Improving Inventory Management in an Automotive Supply Chain: A Multi-objective Optimization Approach Using a Genetic Algorithm

  • João N. C. GonçalvesEmail author
  • M. Sameiro Carvalho
  • Lino Costa
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 278)


Inventory management represents a cornerstone inherent to any supply chain, regardless of industry type. Nevertheless, uncertainty phenomena related to demand and supply can induce overstock or even inventory stock-outs occurrences which, in turn, jeopardize one of the major principles of supply chain management: deliver the right product at the right place, at the right time and to the right cost. This situation may also be aggravated in automotive supply chains, due to their complexity in terms of entities involved. This research paper explores a multi-objective optimization model and applies it to a real industrial company, to address an inventory management problem. Moreover, a genetic algorithm is used to determine solutions corresponding to the order size and to a safety factor system. The obtained results are compared to the current strategy adopted by the company. At this point, the advantages and the drawbacks of the model implementation are assessed. Based on a set of logistic performance indicators, it is showed that the adoption of a smaller order size is potentially beneficial to the overall levels of inventory and to the value of inventory on–hand, without compromising the service level. Assertively, the proposed model reveals to be an useful tool to practitioners involved in automotive electronic supply chains.


Inventory management Multi-objective optimization Automotive supply chain management 



The authors would like to acknowledge the comments and suggestions from the two reviewers, which improved the quality of the paper. This work has been supported by ALGORITMI R&D Center, under COMPETE: POCI-01-0145-FEDER-007043 and FCT–Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • João N. C. Gonçalves
    • 1
    • 2
    Email author
  • M. Sameiro Carvalho
    • 1
    • 2
  • Lino Costa
    • 1
    • 2
  1. 1.Algoritmi R&D CenterUniversity of MinhoBragaPortugal
  2. 2.Department of Production and SystemsUniversity of MinhoBragaPortugal

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