BOOK ANNOUNCEMENT ----------------- Title: Evolutionary Search and the Job Shop; Investigations on Genetic Algorithms for Production Scheduling Author: Dirk C. Mattfeld Series: Production and Logistics Editors: Horst Tempelmeier, University of Cologne Wolfgang Domschke, University of Darmstadt Andreas Drexl, University of Kiel Bernhard Fleischmann, University of Augsburg Hans-Otto Guenther, University of Berlin Hartmut Stadtler, University of Darmstadt Publisher: Springer/Physica Verlag, Heidelberg Pages: X, 152 pp., 62 figs., 30 tabs. Available: March 96, ISBN 3-7908-0917-9 Price: DM 75.00, approx. USD 50.00 Order: http://medusa.fb7.uni-bremen.de/Leute/dirk.html PREFACE ------- Production planning and control systems suffer from insufficient computational support in the field of production scheduling. Practical requirements dictate highly constrained mathematical models with complex and often contradicting objectives. Therefore scheduling even in computerized manufacturing systems still relies on simple priority rule based heuristics. Thus, we can expect a great so far unexploited optimization potential in manufacturing environments. Within the last decade academic research in scheduling has gained a significant progress due to modern Local Search based heuristics. Much effort has been put into suitable neighborhood definitions which go for the key feature of Local Search. However, it remains questionable whether this work can be transferred in order to fit the flexible requirements of production scheduling. Evolutionary Algorithms can be formulated almost independently of the detailed shaping of the problems under consideration. As one would expect, a weak formulation of the problem in the algorithm comes along with a quite inefficient search. Nevertheless, for practical requirements the advantage of constraint and objective independence is most obvious. In this book Evolutionary Algorithms are applied to the Job Shop Scheduling Problem. The problem is analyzed and a survey is given on conventional solution techniques and recent Local Search approaches. Evolutionary Algorithms and their appliance to combinatorial problems are covered. Then, a search space analysis for the Job Shop Problem is performed before a Genetic Algorithm is developed. Finally, this algorithm is refined resulting in a parallel genetic search approach. The benefit of this book is twofold. It gives a comprehensive survey of recent advances for both, production scheduling and Evolutionary Algorithms in the didactic way of a textbook. Moreover, it presents an efficient and robust optimization strategy which can cope with varying constraints and objectives of real world scheduling problems.