Intelligent use of Constraints for Activity Scheduling

  • Navin Chandra
  • David H. Marks
Conference paper


The primary goal of this research effort was to develop a domain independent activity scheduling algorithm that would be able to handle ad-hoc constraints.

The activity scheduling problem is one of assigning tasks (activities) to objects (jobs) while adhering to time and resource constraints. Operations researchers originally had approached the problem using mathematical programming techniques. This approach, however, is poor at solving real world problems. Real World problems tend to be very large and are often too complex to represent numerically.

An algorithm is presented that is based on an heuristic search paradigm. Symbolic constraints are used to assist the search process. The scheduling problem is represented as a group of variables. Each variable has a corresponding set of possible values, called a value set. The aim is to assign each variable a value from its value set while adhering to the imposed constraints. The schedule is deemed complete as soon as the first set of variable assignments which satisfy all the constraints is obtained. The program does not make any attempts to optimize the solution.

The role of constraints in activity scheduling is examined and intuitions about constraint generation, posting & propagation are presented.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References & Bibliography

  1. Bansal, S.P. 1977 “Minimizing the Sum of Completion Times of a n Job over m machines in a Flowshop- A Branch & Bound Approach”, AIIE Trans, Vol 9, No 3, Sept 1977.Google Scholar
  2. Chandra Navin, 1985a “IMST user’s Manual: A tool for building Rule based Expert Systems” MIT, Center for Construction Research & Education, Technical Report: CCRE-85–6.Google Scholar
  3. Chandra, Navin, 1985b “Intelligent Use of Constraint in Activity Scheduling” M.S. Thesis. MIT, Center for Construction Research & Education, Technical Report: CCRE-85–8.Google Scholar
  4. Fikes R.E. (1970) “REF-ARF: A system for Solving Problems Stated as Procedures”, Artificial Intelligence, Vol1, pp27–120Google Scholar
  5. Fox, M.S. (1983) “Constraint Directed Search: A case of Job Shop Scheduling”. PhD Thesis, Carnegie-Mellon University.Google Scholar
  6. Fukumori K., (1980) “Fundamental Scheme for train Scheduling”, MIT AI Memo No 596, Artificial Intelligence Laboratory, MIT, Cambridge MA.Google Scholar
  7. Goldstein I.P., Robert R.B. (1977) “NUDGE: A knowledge-based Scheduling program,” MIT AI Memo 405.Google Scholar
  8. Ouciuch Ed, Frost John (1985) “ISA: Intelligent Scheduling Assistant”, AI Technology Center, Digital Equipment Corp. Hudson MA 01749Google Scholar
  9. Sacerdoti, E.D. (1977) “A Structure for Plans and Behaviour.” NY: Elsevier North-Holland, 1977. AI Series.Google Scholar
  10. Stallman, R. and G.J. Sussman (1977) “Forward reasoning and dependency directed backtracking in a system for computer aided circuit-analysis”, AI 9:135–196.zbMATHGoogle Scholar
  11. Stefik M. (1981) “Planning with Constraints (MOLGEN: Parti)”, AI, Vol 16, pp 111–140.Google Scholar
  12. Stefik M. (1980) “Planning with Constraints”, STAN-CS-80–784, PhD. ThesisGoogle Scholar
  13. Stinson Joel, David Edward, Khumwala Basheeer (1978). “Multiple Resource Constrained Scheduling using Branch & Bound”, AIIE Trans, Vol 10, No 3, Sept 1978.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1986

Authors and Affiliations

  • Navin Chandra
    • 1
  • David H. Marks
    • 1
  1. 1.Intelligent Engineering Systems Laboratory, Department of Civil EngineeringMassachusetts Institute of TechnologyUSA

Personalised recommendations