A Genetic Programming Approach for Heuristic Selection in Constrained Project Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Rema Padman and Stephen Roehrig",
  title =        "A Genetic Programming Approach for Heuristic Selection
                 in Constrained Project Scheduling",
  institution =  "H. John Heinz III School of Public Policy and
                 Management, Carniege-Mellon University",
  year =         "1995",
  number =       "95-30",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The resource-constrained project scheduling problem
                 (RCPSP) with cash flows investigates the scheduling of
                 activities that are linked by precedence constraints
                 and multiple resource restrictions. Given the presence
                 of cash flows which represent expenses for initiating
                 activities and payments for completed work, maximizing
                 the Net Present Value of the project is a practical
                 objective. This is a complex combinatorial optimization
                 problem which precludes the development of optimal
                 schedules for large projects. Many heuristics exists
                 for the RCPSP, but it has proven difficult to decide in
                 advance which heuristic will provide the best result,
                 given a problem characterization in terms of parameters
                 such as size and complexity. In this paper we discuss
                 the use of genetic programming (GP) for heuristic
                 selection, and compare it directly to alternative
                 methods such as OLS regression and neural networks. The
                 study indicates that the GP approach yields results
                 that are an improvement on earlier methods. The GP
                 solution also gives valualbe information about project
                 environments where a given heuristic is inappropriate.
                 In addition, this approach has no problem evolving
                 complex nonlinear functions to capture the relationship
                 between problem parameters and heuristic performance.
                 Thus the results given in this paper shed light on the
                 logical domains of applicability of the various
                 heuristics, while at the same time provide an improved
                 heuristic selection process.",
  notes =        "http://www.heinz.cmu.edu/heinz/wpapers/active/wp00019.html

                 {"}On the Use of Genetic Programming for Selecting
                 Heuristics for Resource-Constrained Project Scheduling
                 - An Extended Abstract{"} (with Stephen Roehrig);
                 Proceedings of the Fourth International Workshop on
                 Project Management and Scheduling, Leuven, Belgium, pp.
                 129-132, 1994.

                 {"}A Genetic Programming Approach for Heuristic
                 Selection in Constrained Project Scheduling{"} (with
                 Rema Padman); Computer Science and Operations Research:
                 Recent Advances in the Interface (R. Helgason, ed.),
                 Kluwer, 1995.


Genetic Programming entries for Rema Padman Stephen F Roehrig