Integrating AI and OR: an industrial engineering perspective

Authors:
Irem Ozkarahan;Seyda Topaloglu;Ceyhun Araz;Bilge Bilgen;Hasan Selim
Affiliations:
Industrial Engineering Department, Dokuz Eylul University, Izmir, Turkey;Industrial Engineering Department, Dokuz Eylul University, Izmir, Turkey;Industrial Engineering Department, Dokuz Eylul University, Izmir, Turkey;Industrial Engineering Department, Dokuz Eylul University, Izmir, Turkey;Industrial Engineering Department, Dokuz Eylul University, Izmir, Turkey
Venue:
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
Year:
2004

Citing 12
Cited 0

A new polynomial-time algorithm for linear programming

Combinatorica
Concepts and applications of backup coverage

Management Science
Priority structure in fuzzy goal programming

Fuzzy Sets and Systems
Fuzzy goal programming- an additive model

Fuzzy Sets and Systems
Operations research: the next decade committee on the next decade in operations research

Operations Research
Constraint satisfaction in logic programming

Constraint satisfaction in logic programming
Minimizing total tardiness on one machine is NP-hard

Mathematics of Operations Research
The maximal covering location problem with capacities on total workload

Management Science
A scheduling model for hospital residents

Journal of Medical Systems
OPL Script: Composing and Controlling Models

Selected papers from the Joint ERCIM/Compulog Net Workshop on New Trends in Contraints
Artificial intelligence and operations research: challenges and opportunities in planning and scheduling

The Knowledge Engineering Review
On the use of integer programming models in AI planning

IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.