This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups according to a set of guiding decision criteria. Examples of such problems are: vehicle routing problems, team formation problems, timetabling problems, assembly line balancing, group maintenance planning, modular design, and task assignment. A wide range of industrial grouping problems, drawn from diverse fields such as logistics, supply chain management, project management, manufacturing systems, engineering design and healthcare, are presented. Typical complex industrial grouping problems, with multiple decision criteria and constraints, are clearly described using illustrative diagrams and formulations. The problems are mapped into a common group structure that can conveniently be used as an input scheme to specific variants of grouping genetic algorithms. Unique heuristic grouping techniques are developed to handle grouping problems efficiently and effectively. Illustrative examples and computational results are presented in tables and graphs to demonstrate the efficiency and effectiveness of the algorithms. Researchers, decision analysts, software developers, and graduate students from various disciplines will find this in-depth reader-friendly exposition of advances and applications of grouping genetic algorithms an interesting, informative and valuable resource.

Continuous improvements in digitized practices have created opportunities for businesses to develop more streamlined processes. This not only leads to higher success in day-to-day production, but it also increases the overall success of businesses. E-Manufacturing and E-Service Strategies in Contemporary Organizations is a critical scholarly resource that explores the advances in cloud-based solutions in the service and manufacturing realms of corporations and promotes communication between customers and service providers and manufacturers. Featuring coverage on a wide range of topics including smart manufacturing, internet banking, database system adoption, this book is geared towards researchers, professionals, managers, and academicians seeking current and relevant research on the improvement of cloud-based systems for manufacturing and service.

This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups according to a set of guiding decision criteria. Examples of such problems are: vehicle routing problems, team formation problems, timetabling problems, assembly line balancing, group maintenance planning, modular design, and task assignment. A wide range of industrial grouping problems, drawn from diverse fields such as logistics, supply chain management, project management, manufacturing systems, engineering design and healthcare, are presented. Typical complex industrial grouping problems, with multiple decision criteria and constraints, are clearly described using illustrative diagrams and formulations. The problems are mapped into a common group structure that can conveniently be used as an input scheme to specific variants of grouping genetic algorithms. Unique heuristic grouping techniques are developed to handle grouping problems efficiently and effectively. Illustrative examples and computational results are presented in tables and graphs to demonstrate the efficiency and effectiveness of the algorithms. Researchers, decision analysts, software developers, and graduate students from various disciplines will find this in-depth reader-friendly exposition of advances and applications of grouping genetic algorithms an interesting, informative and valuable resource.

Healthcare operations, in hospitals and home healthcare settings, are inundated with complex fuzzy features that impose difficulties in the creation of work schedules. As healthcare workers call for schedules that accommodate their individual preferences and patients continue to call for more personalized healthcare, further research into multi-criteria solution approaches to staff scheduling is imperative. Healthcare Staff Scheduling: Emerging Fuzzy Optimization Approaches presents in-depth research into emerging approaches to healthcare staff scheduling. It starts by reviewing the key issues and challenges inherent in staff scheduling, along with the basic concepts of fuzzy set theory. Examining research applications in healthcare staff scheduling, it details promising fuzzy optimization algorithms derived from biologically inspired approaches and fuzzy theory. Providing researchers, operations analysts, scientists, and practitioners with a practical and in-depth understanding of modern fuzzy metaheuristic optimization approaches, the book presents cutting-edge research on multi-criteria algorithms and their applications in healthcare operations, particularly in staff scheduling. The book illustrates flexible techniques for solving complex scheduling problems that account for the variability that results from imprecise human preferences. Covering recent developments in the methods utilized to create high-quality staff schedules, it includes many instructive examples of healthcare schedule problems along with potential solutions. Considering avenues for future research, this book will help readers pave the way to new and improved methods for solving staff scheduling problems that are difficult to evaluate quantitatively due to imprecision, fuzziness, and vagueness. To promote the quality of the work presented, all chapters in this book have been rigorously reviewed by leading international experts and researche