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 F. Roehrig",
  title =        "A Genetic Programming Approach for Heuristic Selection
                 in Constrained Project Scheduling",
  booktitle =    "Interfaces in Computer Science and Operations
                 Research: Advances in Metaheuristics, Optimization, and
                 Stochastic Modeling Technologies",
  publisher =    "Kluwer Academic Publishers",
  year =         "1997",
  editor =       "Richard S. Barr and Richard V. Helgason and 
                 Jeffrey L. Kennington",
  chapter =      "18",
  pages =        "405--421",
  address =      "Norwell, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0792398448",
  URL =          "http://www.amazon.co.uk/Interfaces-Computer-Science-Operations-Research/dp/0792398440",
  DOI =          "doi:10.1007/978-1-4615-4102-8_18",
  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
                 problem. This is a complex combinatorial optimization
                 problem which precludes the development of optimal
                 schedules for large projects. Many heuristics exist 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
                 which are an improvement on earlier methods. The GP
                 solution also gives valuable 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 =        "GP used to select which of 16 predefined schduling
                 heuristic to use. Test case 1440 randomly generated
                 project networks of 144 types chosen to span a domain.
                 GP better than ANN approach. cf


Genetic Programming entries for Rema Padman Stephen F Roehrig