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Author: Pinar Keles Publisher: ISBN: 9780549277545 Category : Languages : en Pages : 122
Book Description
Investments, especially those in engineering applications and research and development, are generally made in stages. Thus, a company must often manage a portfolio of projects in various stages of development while also determining which new investments to undertake. We model, solve, and analyze the portfolio management problem for multi-stage investments with stochastic dynamic programming (SDP). As the presented recursion is intractable for large-scale problem instances, we present an approximation scheme which allows for the solution of longer horizon problems in order to ensure good time zero decisions when maximizing the discounted, expected worth of decisions over time. Additionally, the approximation approach provides two estimates of the probability of making the best decision at time zero, providing additional information to the decision maker. Numerous examples illustrate the ability to examine different budget levels, delay options (lengths, penalties, and costs), initial portfolios, project returns, and interaction effects of projects in the pipeline. While previous research focusing on single project analysis has highlighted the importance of the delay option, we illustrate how critical this option is when one considers an interdependent portfolio of projects over time, especially when projects late in the review process may fail and/or budgets are small. We show that our approximate dynamic programming (ADP) approach can be used to solve large scale multi-stage investment problems by decomposing them into subproblems and solving each subproblem individually before combining them under a total budget constraint. This method provides optimal investment levels for different categories of projects while considering the interaction between different categories. We specifically consider pharmaceutical R & D projects. We also analyze the impact of two different budget expenditure policies on periodic investment decisions. These two policies are 'whole costing', where we need to pay the entire stage cost at the time of an accept decision, and 'periodic costing', where we break investment costs based on the duration of the stage and reduce the current cycle budget only by the amount of cost allocated to the current cycle. Through several experiments, it is concluded that the periodic costing better uses budget dollars over long problem horizons, especially when we have the delay option.
Author: Pinar Keles Publisher: ISBN: 9780549277545 Category : Languages : en Pages : 122
Book Description
Investments, especially those in engineering applications and research and development, are generally made in stages. Thus, a company must often manage a portfolio of projects in various stages of development while also determining which new investments to undertake. We model, solve, and analyze the portfolio management problem for multi-stage investments with stochastic dynamic programming (SDP). As the presented recursion is intractable for large-scale problem instances, we present an approximation scheme which allows for the solution of longer horizon problems in order to ensure good time zero decisions when maximizing the discounted, expected worth of decisions over time. Additionally, the approximation approach provides two estimates of the probability of making the best decision at time zero, providing additional information to the decision maker. Numerous examples illustrate the ability to examine different budget levels, delay options (lengths, penalties, and costs), initial portfolios, project returns, and interaction effects of projects in the pipeline. While previous research focusing on single project analysis has highlighted the importance of the delay option, we illustrate how critical this option is when one considers an interdependent portfolio of projects over time, especially when projects late in the review process may fail and/or budgets are small. We show that our approximate dynamic programming (ADP) approach can be used to solve large scale multi-stage investment problems by decomposing them into subproblems and solving each subproblem individually before combining them under a total budget constraint. This method provides optimal investment levels for different categories of projects while considering the interaction between different categories. We specifically consider pharmaceutical R & D projects. We also analyze the impact of two different budget expenditure policies on periodic investment decisions. These two policies are 'whole costing', where we need to pay the entire stage cost at the time of an accept decision, and 'periodic costing', where we break investment costs based on the duration of the stage and reduce the current cycle budget only by the amount of cost allocated to the current cycle. Through several experiments, it is concluded that the periodic costing better uses budget dollars over long problem horizons, especially when we have the delay option.
Author: Eymen Errais Publisher: ISBN: Category : Languages : en Pages : 24
Book Description
We introduce a general discrete time dynamic framework to value pilot project investments that reduce idiosyncratic uncertainty with respect to the final costs of a project. The model generalizes different settings introduced previously in the literature by incorporating both, market and technical uncertainty and differentiating between the commercial phase and the pilot phase of a project. In our model, the pilot phase requires N stages of investment for completion. With this distinction we are able to frame the problem as a compound perpetual Bermudan option. We work in an incomplete market setting where market uncertainty is spanned by tradable assets and technical uncertainty is idiosyncratic to the firm. The value of the option to invest as well as the optimal exercise policy are solved by an approximate dynamic programming algorithm that relies on the independent increments of the state variables. We prove the convergence of our algorithm and derive a theoretical bound on how the errors compound as the number of stages of the pilot phase is increased. We implement the algorithm for a simplified version of our model where revenues are fixed, providing an economic interpretation of the effects of the main parameters driving the model. In particular, we explore how the value of the investment opportunity and the optimal investment threshold are influenced by changes in market volatility, technical volatility, the learning coefficient and the drift rate of costs.
