Bücher der Reihe Synthesis Lectures on Operations Research and Applications

This text is an introduction to Operations Management. Three themes are woven throughout the book: optimization or trying to do the best we can, managing tradeoffs between conflicting objectives, and dealing with uncertainty. After a brief introduction, the text reviews the fundamentals of probability including commonly used discrete and continuous distributions and functions of a random variable. The next major section, beginning in Chapter 7, examines optimization. The key fundamentals of optimization-inputs, decision variables, objective(s), and constraints-are introduced. Optimization is applied to linear regression, basic inventory modeling, and the newsvendor problem, which incorporates uncertain demand. Linear programming is then introduced. We show that the newsvendor problem can be cast as a network flow linear programming problem. Linear programming is then applied to the problem of redistributing empty rental vehicles (e.g., bicycles) at the end of a day and the problem of assigning students to seminars. Several chapters deal with location models as examples of both simple optimization problems and integer programming problems. The next major section focuses on queueing theory including single-and multi-server queues. This section also introduces a numerical method for solving for key performance metrics for a common class of queueing problems as well as simulation modeling. Finally, the text ends with a discussion of decision theory that again integrates notions of optimization, tradeoffs, and uncertainty analysis. The text is designed for anyone with a modest mathematical background. As such, it should be readily accessible to engineering students, economics, statistics, and mathematics majors, as well as many business students.

This book provides a blend of quantitative and qualitative approaches to decision making, while also bridging the gap between the theory of how to make good decisions versus how people actually make decisions.  The authors present the tools and techniques of decision analysis to learn how to become a FOCCUSSED decision maker: Identify and properly Frame the decision or problem at handSpecify the goals, Objectives, and values that you are trying to achieveDevelop creative, meaningful Choices from among which you can chooseEvaluate the Consequences of selecting each alternative using your goals, objectives, and valuesThink about the key Uncertainties that could impact the decisionUnderstand the Swaps and trade-offs that you are willing to makeDevelop an approach for implementing your SolutionElicit the data you'll need from a variety of sourcesand Disseminate and communicate your decisions to others.  The authors define a decision as the choice among alternatives, based on how we value and trade-off their pros and cons, made in the face of uncertainty about what will actually happen. The decision-making process is presented as having three pillars to support the decision maker: Preferences-what we prefer, what meets our goals and objectives, and the recognition that preferences are personal to the one making the decision; Alternatives-the choices, options, or courses of action that we have, and over which we have some degree of control; and Information-what we know about the situation, what we don't know, how we connect choices to outcomes, and how we deal with uncertainty.  Key components of good decision-making include how to define your goals and objectives, how to incorporate uncertainties that we all face, and how to develop better alternatives, all of which are discussed.  Sophisticated techniques are presented in a way that is accessible to the average decision maker. Probability theory is utilized to improve decisions, and uncertainties are captured in decision trees.  Risk avoidance, risk transfer, and risk mitigation are also discussed.  Readers will gain a clear understanding of how to articulate the goals and objectives that should be the focal point of any decision.

This book provides a concerted supply chain perspective for dealing with pandemics on the scale of COVID-19. Specifically, this book describes a new approach, supply chain immunity, to illustrate what is needed to fix our economy and healthcare systems. The authors of this book are experts in supply chain management, health care supply chains, major systems acquisition, and contingency sourcing methods. Based on first-hand experiences working during COVID in the depths of the nation¿s supply chain failures, the authors develop important themes for private and public sector supply chain managers to consider in rebuilding a more immune supply chain. The book is targeted at policy makers, academics, practitioners, and students of disaster response, public policy, healthcare, and supply chain management who are interested in learning contemporary lessons from the COVID-19 pandemic. From the perspective of those who lived through the chaos, the authors furtherexplore the application of novel concepts in joint planning, market intelligence, and governance related to a national pandemic or other global contingency.

This book presents a new search paradigm for solving the Traveling Salesman Problem (TSP). The intrinsic difficulty of the TSP is associated with the combinatorial explosion of potential solutions in the solution space. The author introduces the idea of using the attractor concept in dynamical systems theory to reduce the search space for exhaustive search for the TSP. Numerous examples are used to describe how to use this new search algorithm to solve the TSP and its variants including: multi-objective TSP, dynamic TSP, and probabilistic TSP. This book is intended for readers in the field of optimization research and application.

This book is the first comprehensive guide to logic-based Benders decomposition (LBBD), a general and versatile method for breaking large, complex optimization problems into components that are small enough for practical solution. The author introduces logic-based Benders decomposition for optimization, which substantially generalizes the classical Benders method. It can reduce solution times by orders of magnitude and allows decomposition to be applied to a much wider variety of optimization problems. On the theoretical side, this book provides a full account of inference duality concepts that underlie LBBD, as well as a description of how LBBD can be combined with stochastic and robust optimization, heuristic methods, and decision diagrams. It also clarifies the connection between LBBD and combinatorial Benders cuts for mixed integer programming. On the practical side, it explains how LBBD has been applied to a rapidly growingvariety of problem domains. After describing basic theory, this book provides a comprehensive review of the rapidly growing literature that describes these applications, in each case explaining how LBBD is adapted to the problem at hand. In doing so this work provides a sourcebook of ideas for applying LBBD to new problems as they arise.