DEPARTEMENT TOEGEPASTE ECONOMISCHE WETENSCHAPPEN PHASE TRANSITIONS IN PROJECT SCHEDULING

Researchers in the area of artificial intelligence have recently shown that many NP-complete problems exhibit phase transitions. Often, problem instances change from being easy to being hard to solve to again being easy to solve when certain of their characteristics are modified. Most often the transitions are sharp, but sometimes they are rather continuous in the order parameters that are characteristic of the system as a whole. To the best of our knowledge, no evidence has been provided so far that similar phase transitions occur in NP-hard scheduling problems. In this paper we report on the existence of phase transitions in various resource-constrained project scheduling problems. We discuss the use of network complexity measures and resource parameters as potential order parameters. We show that while the network complexity measures seem to reveal continuous easy-hard or hard-easy phase transitions, the resource parameters exhibit a relatively sharp easy-hard-easy transition behaviour.

[1]  E. M. Davies An Experimental Investigation of Resource Allocation in Multiactivity Projects , 1973 .

[2]  Bert De Reyck,et al.  A branch-and-bound procedure for the resource-constrained project scheduling problem with generalized precedence relations , 1998, Eur. J. Oper. Res..

[3]  Hector J. Levesque,et al.  Generating Hard Satisfiability Problems , 1996, Artif. Intell..

[4]  Toby Walsh,et al.  The Satisfiability Constraint Gap , 1996, Artif. Intell..

[5]  Richard E. Korf,et al.  An Average-Case Analysis of Branch-and-Bound with Applications: Summary of Results , 1992, AAAI.

[6]  Richard E. Korf,et al.  Performance of Linear-Space Search Algorithms , 1995, Artif. Intell..

[7]  Subhash C. Narula,et al.  Multi-Project Scheduling: Analysis of Project Performance , 1985 .

[8]  James M. Crawford,et al.  Implicates and Prime Implicates in Random 3-SAT , 1996, Artif. Intell..

[9]  F. Brian Talbot,et al.  Resource-Constrained Project Scheduling with Time-Resource Tradeoffs: The Nonpreemptive Case , 1982 .

[10]  Nageshwara Rae Vempaty,et al.  Depth-First vs , 1991 .

[11]  Béla Bollobás,et al.  Random Graphs , 1985 .

[12]  Richard M. Karp,et al.  On the Computational Complexity of Combinatorial Problems , 1975, Networks.

[13]  Jerzy Kamburowski,et al.  Optimal Reductions of Two-Terminal Directed Acyclic Graphs , 1992, SIAM J. Comput..

[14]  Erik Demeulemeester,et al.  A branch-and-bound procedure for the multiple resource-constrained project scheduling problem , 1992 .

[15]  Erik Demeulemeester,et al.  New computational results on the discrete time/cost trade-off problem in project networks , 1998, J. Oper. Res. Soc..

[16]  A. A. Mastor,et al.  An Experimental Investigation and Comparative Evaluation of Production Line Balancing Techniques , 1970 .

[17]  Edward W. Davis,et al.  Project Network Summary Measures Constrained- Resource Scheduling , 1975 .

[18]  Richard E. Korf,et al.  Depth-First vs. Best-First Search: New Results , 1993, AAAI.

[19]  A. Thesen,et al.  Measures of the restrictiveness of project networks , 1977, Networks.

[20]  S Kirkpatrick,et al.  Critical Behavior in the Satisfiability of Random Boolean Expressions , 1994, Science.

[21]  David S. Johnson,et al.  Computers and In stractability: A Guide to the Theory of NP-Completeness. W. H Freeman, San Fran , 1979 .

[22]  Erik Demeulemeester,et al.  Optimal procedures for the discrete time/cost trade-off problem in project networks , 1996 .

[23]  Bart Selman,et al.  Critical Behavior in the Computational Cost of Satisfiability Testing , 1996, Artif. Intell..

[24]  Ezey M. Dar-El,et al.  MALB—A Heuristic Technique for Balancing Large Single-Model Assembly Lines , 1973 .

