Springer Flow Shop Lot Streaming

This book discusses the process of "Lot Streaming" and how it can significantly improve the overall performance of a production process, and thereby, make the operation of a manufacturing system lean. It provides a complete introduction to the Flow Shop Lot Streaming Problem and provides a historical perspective. It further presents algorithms for a variety of lot streaming problems with numerical illustrations for ease of understanding and implementation. Review From the reviews: "This book is devoted to a ... topic of flow shop lot-streaming. ... The book consists of five chapters. ... The book will be suitable for researchers and post-graduate students in scheduling and/or operations management interested in combining scheduling with various logistics decisions ... as well as for practitioners who work in mass production and need advanced methods for the production planning at the shop flow level." (Vitaly A. Strusevich, Zentralblatt MATH, Vol. 1138 (16), 2008) From the Back Cover 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.