Integrating AI and OR: An Industrial Engineering

Many researchers have spent significant effort in developing techniques for solving hard combinatorial optimization problems. We see that both the Operations Research (OR) and the Artificial Intelligence (AI) communities are interested in solving these types of problems. OR focuses on tractable representations, such as linear programming whereas AI techniques provide richer and more flexible representations of real world problems. In this paper, we attempt to demonstrate the impressive impact of OR and AI integration. First we discuss opportunities for integration of OR and AI. Then three applications are presented to demonstrate how OR and AI are integrated.

[1]  T. Fry,et al.  A Heuristic Solution Procedure to Minimize T on a Single Machine , 1989 .

[2]  V. Klee,et al.  HOW GOOD IS THE SIMPLEX ALGORITHM , 1970 .

[3]  Hanif D. Sherali,et al.  Hospital resident scheduling problem , 2002 .

[4]  Charles ReVelle,et al.  Concepts and applications of backup coverage , 1986 .

[5]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[6]  Edward L. Hannan,et al.  SOME FURTHER COMMENTS ON FUZZY PRIORITIES , 1981 .

[7]  Pascal Van Hentenryck.  Constraint satisfaction in logic programming , 1989, Logic programming.

[8]  Christos Koulamas,et al.  A heuristic for the single machine tardiness problem , 1993 .

[9]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[10]  Chris N. Potts,et al.  A decomposition algorithm for the single machine total tardiness problem , 1982, Oper. Res. Lett..

[11]  Christoph Beierle,et al.  Logic Programming: Formal Methods and Practical Applications , 1994 .

[12]  Thomas E. Morton,et al.  Heuristic scheduling systems : with applications to production systems and project management , 1993 .

[13]  R. Tiwari,et al.  Fuzzy goal programming- an additive model , 1987 .

[14]  R. Narasimhan.  GOAL PROGRAMMING IN A FUZZY ENVIRONMENT , 1980 .

[15]  R. Tiwari,et al.  Priority structure in fuzzy goal programming , 1986 .

[16]  H. Pirkul,et al.  The Maximal Covering Location Problem with Capacities on Total Workload , 1991 .

[17]  H. Zimmermann.  Fuzzy programming and linear programming with several objective functions , 1978 .

[18]  Roy E. Marsten,et al.  Feature Article - Interior Point Methods for Linear Programming: Computational State of the Art , 1994, INFORMS J. Comput..

[19]  D. T. Seitman,et al.  A Recursive Computer Program To Compute The Daily On-call Assignments For A Medical Department , 1990, [1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Herbert A. Simon.  Two Heads Are Better than One: The Collaboration between AI and OR , 1987 .

[21]  A. J. Clewett,et al.  Introduction to sequencing and scheduling , 1974 .

[22]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[23]  I. Ozkarahan.  A scheduling model for hospital residents , 1994, Journal of Medical Systems.

[24]  Dana S. Nau,et al.  On the Use of Integer Programming Models in AI Planning , 1999, IJCAI.

[25]  Chris N. Potts,et al.  Constraint satisfaction problems: Algorithms and applications , 1999, Eur. J. Oper. Res..

[26]  Vladimir Marianov,et al.  Siting Emergency Services , 1995 .

[27]  Joseph Y.-T. Leung,et al.  Minimizing Total Tardiness on One Machine is NP-Hard , 1990, Math. Oper. Res..

[28]  Narendra Karmarkar,et al.  A new polynomial-time algorithm for linear programming , 1984, Comb..

[29]  A. Charnes,et al.  Goal programming and multiple objective optimizations: Part 1 , 1977 .

[30]  Robert M. Haralick,et al.  Increasing Tree Search Efficiency for Constraint Satisfaction Problems , 1979, Artif. Intell..

[31]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .