A multi-item integrated inventory model with different replenishment frequencies of retailers in a two-echelon supply chain management: a tuned-parameters hybrid meta-heuristic

As replenishment frequencies play an important role in integrated inventory models to reduce the total cost of supply chains, this paper extends a vendor managed inventory (VMI) model for a two-echelon supply chain management, which several retailers are replenished with a different rate for several items by one vendor. While a vendor supplies several products to retailers, the vendor’s warehouse has a capacity constraint. The aim of this paper is to find replenishment frequencies and order quantities to minimize the total inventory cost. The proposed model is a mixed-integer nonlinear programming (MINLP), which means that it is unable to be solved by exact methods in large-scale problems. Thus, a genetic algorithm (GA), as a meta-heuristic optimization, is employed to solve it. The proposed algorithm is verified with an exact solver namely Couenne using GAMS software. Moreover, to improve proposed GA, it is hybridized by a local searcher namely the imperialist competitive algorithm (ICA) and a boundary operator. The Taguchi method in design of experiments tunes parameters of algorithms to improve the performance of the hybrid GA (HGA).

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