Rescheduling Under Disruptions In Manufacturing Systems

This book provides an introduction to the models, methods, and results of some rescheduling problems in the presence of unexpected disruption events, including job unavailability, arrival of new jobs, and machine breakdown. The occurrence of these unexpected disruptions may cause a change in the planned schedule, which may render the originally feasible schedule infeasible. Rescheduling, which involves adjusting the original schedule to account for a disruption, is necessary in order to minimize the effects of the disruption on the performance of the system. This involves a trade-off between finding a cost-effective new schedule and avoiding excessive changes to the original schedule. This book views scheduling theory as practical theory, and it has made sure to emphasize the practical aspects of its topic coverage. Thus, this book considers some scenarios existing in most real-world environments, such as preventive machine maintenance, and deteriorating effect where the actual processing time of a job gets longer along with machine’s usage and age. To alleviate the effect of disruption events, some flexible strategies are adopted, including allocation extra resources to reduce job processing times or rejection the production of some jobs. For each considered scenario, depending on the model settings and on the disruption events, this book addresses the complexity, and the design of efficient exact or approximated algorithms. Especially when optimization methods and analytic tools fall short, this book stresses metaheuristics including improved elitist non-dominated sorting genetic algorithm and differential evolution algorithm. This book also provides extensive numerical studies to evaluate the performance of the proposed algorithms. The problem of rescheduling in the presence of unexpected disruption events is of great importance for the successful implementation of real-world scheduling systems. There is now an astounding body of knowledge in this field. This book is the first monograph on rescheduling. It aims at introducing the author's research achievements in rescheduling. It is written for researchers and Ph.D. students working in scheduling theory and other members of scientific community who are interested in recent scheduling models. Our goal is to enable the reader to know about some new achievements on this topic.

Rescheduling is an essential operating task to efficiently tackle uncertainties and unexpected events frequently encountered in today's complex and flexible manufacturing systems. The main purpose of this thesis is to develop a real time reactive scheduling methodology in order to respond to such disturbances and uncertainties in a cost efficient manner. In order to assess the impact of schedule changes, a compound rescheduling cost function is developed based on machine, job, and material related rescheduling activities. A Total Rescheduling (TR) approach based on the Filtered-Beam-Search-heuristic algorithm (FBS) is proposed to generate a prespecified number of cost efficient suboptimal schedules by using the proposed cost function in case of each disruption. Thereafter, the current schedule is replaced by the alternative schedule which causes the minimum rescheduling cost. Responding to each single disruption with TR may cause system nervousness and increase the operational cost. Hence, a partial rescheduling approach is developed by a Modified Filtered-Beam-Search-heuristic algorithm (MFBSR) in order to generate a prespecified number of sub optimal cost-efficient schedules with a lower rescheduling cost and fewer deviations than TR. In order to validate the performance of the proposed methodologies, TR and MFBSR, different case studies and experimental designs have been performed considering various disruption scenarios. The performance of the suggested methods in terms of rescheduling cost, makespan efficiency and stability have been compared with similar rescheduling and repair methods in the literature. The results reveal that the proposed methodologies could be considered as competitive methods in responding to disruptions in flexible manufacturing systems.

Basically five problems areas are addressed by operations research specialists in the manufacturing domain: theore- tical and practical aspectsin production planning, facility layout, inventory control, tool management and scheduling. Some of these problems can be solved off-line, while others must be treated as real-time problems impacted by the changing state of the system. Additionally, all of these problems have to be dealt with in an integrated systems framework. Several new topics have recently appeared in the scientific literature which now attract the interest of operations researchers. These include distributed real-time scheduling, hierarchical and heterarchical control systems, integrated algorithms for design, process planning, and equipment level programming, material handling in a finite capacity resource environment, and designing and implementing distributed data management systems. The contributions of these proceedings represent new andunique theoretical developments and applications related to these new topics. They deal with modelling production structures and applying expert systems or neural networks to production systems. Mathematical programming, control theory, simulation, genetic algorithms, tabu search, and simulated annealing are applied as solutiton techniques.

This book constitutes the proceedings of the 16th International Conference on Algorithmic Aspects in Information and Management, AAIM 2022, which was held online during August 13-14, 2022. The conference was originally planned to take place in Guangzhou, China, but changed to a virtual event due to the COVID-19 pandemic. The 41 regular papers included in this book were carefully reviewed and selected from 59 submissions.

The book reports on the latest advances and challenges of soft computing. It gathers original scientific contributions written by top scientists in the field and covering theories, methods and applications in a number of research areas related to soft-computing, such as decision-making, probabilistic reasoning, image processing, control, neural networks and data analysis.

This book has resulted from the activities of IFAC TC 5.2 “Manufacturing Modelling for Management and Control”. The book offers an introduction and advanced techniques of scheduling applications to cloud manufacturing and Industry 4.0 systems for larger audience. This book uncovers fundamental principles and recent developments in the theory and application of scheduling methodology to cloud manufacturing and Industry 4.0. The purpose of this book is to present recent developments in scheduling in cloud manufacturing and Industry 4.0 and to systemize these developments in new taxonomies and methodological principles to shape this new research domain. This book addresses the needs of both researchers and practitioners to uncover the challenges and opportunities of scheduling techniques’ applications to cloud manufacturing and Industry 4.0. For the first time, it comprehensively conceptualizes scheduling in cloud manufacturing and Industry 4.0 systems as a new research domain. The chapters of the book are written by the leading international experts and utilize methods of operations research, industrial engineering and computer science. Such a multi-disciplinary combination is unique and comprehensively deciphers major problem taxonomies, methodologies, and applications to scheduling in cloud manufacturing and Industry 4.0.

This book gathers the peer-reviewed papers presented at the seventh edition of the international workshop "Service Orientation in Holonic and Multi-Agent Manufacturing - SOHOMA'17", held on October 19-20, 2017 and organized by the University of Nantes, France in collaboration with the CIMR Research Centre in Computer Integrated Manufacturing and Robotics at the University Politehnica of Bucharest, Romania, the LAMIH Laboratory of Industrial and Human Automation Control, Mechanical Engineering and Computer Science at the University of Valenciennes and Hainaut-Cambrésis, France and the CRAN Research Centre for Automatic Control, Nancy at the University of Lorraine, France. The main objective of SOHOMA'17 was to foster innovation in smart and sustainable manufacturing and logistics systems and in this context to promote concepts, methods and solutions addressing trends in service orientation of agent-based control technologies with distributed intelligence. The book is organized in eight parts, each with a number of chapters describing research in current domains of the digital transformation in manufacturing and trends in future service and computing oriented manufacturing control: Part 1: Advanced Manufacturing Control, Part 2: Big Data Management, Part 3: Cyber-Physical Production Systems, Part 4: Cloud- and Cyber-Physical Systems for Smart and Sustainable Manufacturing, Part 5: Simulation for Physical Internet and Intelligent & Sustainable Logistics Systems, Part 6: Formal Methods and Advanced Scheduling for Future Industrial Systems, Part 7: Applications and Demonstrators, Part 8: Production and Logistic Control Systems. The contributions focus on how the digital transformation, such as the one advocated by "Industry 4.0" or "Industry of the future" concepts, can improve the maintainability and the sustainability of manufacturing processes, products, and logistics. Digital transformation relates to the interaction between the physical and informational worlds and is realized by virtualization of products, processes and resources managed as services.