An Integrated Hierarchy Of Production Planning And Scheduling For Semiconductor Manufacturing

This dissertation is composed of three major parts, each studying a problem related to semiconductor manufacturing. The first part of the dissertation proposes a high-level scheduling model that serves as an intermediate stage between planning and detailed scheduling in the usual planning hierarchy. The high-level scheduling model explicitly controls the WIP over time in the system and provides a more specific guide to detailed scheduling. WIP control is used to balance the WIP (Work In Process) level and to keep the bottleneck station busy to maintain a high throughput rate. A mini-fab simulation model is used to evaluate the benefits of different approaches to implementing such a high-level scheduling model, and to compare different WIP control policies. Extensive numerical studies show that the proposed approaches can achieve much shorter cycle times than the traditional planning-scheduling approach, with only a small increase in inventory and backorder costs. With increasing worldwide competition, high technology product manufacturing companies have to pay great attention to lower their production costs and guarantee high quality at the same time. Advanced process control (APC) is widely used in semiconductor manufacturing to adjust machine parameters so as to achieve satisfactory product quality. The interaction between scheduling and APC motivates the second part of this dissertation. First, a single-machine makespan problem with APC constraints is proved to be NPcomplete. For some special cases, an optimal solution is obtained analytically. In more general cases, the structure of optimal solutions is explored. An efficient heuristic algorithm based on these structural results is proposed and compared to an integer programming approach. Another important issue in manufacturing system is maintenance, which affects cycle time and yield management. Although there is extensive literature regarding maintenance policies, the analysis in most papers is restricted to conventional preventive maintenance (PM) policies, i.e., calendar-based or jobbased PM policies. With the rapid development of new technology, predictive maintenance has become more feasible, and has attracted more and more attention from semiconductor manufacturing companies in recent years. Thus, the third problem considered in this dissertation is predictive maintenance in an M/G/1 queueing environment. One-recipe and two-recipe problems are studied through semi-Markov decision processes (SMDP), and structural properties are obtained. Discounted SMDP problems are solved by linear programming and expected machine availabilities are calculated to evaluate different PM policies. The optimal policy can maintain a high machine availability with low long-run cost. The structures of the optimal PM policies show that it is necessary to consider multiple recipes explicitly in predictive maintenance models.

This book is dedicated to questions of production planning and scheduling activities both in general and in semiconductor manufacturing environments, which have the characteristics of high volume and high mixture. It explores topics such as shop models, work-in-process management, the treatment of setup times, basic techniques of lot batching and splitting, lot sizing and group technology approaches, as well as rescheduling questions. A number of directions for further research is suggested in the book, and a broad collection of references is provided.

If one accepts the premise that there is no wealth without production, whether at the individual or national level, one is immediately led to the conclusion that the study of productive systems lies at the forefront of subjects that should be intensively, as well as rationally and extensively, studied to achieve the desired 'sustainable growth' of society, where the latter is defined as growth in the quality of life that does not waste the available resources in the long run. Since the end of World War II there has been a remarkable evolution in thinking about production, abetted to a large measure by the nascent field of informatics: the computer technology and the edifices that have been built around it, such as information gathering and dissemination worldwide through communication networks, software products, peripheral interfaces, etc. Additionally, the very thought processes that guide and motivate studies in production have undergone fundamental changes which verge on being revolutionary, thanks to developments in operations research and cybernetics.

Patricia Shiroma explores the possibility of combining genetic algorithms with simulation studies in order to generate efficient production schedules for parallel manufacturing processes. The result is a flexible, highly effective production scheduling system.

At the crossroads of artificial intelligence, manufacturing engineering, operational research and industrial engineering and management, multi-agent based production planning and control is an intelligent and industrially crucial technology with increasing importance. This book provides a complete overview of multi-agent based methods for today’s competitive manufacturing environment, including the Job Shop Manufacturing and Re-entrant Manufacturing processes. In addition to the basic control and scheduling systems, the author also highlights advance research in numerical optimization methods and wireless sensor networks and their impact on intelligent production planning and control system operation. Enables students, researchers and engineers to understand the fundamentals and theories of multi-agent based production planning and control Written by an author with more than 20 years’ experience in studying and formulating a complete theoretical system in production planning technologies Fully illustrated throughout, the methods for production planning, scheduling and controlling are presented using experiments, numerical simulations and theoretical analysis Comprehensive and concise, Multi-Agent Based Production Planning and Control is aimed at the practicing engineer and graduate student in industrial engineering, operational research, and mechanical engineering. It is also a handy guide for advanced students in artificial intelligence and computer engineering.

The paper describes the development of a hierarchical planning and scheduling system for a multiple plant, multiple product, seasonal demand situation. In this hierarchical structure, optimal decisions at an aggregate level (planning) provide constraints for detailed decision making (scheduling). (Modified author abstract).

Over the last fifty-plus years, the increased complexity and speed of integrated circuits have radically changed our world. Today, semiconductor manufacturing is perhaps the most important segment of the global manufacturing sector. As the semiconductor industry has become more competitive, improving planning and control has become a key factor for business success. This book is devoted to production planning and control problems in semiconductor wafer fabrication facilities. It is the first book that takes a comprehensive look at the role of modeling, analysis, and related information systems for such manufacturing systems. The book provides an operations research- and computer science-based introduction into this important field of semiconductor manufacturing-related research.