De-Supply: Deep Reinforcement Learning for Multivariate Supply Chain Optimization

Franck Romuald Fotso Mtope; Diptangshu Pandit; Sina Joneidy; Farzad Rahimian

doi:10.1007/978-981-96-4051-5_57

Conference paperChapter 57pp. 583 to 592

De-Supply: Deep Reinforcement Learning for Multivariate Supply Chain Optimization

Franck Romuald Fotso Mtope¹, Diptangshu Pandit¹, Sina Joneidy¹, Farzad Rahimian¹

Teesside University

Published in Proceedings of the International Conference on Smart and Sustainable Built Environment (SASBE 2024), edited by Ali GhaffarianHoseini, Amirhosein GhaffarianHoseini, Farzad Rahimian and Mahesh Babu Purushothaman. Springer Nature, Lecture Notes in Civil Engineering, volume 591, 2025, pages 583 to 592. DOI 10.1007/978-981-96-4051-5_57.

Read the full paper on Springer Nature All SASBE 2024 papers

Abstract

This paper presents De-Supply, a novel approach utilizing deep reinforcement learning for multi-objective supply-chain optimization. It formulates the problem as a Markov Decision Process with complex action and observation spaces, addressing real-world supply chain challenges. De-Supply’s custom policy network enables agents to make informed procurement decisions, resulting in efficient stock management and cost control. Extensive experiments demonstrate its effectiveness, using historical data to outperform baselines and human-level performance. De-Supply offers robust solutions for warehouse businesses and holds promise for broader applications in the industry.

Keywords

Supply Chain OptimizationInventory ManagementProcurementDeep Reinforcement Learning

Session

Presented in Track G, Friday 8 November 2024, 16:30 to 18:30, room WG 1001, Auckland University of Technology. Session chair Dr Funmilayo Ebun Rotimi.

How to Cite

Mtope, F. R. F., Pandit, D., Joneidy, S., & Rahimian, F. (2025). De-Supply: Deep Reinforcement Learning for Multivariate Supply Chain Optimization. In A. GhaffarianHoseini, A. GhaffarianHoseini, F. Rahimian, & M. B. Purushothaman (Eds.), Proceedings of the International Conference on Smart and Sustainable Built Environment (SASBE 2024) (Lecture Notes in Civil Engineering, Vol. 591, pp. 583–592). Springer Nature Singapore. https://doi.org/10.1007/978-981-96-4051-5_57

About the Conference

Presented at SASBE 2024, the International Conference on Smart and Sustainable Built Environment, held in Auckland from 7 to 9 November 2024 and chaired by Professors Ali and Amirhosein GhaffarianHoseini, founders of GDI Academy. The version of record is published by Springer Nature; this page is the conference archive record kept by GDI Academy.