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Flowshop NEH-Based Heuristic Recommendation

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12692))

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Abstract

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.

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Notes

  1. 1.

    The global best NEH found by irace uses the hill-sorted absolute difference of processing times with NM tie-breaking strategy.

  2. 2.

    Source code and supplementary material link: https://github.com/lucasmpavelski/flowshop-neh-based-heuristic-recommendation.

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Acknowledgments

M. Delgado acknowledges CNPq (a Brazilian research-funding agency) for her partial financial support, grants 309935/2017-2 and 439226/2018-0.

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Authors and Affiliations

  1. Federal University of Technology - Paraná, Av. Sete de Setembro - 3165 - 80230-901, Curitiba, Brazil

    Lucas Marcondes Pavelski

  2. Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, 59000, Lille, France

    Marie-Éléonore Kessaci

  3. Federal University of Technology - Paraná, Curitiba, Brazil

    Myriam Delgado

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  1. Lucas Marcondes Pavelski
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Correspondence to Lucas Marcondes Pavelski .

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Editors and Affiliations

  1. Aberystwyth University, Aberystwyth, UK

    Christine Zarges

  2. Université du Littoral Côte d'Opale, Calais, France

    Sébastien Verel

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Pavelski, L.M., Kessaci, MÉ., Delgado, M. (2021). Flowshop NEH-Based Heuristic Recommendation. In: Zarges, C., Verel, S. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2021. Lecture Notes in Computer Science(), vol 12692. Springer, Cham. https://doi.org/10.1007/978-3-030-72904-2_9

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