HAL CCSD
Optimization of (R, Q) policies for serial inventory systems using the guaranteed service approach
Chen, Haoxun
Li, Peng
Laboratoire d'Optimisation des Systèmes Industriels (LOSI) ; Institut Charles Delaunay (ICD) ; Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)
Xi'an University of Technology (XUT)
International audience
ISSN: 0360-8352
Computers & Industrial Engineering
Elsevier
hal-02500775
https://utt.hal.science/hal-02500775
https://utt.hal.science/hal-02500775
Computers & Industrial Engineering, 2015, 80, pp.261-273. ⟨10.1016/j.cie.2014.12.003⟩
DOI: 10.1016/j.cie.2014.12.003
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cie.2014.12.003
en
Inventory management
Multi-echelon inventory systems
Guaranteed-service approach
(R Q) policy
Dynamic programming
[INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO]
info:eu-repo/semantics/article
Journal articles
The guaranteed-service approach (GSA) was recently used to optimally place strategic safety stocks in multi-echelon inventory systems without fixed order costs. Under the GSA, excessive customer demand superior to a bound is fulfilled by using operating flexibility measures such as expediting and overtime. In this paper, the GSA is used to optimize the (R, Q) policy for a continuous review serial inventory system with Poisson demand and fixed order costs at each stocking location. Except for inventory holding costs, we also consider fixed order costs and operating flexibility costs in the system. Firstly, a deterministic mathematical programming model that considers the effects of using operating flexibility measures on the physical flow and the total cost of the system is established for optimizing the inventory policy under a given cycle service level. The model is solved by using an iterative procedure. In each iteration of the procedure, the fill rate of the system is estimated, and two sub-problems of the model, the order size decision sub-problem and the reorder point decision sub-problem, are solved by using dynamic programming algorithms for a given fill rate. With this procedure, the original optimization problem is solved by finding the optimal cycle service level of the system using a line search. Numerical results on randomly generated instances demonstrate the efficiency of the algorithms and the procedure.
2015-02