DynStack - A Benchmarking Framework for Dynamic Optimization Problems in Warehouse Operations

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

2 Zitate (Scopus)

Abstract

Dynamic optimization problems (DOPs) are an underrepresented class in benchmarking evolutionary computation systems (ECS). Most benchmarks focus on more or less expensive problems, but which never change during the optimization. In real-world logistics operations however, dynamic changes and even uncertainty are natural and have to be dealt with. While evolutionary algorithms are certainly well suited methods to tackle such problems, the field lacks public and open source, easy-to-use, but still complex dynamic environments for comparing and further developing the methods. In this work, we highlight the framework that we have created and open sourced as part of the DynStack competition which was first held at GECCO 2020. We present the underlying principles of the framework, the architecture that eases the application, and potential ways to benchmark a range of methods. The environments implemented in this framework are real-world industrial scenarios, that have been simplified, but which still convey practical challenges in the application of ECS to real-world problems.

OriginalspracheEnglisch
TitelGECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference
Herausgeber (Verlag)Association for Computing Machinery, Inc
Seiten1984-1991
Seitenumfang8
ISBN (elektronisch)9781450392686
DOIs
PublikationsstatusVeröffentlicht - 9 Juli 2022
Veranstaltung2022 Genetic and Evolutionary Computation Conference, GECCO 2022 - Virtual, Online, USA/Vereinigte Staaten
Dauer: 9 Juli 202213 Juli 2022

Publikationsreihe

NameGECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference

Konferenz

Konferenz2022 Genetic and Evolutionary Computation Conference, GECCO 2022
Land/GebietUSA/Vereinigte Staaten
OrtVirtual, Online
Zeitraum09.07.202213.07.2022

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