criteria, whose state process evolves according to a stochastic difference equation. The corresponding disturbance process is an observable sequence of independent and identically distributed random variables with unknown distribution for both players. Unlike the standard case, the game is played over an infinite horizon evolving as follows. At each stage, once the players have observed the state of the game, and before choosing the actions, players 1 and 2 implement a statistical estimation process to obtain estimates o...
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This volume provides a general overview of discrete- and continuous-time Markov control processes and stochastic games, along with a look at the range of applications of stochastic control and some of its recent theoretical developments. These topics include various aspects of dynamic programming, approximation algorithms, and infinite-dimensional linear programming. In all, the work comprises 18 carefully selected papers written by experts in their respective fields. Optimization, Control, and Applications of Stochastic Systems will be a valuable resource for all practitioners, researchers, and professionals in applied mathematics and operations research who work in the areas of stochastic control, mathematical finance, queueing theory, and inventory systems. It may also serve as a supplemental text for graduate courses in optimal control and dynamic games.
The recent success of Reinforcement Learning and related methods can be attributed to several key factors. First, it is driven by reward signals obtained through the interaction with the environment. Second, it is closely related to the human learning behavior. Third, it has a solid mathematical foundation. Nonetheless, conventional Reinforcement Learning theory exhibits some shortcomings particularly in a continuous environment or in considering the stability and robustness of the controlled process. In this monograph, the authors build on Reinforcement Learning to present a learning-based approach for controlling dynamical systems from real-time data and review some major developments in t...