Scheduling is a resource allocation problem which exists in virtually every type of organization. Scheduling problems have produced roughly 40 years of research primarily within the OR community. This community has traditionally emphasized mathematical modeling techniques which seek exact solutions to well formulated optimization problems. While this approach produced important results, many contemporary scheduling problems are particularly difficult. Hence, over the last ten years operations researchers interested in scheduling have turned increasingly to more computer intensive and heuristic approaches. At roughly the same time, researchers in AI began to focus their methods on industrial and management science applications. The result of this confluence of fields has been a period of remarkable growth and excitement in scheduling research. Intelligent Scheduling Systems captures the results of a new wave of research at the forefront of scheduling research, of interest to researchers and practitioners alike. Presented are an array of the latest contemporary tools -- math modeling to tabu search to genetic algorithms -- that can assist in operational scheduling and solve difficult scheduling problems. The book presents the most recent research results from both operations research (OR) and artificial intelligence (AI) focusing their efforts on real scheduling problems.

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Preface. Issues in Scheduling: A Survey of Intelligent Scheduling Systems; D. Brown, J. Marin, W. Scherer. Schedulers & Planners: What and How Can we Learn from Them? K. McKay, F. Safayeni, J. Buzacott. Decision-Theoretic Control of Constraint Satisfaction and Scheduling; O. Hansson, A. Mayer. Guided Forward Search in Tardiness Scheduling of Large One Machine Problems; T. Morton, P. Ramnath. Production Scheduling: An Overview of Tabu Search Approaches to Production Scheduling Problems; J. Barnes, M. Laguna, F. Glover. Measuring the Quality of Manufacturing Schedules; K. Gary, R. Uzsoy, S.P. Smith, K. Kempf. A Methodology and Architecture for Reactive Scheduling; S.F. Smith. Intelligent Scheduling with Machine Learning; S. Park, S. Piramuthu, N. Raman, M. Shaw. Transportation Scheduling: Solving Large Integer Programs Arising from Air Traffic Flow Problems; R. Burlingame, A. Boyd, K. Lindsay. Intelligent Scheduling Support for the U.S. Coastguard; K. Darby-Dowman, C. Lucas, G. Mitra, R. Fink, L. Kingsley, J. Smith. Index.