Process planning and scheduling are two crucial components in a flexible manufacturing system. Lots of novel meta-heuristics have been applied to the integrated process planning and scheduling (IPPS) problem for an efficient utilization of manufacturing resources; nevertheless, the tricky part in real life stems from the uncertainty in processing times.
Different with the plain flexible job-shop scheduling problem (FJSP), the FJSP with routing flexibility is more complex and it can be deemed as the integrated process planning and (job shop) scheduling (IPPS) problem, where the process planning and the job shop scheduling two important functions are considered as a whole and optimized simultaneously to utilize the flexibility in a flexible manufacturing system. Although, many novel meta-heuristics have been introduced to address this problem and corresponding fruitful results have been observed; the dilemma in real-life applications of resultant scheduling schemes stems from the uncertainty or the nondeterminacy in processing times, since the uncertainty in processing times will disturb the predefined scheduling scheme by influencing unfinished operations. As a result, the performance of the manufacturing system will also be deteriorated. Nevertheless, research on such issue has seldom been considered before. This research focuses on the modeling and optimization method of the IPPS problem with uncertain processing times. The neutrosophic set is first introduced to model uncertain processing times. Due to the complexity in the math model, we developed an improved teaching-learning-based optimization(TLBO) algorithm to capture more robust scheduling schemes.
Industrial revolutions have impacted both, manufacturing and service. From the steam engine to digital automated production, the industrial revolutions have conduced significant changes in operations and supply chain management (SCM) processes. Swift changes in manufacturing and service systems have led to phenomenal improvements in productivity. The fast-paced environment brings new challenges and opportunities for the companies that are associated with the adaptation to the new concepts such as Internet of Things (IoT) and Cyber Physical Systems, artificial intelligence (AI), robotics, cyber security, data analytics, block chain and cloud technology. These emerging technologies facilitated and expedited the birth of Logistics 4.0. Industrial Revolution 4.0 initiatives in SCM has attracted stakeholders’ attentions due to it is ability to empower using a set of technologies together that helps to execute more efficient production and distribution systems. This initiative has been called Logistics 4.0 of the fourth Industrial Revolution in SCM due to its high potential. Connecting entities, machines, physical items and enterprise resources to each other by using sensors, devices and the internet along the supply chains are the main attributes of Logistics 4.0. IoT enables customers to make more suitable and valuable decisions due to the data-driven structure of the Industry 4.0 paradigm. Besides that, the system’s ability of gathering and analyzing information about the environment at any given time and adapting itself to the rapid changes add significant value to the SCM processes. In this peer-reviewed book, experts from all over the world, in the field present a conceptual framework for Logistics 4.0 and provide examples for usage of Industry 4.0 tools in SCM. This book is a work that will be beneficial for both practitioners and students and academicians, as it covers the theoretical framework, on the one hand, and includes examples of practice and real world.
FLINS, an acronym introduced in 1994 and originally for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended into a well-established international research forum to advance the foundations and applications of computational intelligence for applied research in general and for complex engineering and decision support systems.The principal mission of FLINS is bridging the gap between machine intelligence and real complex systems via joint research between universities and international research institutions, encouraging interdisciplinary research and bringing multidiscipline researchers together.FLINS 2020 is the fourteenth in a series of conferences on computational intelligence systems.
This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.
The changes in the technological and business environment have complicated the nature of the decision-making process in real-world problems, thus motivating the development of new operations research (OR) methodologies. The traditional OR context is usually based on a single objective approach using profit (cost) maximisation (minimisation) criteria. However, it is now widely acknowledged that such an approach overlooks additional factors which are also highly relevant in a decision-making context. This book presents the recent advances to the theory of multicriteria analysis, covering all its major aspects in a unique edited volume.
This book covers the conventional and most recent theories and applications in the area of evolutionary algorithms, swarm intelligence, and meta-heuristics. Each chapter offers a comprehensive description of a specific algorithm, from the mathematical model to its practical application. Different kind of optimization problems are solved in this book, including those related to path planning, image processing, hand gesture detection, among others. All in all, the book offers a tutorial on how to design, adapt, and evaluate evolutionary algorithms. Source codes for most of the proposed techniques have been included as supplementary materials on a dedicated webpage.
