Intelligent Scheduling Systems

1. Front Matter

   Pages i-x

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2. Issues in Scheduling

   1. A Survey of Intelligent Scheduling Systems

      • Donald E. Brown, John A. Marin, William T. Scherer

      Pages 1-40

   2. Schedulers & Planners: What and How Can We Learn From Them

      • Kenneth N. McKay, Frank R. Safayeni, John A. Buzacott

      Pages 41-62

   3. Decision-Theoretic Control of Constraint-Satisfaction and Scheduling

      • Othar Hansson, Andrew Mayer

      Pages 63-78

   4. Guided Forward Search in Tardiness Scheduling of Large One Machine Problems

      • Thomas E. Morton, Prasad Ramnath

      Pages 79-100

3. Production Scheduling

   1. An Overview of Tabu Search Approaches to Production Scheduling Problems

      • J. Wesley Barnes, Manuel Laguna, Fred Glover

      Pages 101-127

   2. Measuring the Quality of Manufacturing Schedules

      • Kevin Gary, Reha Uzsoy, Stephen P. Smith, Karl Kempf

      Pages 129-154

   3. Reactive Scheduling Systems

      • Stephen F. Smith

      Pages 155-192

   4. Intelligent Scheduling with Machine Learning

      • Sang C. Park, Selwyn Piramuthu, Narayan Raman, Michael J. Shaw

      Pages 193-214

4. Transportation Scheduling

   1. Solving Large Integer Programs Arising from Air Traffic Flow Problems

      • Rusty Burlingame, E. Andrew Boyd, Kenneth S. Lindsay

      Pages 215-226

   2. Intelligent Scheduling Support for the U.S. Coast Guard

      • Ken Darby-Dowman, Cormac Lucas, Gautam Mitra, Raymond Fink, Leonard Kingsley, J. Walter Smith

      Pages 227-247

5. Back Matter

   Pages 249-260

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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.

• Manufacturing
• algorithms
• genetic algorithms
• modeling
• operations research
• optimization
• organization
• production
• scheduling

• University of Virginia, USA

  Donald E. Brown, William T. Scherer

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