Future paths for integer programming and links to artificial intelligence

Abstract Integer programming has benefited from many innovations in models and methods. Some of the promising directions for elaborating these innovations in the future may be viewed from a framework that links the perspectives of artificial intelligence and operations research. To demonstrate this, four key areas are examined: 1. (1) controlled randomization, 2. (2) learning strategies, 3. (3) induced decomposition and 4. (4) tabu search. Each of these is shown to have characteristics that appear usefully relevant to developments on the horizon.