An Integrated Approach to Robust Multi-echelon Inventory Policy Decision

Part of the Studies in Computational Intelligence book series (SCI, volume 416)

Abstract

To cope with current turbulent market demands, robust multi-echelon inventory policies are needed for distribution networks in order to lower inventory costs as well as to maintain high responsiveness. This paper analyzes the inventory policies in the context of complex distribution networks and proposes a new integrated approach to robust multi-echelon inventory policy decision, which is composed of three interrelated components: an analytical inventory policy optimisation, a supply chain simulation module and a metaheuristic-based inventory policy optimiser. Based on the existing approximation algorithms designed primarily for two-echelon inventory policy optimisation, an analytical multi-echelon inventory model in combination with an efficient optimisation algorithm has been designed. Through systematic parameter adjustment, an initial generation of optimised multi-echelon inventory policies is calculated. To evaluate optimality and robustness of these multi-echelon inventory policies under market dynamics, they are automatically handed over to a simulation module, which is capable of modeling arbitrary complexity and uncertainties within and outside of a supply chain and simulating them under respective scenarios. Based on the simulation results, i.e. the robustness of the proposed strategies, a metaheuristic-based inventory policy optimiser regenerates improved (more robust) multi-echelon inventory policies, which are once again dynamically evaluated through simulation. This closed feedback loop forms a simulation optimisation process that enables the autonomous evolution of robust multi-echelon inventory policies. The proposed approach has further been validated by an industrial case study, in which favorable outcomes have been obtained.

Keywords

Supply Chain Distribution Network Inventory Policy Inventory Cost Reorder Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Chair of Factory OrganisationTechnical University of DortmundDortmundGermany
  2. 2.Department of Supply Chain EngineeringFraunhofer Institute for Material Flow and LogisticsDortmundGermany

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