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Goal programming is one of the most widely used methodologies in operations research and management science, and encompasses most classes of multiple objective programming models. Ignizio provides a concise and lucid overview of (a) the linear goal programming model, (b) a computationally efficient algorithm for solution, (c) duality and sensitivity analysis and (d) extensions of the methodology to integer as well as non-linear models.
Decision Methods for Forest Resource Management focuses on decision making for forests that are managed for both ecological and economic objectives. The essential modern decision methods used in the scientific management of forests are described using basic algebra, computer spreadsheets, and numerous examples and applications. Balanced treatment is given throughout the book to the ecological and economic impacts of alternative management decisions in both even-aged and uneven-aged forests. - In-depth coverage of both ecological and economic issues - Hands-on examples with Excel spreadsheets; electronic versions available on the authors' website - Many related exercises with solutions - Instructor's Manual available upon request
Practical Goal Programming is intended to allow academics and practitioners to be able to build effective goal programming models, to detail the current state of the art, and to lay the foundation for its future development and continued application to new and varied fields. Suitable as both a text and reference, its nine chapters first provide a brief history, fundamental definitions, and underlying philosophies, and then detail the goal programming variants and define them algebraically. Chapter 3 details the step-by-step formulation of the basic goal programming model, and Chapter 4 explores more advanced modeling issues and highlights some recently proposed extensions. Chapter 5 then details the solution methodologies of goal programming, concentrating on computerized solution by the Excel Solver and LINGO packages for each of the three main variants, and includes a discussion of the viability of the use of specialized goal programming packages. Chapter 6 discusses the linkages between Pareto Efficiency and goal programming. Chapters 3 to 6 are supported by a set of ten exercises, and an Excel spreadsheet giving the basic solution of each example is available at an accompanying website. Chapter 7 details the current state of the art in terms of the integration of goal programming with other techniques, and the text concludes with two case studies which were chosen to demonstrate the application of goal programming in practice and to illustrate the principles developed in Chapters 1 to 7. Chapter 8 details an application in healthcare, and Chapter 9 describes applications in portfolio selection.
Transportation asset management delivers efficient and cost-effective investment decisions to support transportation infrastructure and system usage performance measured in economic, social, health, and environmental terms. It can be applied at national, state, and local levels. This distinctive book addresses asset management for multimodal transportation, taking account of system component interdependency, integration, and risk and uncertainty. It sets out rigorous quantitative and qualitative methods for addressing system goals, performance measures, and needs; data collection and management; performance modeling; project evaluation, selection, and trade-off analysis; innovative financing; and institutional issues. It applies as easily to static traffic and time-dependent or dynamic traffic which exists on a more local level. It is written for transportation planners, engineers, and academia, as well as a growing number of graduate students taking transportation asset management courses.
1. 1. Motivation This book is based on the view-tx)int that both public and private decision making, in practice, can often be ilrproved upon by means of fonnal (nonnative) decision nodels and methods. To sane extent, the validity of this statement can be measured by the irrpressive number of su=esses of disciplines as operations research and management science. Hcwever, as witnessed by the many discussions in the professional journals in these fields, many rrodels and methods do not completely meet the requirements of decision making in prac tice. Of all possible origins of these clear shortcomings, we main-· ly focus on only one: the fact that nost of these nodels and methods are unsuitable for decision situations in which multiple and possi bly conflicting objectives playa role, because they are concentra ted on the (optimal) fulfilment of only one objective. The need to account for multiple goals was observed relatively early. Hoffman [1955], while describing 'what seem to be the prin cipal areas (in linear prograrrrning) where new ideas and new methods are needed' gives an exanple with conflicting goals. In this pro blem, the assignrrent of relative weights is a great problem for the planning staff and is 'probably not the province of the mathemati cian engaged in solving this problem'. These remarks were true pre cursors of later develor:nents. Nevertheless, the need for methods dealing with multiple goals was not widely recognized until much later.
