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This book provides an in-depth presentation of algorithms for and complexity of open shop scheduling. Open shops allow operations of a job to be executed in any order, contrary to flow and job shops where the order is pre-specified. The author brings the field up to date with more emphasis on new and recent results, and connections with graph edge coloring and mathematical programming. The book explores applications to production and operations management, wireless network scheduling, and timetabling. The book is addressed to researchers, graduate students, and practitioners in Operations Research, Operations Management, computer science and mathematics, who are developing and using mathematical approaches to applications in manufacturing, services and distributed wireless network scheduling.
Scheduling theory is an important branch of operations research. Problems studied within the framework of that theory have numerous applications in various fields of human activity. As an independent discipline scheduling theory appeared in the middle of the fifties, and has attracted the attention of researchers in many countries. In the Soviet Union, research in this direction has been mainly related to production scheduling, especially to the development of automated systems for production control. In 1975 Nauka ("Science") Publishers, Moscow, issued two books providing systematic descriptions of scheduling theory. The first one was the Russian translation of the classical book Theory of Scheduling by American mathematicians R. W. Conway, W. L. Maxwell and L. W. Miller. The other one was the book Introduction to Scheduling Theory by Soviet mathematicians V. S. Tanaev and V. V. Shkurba. These books well complement each other. Both. books well represent major results known by that time, contain an exhaustive bibliography on the subject. Thus, the books, as well as the Russian translation of Computer and Job-Shop Scheduling Theory edited by E. G. Coffman, Jr., (Nauka, 1984) have contributed to the development of scheduling theory in the Soviet Union. Many different models, the large number of new results make it difficult for the researchers who work in related fields to follow the fast development of scheduling theory and to master new methods and approaches quickly.
This book comprises select proceedings of the International Conference on Emerging Trends in Mechanical Engineering (ICETME 2018). The book covers various topics of mechanical engineering like computational fluid dynamics, heat transfer, machine dynamics, tribology, and composite materials. In addition, relevant studies in the allied fields of manufacturing, industrial and production engineering are also covered. The applications of latest tools and techniques in the context of mechanical engineering problems are discussed in this book. The contents of this book will be useful for students, researchers as well as industry professionals.
This volume presents a collection of peer-reviewed, scientific articles from the 15th International Conference on Information Technology – New Generations, held at Las Vegas. The collection addresses critical areas of Machine Learning, Networking and Wireless Communications, Cybersecurity, Data Mining, Software Engineering, High Performance Computing Architectures, Computer Vision, Health, Bioinformatics, and Education.
This book is a printed edition of the Special Issue " Algorithms for Scheduling Problems" that was published in Algorithms
This book provides a theoretical and application-oriented analysis of deterministic scheduling problems in advanced planning and computer systems. The text examines scheduling problems across a range of parameters: job priority, release times, due dates, processing times, precedence constraints, resource usage and more, focusing on such topics as computer systems and supply chain management. Discussion includes single and parallel processors, flexible shops and manufacturing systems, and resource-constrained project scheduling. Many applications from industry and service operations management and case studies are described. The handbook will be useful to a broad audience, from researchers to practitioners, graduate and advanced undergraduate students.
This book approaches its subject matter by promoting concepts, methods and solutions for the digital transformation of manufacturing through service orientation in holonic and agent-based control with distributed intelligence. The scientific theme of the book concerns “Manufacturing as a Service”, developed by virtualizing and encapsulating manufacturing resources, activities and controls into cloud networked services in an open perspective that spans models from shop floor resource allocation to enterprise infrastructure sharing. The papers included in the application space have a profound human dedication and aim at solving societal needs serving the partnership of the future—people and industry in the era of Society 5.0. The book’s readership includes researchers and engineers working in manufacturing, supply chains and logistics areas who innovate, develop and use digital control solutions and students enrolled in Engineering and Service Science programs.
Meta-heuristics have developed dramatically since their inception in the early 1980s. They have had widespread success in attacking a variety of practical and difficult combinatorial optimization problems. These families of approaches include, but are not limited to greedy random adaptive search procedures, genetic algorithms, problem-space search, neural networks, simulated annealing, tabu search, threshold algorithms, and their hybrids. They incorporate concepts based on biological evolution, intelligent problem solving, mathematical and physical sciences, nervous systems, and statistical mechanics. Since the 1980s, a great deal of effort has been invested in the field of combinatorial optimization theory in which heuristic algorithms have become an important area of research and applications. This volume is drawn from the first conference on Meta-Heuristics and contains 41 papers on the state-of-the-art in heuristic theory and applications. The book treats the following meta-heuristics and applications: Genetic Algorithms, Simulated Annealing, Tabu Search, Networks & Graphs, Scheduling and Control, TSP, and Vehicle Routing Problems. It represents research from the fields of Operations Research, Management Science, Artificial Intelligence and Computer Science.
Besides scheduling problems for single and parallel machines and shop scheduling problems, the book covers advanced models involving due-dates, sequence dependent change-over times and batching. A discussion of multiprocessor task scheduling and problems with multi-purpose machines is accompanied by the methods used to solve such problems, such as polynomial algorithms, dynamic programming procedures, branch-and-bound algorithms and local search heuristics, and the whole is rounded off with an analysis of complexity issues.
During the past decades scheduling has been among the most studied op- mization problemsanditisstillanactiveareaofresearch!Schedulingappears in many areas of science, engineering and industry and takes di?erent forms depending on the restrictions and optimization criteria of the operating en- ronments [8]. For instance, in optimization and computer science, scheduling has been de?ned as “the allocation of tasks to resources over time in order to achieve optimality in one or more objective criteria in an e?cient way” and in production as “production schedule, i. e. , the planning of the production or the sequence of operations according to which jobs pass through machines and is optimal with respect to certain optimization criteria. ” Although there is a standardized form of stating any scheduling problem, namely “e?cient allocation ofn jobs onm machines –which can process no more than one activity at a time– with the objective to optimize some - jective function of the job completion times”, scheduling is in fact a family of problems. Indeed, several parameters intervene in the problem de?nition: (a) job characteristics (preemptive or not, precedence constraints, release dates, etc. ); (b) resource environment (single vs. parallel machines, un- lated machines, identical or uniform machines, etc. ); (c) optimization criteria (minimize total tardiness, the number of late jobs, makespan, ?owtime, etc. ; maximize resource utilization, etc. ); and, (d) scheduling environment (static vs. dynamic,intheformerthenumberofjobstobeconsideredandtheirready times are available while in the later the number of jobs and their charact- istics change over time).