This article presents a case-based reasoning approach for the development of learning heuristics for solving repetitive operations research problems. We first define the subset of problems we will consider in this work: repetitive combinatorial optimization problems. We then present several general forms that can be used to select previously solved problems that might aid in the solution of the current problem, and several different techniques for actually using this information to derive a solution for the current problem. We describe both fixed forms and forms that have the ability to change as we solve more problems. A simple example for the 0–1 knapsack problem is presented.
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Kraay, D.R., Harker, P.T. Case-based reasoning for repetitive combinatorial optimization problems, part I: Framework. J Heuristics 2, 55–85 (1996). https://doi.org/10.1007/BF00226293
- artificial intelligence
- combinatorial optimization