.png){kind=icon}
.png){kind=icon}
Return
An LSTM network-based genetic algorithm for integrated procurement and scheduling optimisation
.png){kind=icon}
OA
AI
DOI：10.1080/00207543.2024.2434948.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Modern supply chains are characterised by high complexity, requiring effective management through coordinated activities across interrelated functions. This study aims to move from isolated optimisation to integrated decision-making, which offers new potential for efficiency. We investigate an integrated procurement-production problem based on a real case study from a German company specialising in printed circuit board assembly. We propose a novel solution approach that combines a genetic algorithm with a neural network to increase computational efficiency. Our comprehensive evaluation scheme demonstrates the viability of the approach in generating integrated decisions within a limited time frame. Specifically, we quantify the benefits of integrated over separated decision-making at the operational level, extending previous research focussed on the tactical level. The results indicate considerable benefits of integrated decision-making across a wide range of cost factors, although the exact savings depend on specific cost parameters. In addition, we evaluate our model on a rolling horizon planning basis, which is crucial for modelling realistic supply chain behaviour and remains underrepresented in the literature.
Keywords:
Supply chain management
integrated procurement production problem
hybrid flow shop scheduling
genetic algorithm
supervised learning
rolling horizon planning
Journal
IF:
7.3
Papers:
1.1W
Citations:
3.7W