This book provides both practitioners and academics with a scientific approach to portfolio selection using Goal Programming, an approach which is capable as far as is possible of achieving a required set of preferences deemed appropriate by a decision maker. Goal Programming is perhaps the most widely-used approach in the field of multiple criteria decision-making that enables the decision maker to incorporate numerous variations of constraints and goals. The original portfolio selection problem, with risk and return optimisation, can be viewed as a case of Goal Programming with two objectives. Additional objectives representing other factors, such as liquidity, can be introduced for a more realistic approach to portfolio selection problems. This book comes in a time where scientific frameworks for investment decision-making are absolutely necessary, that is after the recent financial and economic crisis; where irrational decisions and a misuse of mathematical models had equally fed into the spiral of the financial crisis. The real-world decision problems are usually changeable, complex and resist treatment with conventional approaches. Therefore, the optimisation of a single objective subject to a set of rigid constraints is in most cases unrealistic, and that is why Goal Programming was introduced, in an attempt to eliminate or at least mitigate this shortcoming. Most mathematical models are based on very strong theoretical assumptions which are not entirely respected by markets in practice. In contrast, Goal Programming models are based on real-world cases where the most feasible solution is sought as opposed to an ideal simplified solution. Therefore, this book provides practitioners with a new and superior scientific framework for investment decision-making, while aiming to stimulate further research and development. Moreover, the book provides scientific approaches for portfolio selection with Goal Programming, which will provide added value for practitioners in complementing their financial expertise with a sound scientific decision-making framework.
Due to the increasing importance of product differentiation and collapsing product life cycles, a growing number of value-adding activities in the industry and service sector are organized in projects. Projects come in many forms, often taking considerable time and consuming a large amount of resources. The management and scheduling of projects represents a challenging task and project performance may have a considerable impact on an organization's competitiveness. This handbook presents state-of-the-art approaches to project management and scheduling. More than sixty contributions written by leading experts in the field provide an authoritative survey of recent developments. The book serves as a comprehensive reference, both, for researchers and project management professionals. The handbook consists of two volumes. Volume 1 is devoted to single-modal and multi-modal project scheduling. Volume 2 presents multi-project problems, project scheduling under uncertainty and vagueness, managerial approaches and a separate part on applications, case studies and information systems.
An Introduction to Optimization Techniques introduces the basic ideas and techniques of optimization. Optimization is a precise procedure using design constraints and criteria to enable the planner to find the optimal solution. Optimization techniques have been applied in numerous fields to deal with different practical problems. This book is designed to give the reader a sense of the challenge of analyzing a given situation and formulating a model for it while explaining the assumptions and inner structure of the methods discussed as fully as possible. It includes real-world examples and applications making the book accessible to a broader readership. Features Each chapter begins with the Learning Outcomes (LO) section, which highlights the critical points of that chapter. All learning outcomes, solved examples and questions are mapped to six Bloom Taxonomy levels (BT Level). Book offers fundamental concepts of optimization without becoming too complicated. A wide range of solved examples are presented in each section after the theoretical discussion to clarify the concept of that section. A separate chapter on the application of spreadsheets to solve different optimization techniques. At the end of each chapter, a summary reinforces key ideas and helps readers recall the concepts discussed. The wide and emerging uses of optimization techniques make it essential for students and professionals. Optimization techniques have been applied in numerous fields to deal with different practical problems. This book serves as a textbook for UG and PG students of science, engineering, and management programs. It will be equally useful for Professionals, Consultants, and Managers.
Cognition-driven decision support system (DSS) has been recognized as a paradigm in the research and development of business intelligence (BI). Cognitive decision support aims to help managers in their decision making from human cognitive aspects, such as thinking, sensing, understanding and predicting, and fully reuse their experience. Among these cognitive aspects, decision makers’ situation awareness (SA) and mental models are considered to be two important prerequisites for decision making, particularly in ill-structured and dynamic decision situations with uncertainties, time pressure and high personal stake. In today’s business domain, decision making is becoming increasingly complex. To make a successful decision, managers’ SA about their business environments becomes a critical factor. This book presents theoretical models as well practical techniques of cognitiondriven DSS. It first introduces some important concepts of cognition orientation in decision making process and some techniques in related research areas including DSS, data warehouse and BI, offering readers a preliminary for moving forward in this book. It then proposes a cognition-driven decision process (CDDP) model which incorporates SA and experience (mental models) as its central components. The goal of the CDDP model is to facilitate cognitive decision support to managers on the basis of BI systems. It also presents relevant techniques developed to support the implementation of the CDDP model in a BI environment. Key issues addressed of a typical business decision cycle in the CDDP model include: natural language interface for a manager’s SA input, extraction of SA semantics, construction of data warehouse queries based on the manger’s SA and experience, situation information retrieval from data warehouse, how the manager perceives situation information and update SA, how the manager’s SA leads to a final decision. Finally, a cognition-driven DSS, FACETS, and two illustrative applications of this system are discussed.