Flowshop NEH-Based Heuristic Recommendation.

Flowshop problems (FSPs) have many variants and a broad set of heuristics proposed to solve them. Choosing the best heuristic and its parameters for a given FSP instance can be very challenging for practitioners. Per-instance Algorithm Configuration (PIAC) approaches aim at recommending the best algorithm configuration for a particular instance problem. This paper presents a PIAC methodology for building models to automatically configure the Nawaz, Encore, and Ham (NEH) algorithm which proved to be a good choice in most FSP variants (especially when they are used to provide initial solutions). We use irace to build the performance dataset (problem features \(\leftrightarrow \) algorithm configuration), while training Decision Tree and Random Forest models to recommend NEH configurations on unseen problems of the test set. Results show that the recommended heuristics have good performance, especially those by random forest models considering parameter dependencies.