The advancements in decision sciences theory and applications can be regarded as a continuously emerging field in all areas of interest including technology, industry, energy, healthcare, education, agriculture, social sciences, and more. Managers in all disciplines face an endless list of complex issues every day. One of the essential managerial skills is the ability to allocate and utilize limited resources appropriately in the efforts of achieving optimal performance efficiently. This is no less important for those who work in the transportation sector. Decision Sciences and Applications in the Transportation Sector explores the importance of decision sciences and the ways in which they apply to the transportation sector. This book covers technologies and tools including machine learning, mathematical modeling, and simulation and their applications in such tasks as reducing fuel costs, improving passenger flow, and ensuring vehicle safety. It is an essential reference source for managers, professionals in the transport industry, supply chain specialists, safety officers, IT consultants, executives, practitioners, scientists, students, researchers, and academicians.

This book presents best practices involving applications of decision sciences, business tactics and behavioral sciences for COVID-19. Addressing concrete problems in these vital fields, it focuses on theoretical and methodological investigations of managerial decisions that drive production and service enterprises' productivity and success. Moreover, it presents optimization techniques and tools that can also be adopted for other applications in various research areas after a thorough analysis of the specific problem. The book is intended for researchers and practitioners seeking optimum solutions to real-life problems in various application areas concerning COVID-19, helping them make scientifically founded decisions.

This book presents recent contributions and significant development, advanced issues, and challenges. In real-world problems and applications, most of the optimization problems involve different types of constraints. These problems are called constrained optimization problems (COPs). The optimization of the constrained optimization problems is considered a challenging task since the optimum solution(s) must be feasible. In their original design, evolutionary algorithms (EAs) are able to solve unconstrained optimization problems effectively. As a result, in the past decade, many researchers have developed a variety of constraint handling techniques, incorporated into (EAs) designs, to counter this deficiency. The main objective for this book is to make available a self-contained collection of modern research addressing the general constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects.

This book presents best practices involving applications of decision sciences, business tactics and behavioral sciences for COVID-19. Addressing concrete problems in these vital fields, it focuses on theoretical and methodological investigations of managerial decisions that drive production and service enterprises’ productivity and success. Moreover, it presents optimization techniques and tools that can also be adopted for other applications in various research areas after a thorough analysis of the specific problem.  The book is intended for researchers and practitioners seeking optimum solutions to real-life problems in various application areas concerning COVID-19, helping them make scientifically founded decisions.