Subhash C. Sarin

Stochastic scheduling is in the area of production scheduling. There is a dearth of work that analyzes the variability of schedules. In a stochastic environment, in which the processing time of a job is not known with certainty, a schedule is typically analyzed based on the expected value of a performance measure. This book addresses this problem and presents algorithms to determine the variability of a schedule under various machine configurations and objective functions. It is intended for graduate and advanced undergraduate students in manufacturing, operations management, applied mathematics, and computer science, and it is also a good reference book for practitioners. Computer software containing the algorithms is provided on an accompanying website for ease of student and user implementation.

Lot streaming is a process of breaking a batch of jobs into smaller lots, and then processing these in an overlapping fashion on the machines. This important concept can significantly improve the overall performance of a production process, and thereby make the operation of a manufacturing system lean. Flow Shop Lot Streaming introduces the reader to this significant production process, presents various analysis techniques, and allows the reader to quickly become conversant with the state-of-the-art techniques necessary to embark on new research directions. This text begins with an introduction to and a brief historical perspective of the lot streaming problem, and continues with generic mathematical models for this problem. Flow Shop Lot Streaming presents systematic analysis, algorithms, key ideas and illustrative examples using 2-machine, 3-machine, and the general m-machine flow shop lot streaming problems. Flow Shop Lot Streaming will appeal to production and operations management engineers, researchers, and academics interested in implementing the latest models, analysis, and algorithms in the study of manufacturing systems.

Stochastic scheduling is in the area of production scheduling. There is a dearth of work that analyzes the variability of schedules. In a stochastic environment, in which the processing time of a job is not known with certainty, a schedule is typically analyzed based on the expected value of a performance measure. This book addresses this problem and presents algorithms to determine the variability of a schedule under various machine configurations and objective functions. It is intended for graduate and advanced undergraduate students in manufacturing, operations management, applied mathematics, and computer science, and it is also a good reference book for practitioners. Computer software containing the algorithms is provided on an accompanying website for ease of student and user implementation.

Lot streaming is a process of breaking a batch of jobs into smaller lots, and then processing these in an overlapping fashion on the machines. This important concept can significantly improve the overall performance of a production process, and thereby make the operation of a manufacturing system lean. Flow Shop Lot Streaming introduces the reader to this significant production process, presents various analysis techniques, and allows the reader to quickly become conversant with the state-of-the-art techniques necessary to embark on new research directions. This text begins with an introduction to and a brief historical perspective of the lot streaming problem, and continues with generic mathematical models for this problem. Flow Shop Lot Streaming presents systematic analysis, algorithms, key ideas and illustrative examples using 2-machine, 3-machine, and the general m-machine flow shop lot streaming problems. Flow Shop Lot Streaming will appeal to production and operations management engineers, researchers, and academics interested in implementing the latest models, analysis, and algorithms in the study of manufacturing systems.