[25]  Roman Słowiński,et al.  Advances in project scheduling , 1989 .

[26]  Tad Hogg,et al.  Phase Transitions in Artificial Intelligence Systems , 1987, Artif. Intell..

[27]  Peter C. Cheeseman,et al.  Where the Really Hard Problems Are , 1991, IJCAI.

[28]  Hector J. Levesque,et al.  Hard and Easy Distributions of SAT Problems , 1992, AAAI.

[29]  Jon W. Freeman Hard Random 3-SAT Problems and the Davis-Putnam Procedure , 1996, Artif. Intell..

[30]  Robijn Bruinsma,et al.  Soft order in physical systems , 1994 .

[31]  Erik Demeulemeester,et al.  Resource-constrained project scheduling: A survey of recent developments , 1998, Comput. Oper. Res..

[32]  Hector J. Levesque,et al.  Some Pitfalls for Experimenters with Random SAT , 1996, Artif. Intell..

[33]  Richard A. Kaimann,et al.  Coefficient of Network Complexity , 1974 .

[34]  Christoph Schwindt,et al.  Generation of Resource-Constrained Project Scheduling Problems with Minimal and Maximal Time Lags , 1998 .

[35]  Weixiong Zhang,et al.  A Study of Complexity Transitions on the Asymmetric Traveling Salesman Problem , 1996, Artif. Intell..

[36]  Erik Demeulemeester,et al.  New Benchmark Results for the Resource-Constrained Project Scheduling Problem , 1997 .

[37]  Erik Demeulemeester,et al.  The discrete time/resource trade-off problem in project networks: a branch-and-bound approach , 2000 .

[38]  James M. Crawford,et al.  Experimental Results on the Crossover Point in Random 3-SAT , 1996, Artif. Intell..

[39]  Gregory M. Provan,et al.  An Expected-Cost Analysis of Backtracking and Non-Backtracking Algorithms , 1991, IJCAI.

[40]  Willy Herroelen,et al.  Assembly line balancing by resource-constrained project scheduling techniques - A critical appraisal , 1996 .

[41]  Dicky C. K. Yan,et al.  Designing tributary networks with multiple ring families , 1998, Comput. Oper. Res..

[42]  Weixiong Zhang,et al.  Epsilon-Transformation: Exploiting Phase Transitions to Solve Combinatorial Optimization Problems Initial Results , 1994, AAAI.

[43]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[44]  Rainer Kolisch,et al.  Characterization and generation of a general class of resource-constrained project scheduling problems , 1995 .

[45]  Willy Herroelen,et al.  On the use of the complexity index as a measure of complexity in activity networks , 1996 .

[46]  Toby Walsh,et al.  The SAT Phase Transition , 1994, ECAI.

[47]  Erik Demeulemeester An optimal recursive search procedure for the deterministic unconstrained max-nvp project scheduling problem , 1996 .

[48]  Salah E. Elmaghraby,et al.  On the measurement of complexity in activity networks , 1980 .

[49]  Erik Demeulemeester,et al.  A classification scheme for project scheduling problems , 1998 .

[50]  Erik Demeulemeester,et al.  Local search methods for the discrete time/resource trade‐off problem in project networks , 1998 .

[51]  Toby Walsh,et al.  The TSP Phase Transition , 1996, Artif. Intell..

[52]  Tad Hogg,et al.  Phase Transitions and the Search Problem , 1996, Artif. Intell..

[53]  Brian Hayes,et al.  CAN’T GET NO SATISFACTION , 1997 .

[54]  Edward P. C. Kao,et al.  On Dynamic Programming Methods for Assembly Line Balancing , 1982, Oper. Res..

[55]  Richard M. Karp,et al.  Searching for an Optimal Path in a Tree with Random Costs , 1983, Artif. Intell..

[56]  Tad Hogg,et al.  Refining the Phase Transition in Combinatorial Search , 1996, Artif. Intell..

[57]  James H. Patterson,et al.  Project scheduling: The effects of problem structure on heuristic performance , 1976 .