Well-organized and well-referenced, this book gives a clear presentation of heuristic methodology as a systematic form of qualitative research. Investigators of human experiences will find this book invaluable as a research guide. The author illustrates how heuristic concepts and processes form components of the research design and become the basis for a methodology. There is a clear explanation of how heuristic inquiry works in practice and the actual process of conducting a human science investigation is described in detail.

Heuristics are strategies using readily accessible, loosely applicable information to control problem solving. Algorithms, for example, are a type of heuristic. By contrast, Metaheuristics are methods used to design Heuristics and may coordinate the usage of several Heuristics toward the formulation of a single method. GRASP (Greedy Randomized Adaptive Search Procedures) is an example of a Metaheuristic. To the layman, heuristics may be thought of as ‘rules of thumb’ but despite its imprecision, heuristics is a very rich field that refers to experience-based techniques for problem-solving, learning, and discovery. Any given solution/heuristic is not guaranteed to be optimal but heuristic methodologies are used to speed up the process of finding satisfactory solutions where optimal solutions are impractical. The introduction to this Handbook provides an overview of the history of Heuristics along with main issues regarding the methodologies covered. This is followed by Chapters containing various examples of local searches, search strategies and Metaheuristics, leading to an analyses of Heuristics and search algorithms. The reference concludes with numerous illustrations of the highly applicable nature and implementation of Heuristics in our daily life. Each chapter of this work includes an abstract/introduction with a short description of the methodology. Key words are also necessary as part of top-matter to each chapter to enable maximum search engine optimization. Next, chapters will include discussion of the adaptation of this methodology to solve a difficult optimization problem, and experiments on a set of representative problems.

The scope of this book is limited to heuristics, metaheuristics, and approximate methods and algorithms as applied to planning and scheduling problems. While it is not possible to give a comprehensive treatment of this topic in one book, the aim of this work is to provide the reader with a diverse set of planning and scheduling problems and different heuristic approaches to solve them. The problems range from traditional single stage and parallel machine problems to more modern settings such as robotic cells and flexible job shop networks. Furthermore, some chapters deal with deterministic problems while some others treat stochastic versions of the problems. Unlike most of the literature that deals with planning and scheduling problems in the manufacturing and production environments, in this book the environments were extended to nontraditional applications such as spatial scheduling (optimizing space over time), runway scheduling, and surgical scheduling. The solution methods used in the different chapters of the book also spread from well-established heuristics and metaheuristics such as Genetic Algorithms and Ant Colony Optimization to more recent ones such as Meta-RaPS.

Focused on exploring human experience from an authentic researcher perspective, Heuristic Inquiry: Researching Human Experience Holistically presents heuristic inquiry as a unique phenomenological, experiential, and relational approach to qualitative research that is also rigorous and evidence-based. Nevine Sultan describes a distinguishing perspective of this research that treats participants not as subjects of research but rather as co-researchers in an exploratory process marked by genuineness and intersubjectivity. Through the use of real-life examples illustrating the various processes of heuristic research, the book offers an understanding of heuristic inquiry that is straightforward and informal yet honors its creative, intuitive, and poly-dimensional nature.

Creative solutions are easily recognizable, after they have been created. But how to attain them? This book is about a promising approach to creative problem solving - the use of heuristics. The main purpose of an heuristic is to make problem solving more efficient, by making past experience - which could guide the generation of new solutions - promptly available. The heuristic approach is widely used in TRIZ (the Theory of Inventive Problem Solving), which is becoming increasingly popular worldwide. Successful results of using heuristics have been reported by companies such as ABB, Bosch, General Motors, Ford, Mitsubishi, Philips, Siemens, among others. With this book, the reader will be able to: - Understand the 121 Heuristics for problem solving, both from their descriptions and from selected examples; - Find the more promising Heuristic(s) for the solution of his/her problems; - Apply the heuristics and find creative solutions to his/her problems.

Abbott helps social science students discover what questions to ask. This exciting book is not about habits and the mechanics of doing social science research, but about habits of thinking that enable students to use those mechanics in new ways, by coming up with new ideas and combining them more effectively with old ones. Abbott organizes his book around general methodological moves, and uses examples from throughout the social sciences to show how these moves can open new lines of thinking. In each chapter, he covers several moves and their reverses (if these exist), discussing particular examples of the move as well as its logical and theoretical structure. Often he goes on to propose applications of the move in a wide variety of empirical settings. The basic aim of Methods of Discovery is to offer readers a new way of thinking about directions for their research and new ways to imagine information relevant to their research problems. Methods of Discovery is part of the Contemporary Societies series.

Including contributions from leading experts in the field, this book covers applications and developments of heuristic search methods for solving complex optimization problems. The book covers various local search strategies including genetic algorithms, simulated annealing, tabu search and hybrids thereof. These methods have proved extraordinarily successful by solving some of the most difficult, real-world problems. At the interface between Artificial Intelligence and Operational Research, research in this exciting area is progressing apace spurred on by the needs of industry and commerce. The introductory chapter provides a clear overview of the basic techniques and useful pointers to further reading and to current research. The second section of the book covers some of the most recent and exciting developments of the basic techniques, with suggestions not only for extending and improving these but also for hybridizing and incorporating automatic adaption. The third section contains a number of case studies, surveys and comparative studies which span a wide range of application areas ranging from the classic Steiner tree problem to more practical problems arising in telecommunications and data analysis. The coverage of the latest research and the illustrative case studies will ensure that the book is invaluable for researchers and professionals with an interest in heuristic search methods.