J. William Schmidt – författare

Integer Programming: Theory, Applications, and Computations provides information pertinent to the theory, applications, and computations of integer programming. This book presents the computational advantages of the various techniques of integer programming. Organized into eight chapters, this book begins with an overview of the general categorization of integer applications and explains the three fundamental techniques of integer programming. This text then explores the concept of implicit enumeration, which is general in a sense that it is applicable to any well-defined binary program. Other chapters consider the branch-and-bound methods, the cutting-plane method, and its closely related asymptotic problem. This book discusses as well several specialized algorithms for certain well-known integer models and provides an alternative approach to the solution of the integer problem. The final chapter deals with a number of observations about the formulations and executions of integer programming models. This book is a valuable resource for industrial engineers and research workers.

Computer Methods in Operations Research focuses on the computational methods used in operations research. Topics covered range from list processing to sorting and searching, networks, and critical path methods. Resource-constrained scheduling methods and linear programming methods are also discussed, along with the branch and bound concept. Comprised of 11 chapters, this book begins with a review of some of the basic principles that make a software development effort successful, emphasizing the need to keep things simple and understandable. The reader is then introduced to the basic principles of list processing, searching, and sorting; the concept of networks and several matrix- and list-oriented methods for representing networks in the computer; and the critical path method. Subsequent chapters deal with more complex programs and algorithms to handle scheduling of activities under precedence and resource restrictions; the resource-constrained scheduling problem, formulated both in an exact (using integer programming) and in a heuristic manner; the design of algorithms for the solution of large linear programming problems; and the application of list processing concepts to the development of branch and bound algorithms for solution of combinatorial optimization problems. The book also considers the design of random number generators and discrete event simulation programming before concluding with a description of two programming languages, GPSS and WIDES, for use in simulation modeling. This monograph will be of value to students and practitioners of operations research and industrial engineering.

Urban Systems Models provides description, optimization, and analysis of the main features of highly urbanized areas. It details and shows many models intended to aid in the study of urban problems. The book focuses mainly on land use, public facility siting, population analysis, resource allocation in congested urban settings, and transportation networks. The text aims to bridge the gap between the use of applied mathematics and techniques on urban analysis. Civil and industrial engineers, transportation and urban planners, public administrators, researchers, and students in related fields will find the book very useful.

Mathematical Techniques of Applied Probability, Volume 1: Discrete Time Models: Basic Theory provides information pertinent to the basic theory of discrete time models. This book introduces the tools of generating functions and matrix theory to facilitate a detailed study of such models. Organized into five chapters, this volume begins with an overview of the elementary theory of probability for discrete random variables. This text then reviews the concepts of convergence, absolute convergence, uniform convergence, continuity, differentiation, and integration. Other chapters consider the occurrence of general patterns of successes and failures in Bernoulli trials. This book discusses as well the matrix theory, which is used in the study of stochastic processes, particularly in the analysis of the behavior of Markov chains. The final chapter deals with the properties of a special class of discrete time chains. This book is a valuable resource for students and teachers.

Models for Public Systems Analysis considers the mathematical model formulation to improve the delivery of urban service systems, such as sanitation, fire, police, and ambulances. This book is composed of five chapters that demonstrate the translation of significant societal problems into a mathematical framework, as well as the advantages and limitations of these models. Chapter 1 deals with the issue of plant location and siting questions, with a brief overview of water resource modeling, while Chapter 2 provides set-covering models for manpower scheduling as a direct outgrowth of the author''s experience with the Sanitation Department in New York City. Chapters 3 and 4 describe the delivery of emergency services, particularly with models of congestion and delay and of optimal deployment. These chapters also present probabilistic analysis in nature since both the spatial and the temporal patterns of demand are intrinsically uncertain. The tools used are queueing theory and geometric probability. Chapter 5 examines network optimization methods, mainly to explore questions of vehicle routing and scheduling. This chapter also provides a few comments on large-scale models of urban growth, these being generally more familiar to the regional planner then to the operations analyst. This book will prove useful to applied mathematics and policy science students.

Modeling of Complex Systems: An Introduction describes the framework of complex systems. This book discusses the language of system theory, taxonomy of system concepts, steps in model building, and establishing relations using physical laws. The statistical attributes of data, generation of random numbers fundamental problems of recognition, and input-output type models are also elaborated. This text likewise covers the optimization with equality constraints, transfer function models, and competition among species. This publication is written primarily for senior undergraduate students and beginning graduate students who are interested in an interdisciplinary or multidisciplinary approach to large-scale or complex problems of contemporary societal interest.

Foundations of Decision Support Systems focuses on the frameworks, strategies, and techniques involved in decision support systems (DSS). The publication first takes a look at information processing, decision making, and decision support; frameworks for organizational information processing and decision making; and representative decision support systems. Discussions focus on classification scheme for DSS, abilities required for decision making, division of information-processing labor within an organization, and decision support. The text then elaborates on ideas in decision support, formalizations of purposive systems, and conceptual and operational constructs for building a data base knowledge system. The book takes a look at building a data base knowledge system, language systems for data base knowledge systems, and problem-processing systems for data base knowledge systems. Topics include problem processors for computationally oriented DSS, major varieties of logical data structures, and indirect associations among concepts. The manuscript also examines operationalizing modeling knowledge in terms of predicate calculus; combining the data base and formal logic approaches; and the language and knowledge systems of a DSS based on formal logic. The publication is a valuable reference for researchers interested in decision support systems.

Applied Linear Programming for the Socioeconomic and Environmental Sciences discusses applications of linear and related programming to help in the transformation of the student or reader from book learning to computer use. The author reviews the theory, methods and applications of linear programming. The author also presents some programming codes that can be used in solving linear programming problems. He describes processes such as parametric programming, sensitivity analysis, and postoptimal analysis. The author lists five possible applications of linear programming, as follows: 1) estimates involving supply of and demand for services; 2) transport and schedule planning; 3) scale, technologies, and optimal site selection; (4) evaluation of impact of activates; and 5) evaluation of alternative options. The author cites a case study of solid-waste management in New Jersey that is common to other areas: availability of disposal sites, increasing amounts of garbage, and stricter environmental regulations. This book can be appreciated by environmentalist, sociologists, economists, civil engineers, and students and professors of advance mathematics and linear programming.

Applied Statistical Methods covers the fundamental understanding of statistical methods necessary to deal with a wide variety of practical problems. This 14-chapter text presents the topics covered in a manner that stresses clarity of understanding, interpretation, and method of application. The introductory chapter illustrates the importance of statistical analysis. The next chapters introduce the methods of data summarization, including frequency distributions, cumulative frequency distributions, and measures of central tendency and variability. These topics are followed by discussions of the fundamental principles of probability, the concepts of sample spaces, outcomes, events, probability, independence of events, and the characterization of discrete and continuous random variables. Other chapters explore the distribution of several important statistics; statistical tests of hypotheses; point and interval estimation; and simple linear regression. The concluding chapters review the elements of single- and two-factor analysis of variance and the design of analysis of variance experiments. This book is intended primarily for advanced undergraduate and graduate students in the mathematical, physical, and engineering sciences, as well as in economics, business, and related areas. Researchers and line personnel in industry and government will find this book useful in self-study.