Wonderful Solutions And Habitual Domains For Challenging Problems In Changeable Spaces: From Theoretical Framework To Applications
by Moussa Larbani /
2016 / English / PDF
4.4 MB Download
This book introduces a new paradigm called ‘Optimization in
Changeable Spaces’ (OCS) as a useful tool for decision making and
problem solving. It illustrates how OCS incorporates, searches, and
constructively restructures the parameters, tangible and
intangible, involved in the process of decision making. The book
elaborates on OCS problems that can be modeled and solved
effectively by using the concepts of competence set analysis,
Habitual Domain (HD) and the mental operators called the 7-8-9
principles of deep knowledge of HD. In addition, new concepts of
covering and discovering processes are proposed and formulated as
mathematical tools to solve OCS problems. The book also includes
reformulations of a number of illustrative real-life challenging
problems that cannot be solved by traditional optimization
techniques into OCS problems, and details how they can be
addressed. Beyond that, it also includes perspectives related to
innovation dynamics, management, artificial intelligence,
artificial and e-economics, scientific discovery and knowledge
extraction. This book will be of interest to managers of businesses
and institutions, policy makers, and educators and students of
decision making and behavior in DBA and/or MBA.
This book introduces a new paradigm called ‘Optimization in
Changeable Spaces’ (OCS) as a useful tool for decision making and
problem solving. It illustrates how OCS incorporates, searches, and
constructively restructures the parameters, tangible and
intangible, involved in the process of decision making. The book
elaborates on OCS problems that can be modeled and solved
effectively by using the concepts of competence set analysis,
Habitual Domain (HD) and the mental operators called the 7-8-9
principles of deep knowledge of HD. In addition, new concepts of
covering and discovering processes are proposed and formulated as
mathematical tools to solve OCS problems. The book also includes
reformulations of a number of illustrative real-life challenging
problems that cannot be solved by traditional optimization
techniques into OCS problems, and details how they can be
addressed. Beyond that, it also includes perspectives related to
innovation dynamics, management, artificial intelligence,
artificial and e-economics, scientific discovery and knowledge
extraction. This book will be of interest to managers of businesses
and institutions, policy makers, and educators and students of
decision making and behavior in DBA and/or MBA.