Author: Ahti Salo Publisher: Springer Science & Business Media ISBN: 1441999434 Category : Business & Economics Languages : en Pages : 410
Book Description
Portfolio Decision Analysis: Improved Methods for Resource Allocation provides an extensive, up-to-date coverage of decision analytic methods which help firms and public organizations allocate resources to 'lumpy' investment opportunities while explicitly recognizing relevant financial and non-financial evaluation criteria and the presence of alternative investment opportunities. In particular, it discusses the evolution of these methods, presents new methodological advances and illustrates their use across several application domains. The book offers a many-faceted treatment of portfolio decision analysis (PDA). Among other things, it (i) synthesizes the state-of-play in PDA, (ii) describes novel methodologies, (iii) fosters the deployment of these methodologies, and (iv) contributes to the strengthening of research on PDA. Portfolio problems are widely regarded as the single most important application context of decision analysis, and, with its extensive and unique coverage of these problems, this book is a much-needed addition to the literature. The book also presents innovative treatments of new methodological approaches and their uses in applications. The intended audience consists of practitioners and researchers who wish to gain a good understanding of portfolio decision analysis and insights into how PDA methods can be leveraged in different application contexts. The book can also be employed in courses at the post-graduate level.
Author: Brian Hobbs Publisher: Project Management Institute ISBN: 1628250127 Category : Business & Economics Languages : en Pages : 307
Book Description
Project Portfolios in Dynamic Environments: Organizing for Uncertainty is a comprehensive report of research that addresses this important, rising issue. Authors Yvan Petit and Brian Hobbs present the results of their investigation in a report that significantly advances the theory and also offers tips for practice. Currently, those applying project portfolio management tend to focus on the selection, prioritization, and strategic alignment of projects. Little attention is afforded the potential disturbances to project portfolios such as new projects, terminated projects, delayed projects, incorrect planning due to high uncertainty, and changes in the external environment. Yet, these factors can have highly disruptive, even show-stopping influence. This research seeks to answer: How is uncertainty affecting project portfolios managed in dynamic environments?
Author: Warren B. Powell Publisher: John Wiley & Sons ISBN: 0470182954 Category : Mathematics Languages : en Pages : 487
Book Description
A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.
Author: Stephen Boyd Publisher: ISBN: 9781680833287 Category : Mathematics Languages : en Pages : 92
Book Description
This monograph collects in one place the basic definitions, a careful description of the model, and discussion of how convex optimization can be used in multi-period trading, all in a common notation and framework.
Author: Warren B. Powell Publisher: John Wiley & Sons ISBN: 1119815037 Category : Mathematics Languages : en Pages : 1090
Book Description
REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.
Author: A.J. Tallón-Ballesteros Publisher: IOS Press ISBN: 1643683470 Category : Computers Languages : en Pages : 440
Book Description
Fuzzy logic is vital to applications in the electrical, industrial, chemical and engineering realms, as well as in areas of management and environmental issues. Data mining is indispensible in dealing with big data, massive data, and scalable, parallel and distributed algorithms. This book presents papers from FSDM 2022, the 8th International Conference on Fuzzy Systems and Data Mining. The conference, originally scheduled to take place in Xiamen, China, was held fully online from 4 to 7 November 2022, due to ongoing restrictions connected with the COVID-19 pandemic. This year, FSDM received 196 submissions, of which 47 papers were ultimately selected for presentation and publication after a thorough review process, taking into account novelty, and the breadth and depth of research themes falling under the scope of FSDM. This resulted in an acceptance rate of 23.97%. Topics covered include fuzzy theory, algorithms and systems, fuzzy applications, data mining and the interdisciplinary field of fuzzy logic and data mining. Offering an overview of current research and developments in fuzzy logic and data mining, the book will be of interest to all those working in the field of data science.
Author: Pinar Keles
Author: Pinar Keles
Author: Eymen Errais
Author: Ahti Salo
Author: 
Author: Brian Hobbs
Author: Warren B. Powell
Author: 
Author: Stephen Boyd
Author: Warren B. Powell
Author: A.J. Tallón-Ballesteros