An LSTM network-based genetic algorithm for integrated procurement and scheduling optimisation
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 integ...
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| Published in | International journal of production research Vol. 63; no. 11; pp. 4036 - 4065 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Taylor & Francis
03.06.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0020-7543 1366-588X 1366-588X |
| DOI | 10.1080/00207543.2024.2434948 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Lang, Sebastian Bubak, Alexander Reggelin, Tobias Rolf, Benjamin Stuckenschmidt, Heiner |
| Author_xml | – sequence: 1 givenname: Alexander orcidid: 0000-0003-0362-5500 surname: Bubak fullname: Bubak, Alexander email: alexander.bubak@uni-mannheim.de organization: Universität Mannheim – sequence: 2 givenname: Benjamin orcidid: 0000-0002-5454-8894 surname: Rolf fullname: Rolf, Benjamin organization: Otto-von-Guericke-Universität Magdeburg – sequence: 3 givenname: Tobias orcidid: 0000-0003-3001-9821 surname: Reggelin fullname: Reggelin, Tobias organization: Otto-von-Guericke-Universität Magdeburg – sequence: 4 givenname: Sebastian orcidid: 0000-0003-3397-1551 surname: Lang fullname: Lang, Sebastian organization: Fraunhofer-Institut für Fabrikbetrieb und -automatisierung IFF – sequence: 5 givenname: Heiner orcidid: 0000-0002-0209-3859 surname: Stuckenschmidt fullname: Stuckenschmidt, Heiner organization: Universität Mannheim |
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| SubjectTerms | GA: Genetic algorithm genetic algorithm hybrid flow shop scheduling integrated procurement production problem LSTM: Long short-term memory MILP:Mixed-integer linear program OAP: Order allocation problem OR: Operations research PCB: Printed circuit board RNN: Recurrent neural network rolling horizon planning supervised learning Supply chain management TS: Tabu search VNS:Variable neighbourhood search |
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| Title | An LSTM network-based genetic algorithm for integrated procurement and scheduling optimisation |
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