2023
Tobias Benecke; Oliver Antons; Sanaz Mostaghim; Julia C. Arlinghaus
A Generalized Circular Supply Chain Problem for Multi-Objective Evolutionary Algorithms Proceedings Article
In: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pp. 355–358, Association for Computing Machinery, Lisbon, Portugal, 2023, ISBN: 9798400701207.
Abstract | Links | BibTeX | Tags: Benchmarking, Evolutionary algorithms, Multi-objective optimization, Supply chain optimization
@inproceedings{10.1145/3583133.3590742,
title = {A Generalized Circular Supply Chain Problem for Multi-Objective Evolutionary Algorithms},
author = {Tobias Benecke and Oliver Antons and Sanaz Mostaghim and Julia C. Arlinghaus},
url = {https://doi.org/10.1145/3583133.3590742},
doi = {10.1145/3583133.3590742},
isbn = {9798400701207},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Proceedings of the Companion Conference on Genetic and Evolutionary Computation},
pages = {355\textendash358},
publisher = {Association for Computing Machinery},
address = {Lisbon, Portugal},
series = {GECCO '23 Companion},
abstract = {The idea of a circular economy proves promising with the evergrowing need for more sustainable production methods and resource utilization. However, this introduces new challenges compared to the traditional, mostly linear production processes and often leads to a tradeoff between sustainability and costs. In these environments, multi-objective evolutionary algorithms (MOEAs) are a great tool to tackle the increased complexity of supply chains in a circular economy. While MOEAs have been used to optimize circular supply chain models in the past, it was usually done for specific industries and using standard operators. In this paper, we propose a generalized test problem to provide a tool for evaluating MOEAs with respect to a circular supply chain (CSC) problem. In this problem, we try to optimize the product plan as well as the material sourcing at the same time, considering the objectives of maximizing the profit and sustainable resource use.},
keywords = {Benchmarking, Evolutionary algorithms, Multi-objective optimization, Supply chain optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
The idea of a circular economy proves promising with the evergrowing need for more sustainable production methods and resource utilization. However, this introduces new challenges compared to the traditional, mostly linear production processes and often leads to a tradeoff between sustainability and costs. In these environments, multi-objective evolutionary algorithms (MOEAs) are a great tool to tackle the increased complexity of supply chains in a circular economy. While MOEAs have been used to optimize circular supply chain models in the past, it was usually done for specific industries and using standard operators. In this paper, we propose a generalized test problem to provide a tool for evaluating MOEAs with respect to a circular supply chain (CSC) problem. In this problem, we try to optimize the product plan as well as the material sourcing at the same time, considering the objectives of maximizing the profit and sustainable resource use.