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21 October 2021
Article (Scientific journals)
Solving inventory routing with transshipment and substitution under dynamic and stochastic demands using genetic algorithm and deep reinforcement learning
Achamrah, Fatima Ezzahra; Riane, Fouad; Limbourg, Sabine 
2021 • In International Journal of Production Research
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Keywords :
Dynamic and stochastic routing; Transshipment; Substitution; Genetic algorithm; deep reinforcement learning
Abstract :
[en] In this paper, we investigate a two-level supply chain consisting of a company which manufactures a set of products and distributes them via its central warehouse to a set of customers. The problem is modelled as a dynamic and stochastic inventory routing problem (DSIRP) that considers two flexible instruments of transshipment and substitution to mitigate shortages at the customer level. A new resolution approach, based on the hybridisation of mathematical modelling, Genetic Algorithm and Deep Reinforcement Learning is proposed to handle the combinatorial complexity of the problem at hand. Tested on the 150 most commonly used benchmark instances for single-vehicle-product DSIRP, results show that the proposed algorithm outperforms the current best results in the literature for medium and large instances. Moreover, 450 additional instances for multi-products DSIRP are generated. Different demand distributions are examined in these experiments, namely, Normal distribution, Poisson distribution for demand occurrence, combined with demands of constant size; Stuttering Poisson distribution and Negative Binomial distribution. In terms of managerial insights, results show the advantages of promoting inventory sharing and substitutions on the overall supply chain performance.
Disciplines :
Quantitative methods in economics & management
Author, co-author :
Achamrah, Fatima Ezzahra
Riane, Fouad
Limbourg, Sabine  ;  Université de Liège - ULiège > HEC Liège : UER > UER Opérations : Logistique
Language :
English
Title :
Solving inventory routing with transshipment and substitution under dynamic and stochastic demands using genetic algorithm and deep reinforcement learning
Publication date :
2021
Journal title :
International Journal of Production Research
ISSN :
0020-7543
eISSN :
1366-588X
Publisher :
Taylor and Francis, Abingdon, United Kingdom
Peer reviewed :
Peer Reviewed verified by ORBi

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