چکیده:
Vendor-managed inventory (VMI) system is a mechanism where the supplier creates the purchase orders based on the demand information exchanged by the retailer/customer. In this paper, the performance of the traditional and VMI system is compared by using EOQ model Mathematical modeling is applied and total inventory cost in the supply chain is used as the performance measure. The supply chain is considered in two levels, i.e., buyer and supplier, with the assumption
that the supplier faces n buyers and more products as the contract party Results of proposed model of VMI are clearly better than traditional model. In order to make the model more applicable to real-world production and inventory control problems, we expand this model by assuming a multi-product economic order quantity problem with limited warehouse-space and capital limitation. Under this condition, we formulate the problem as a non-linear integer programming model and
propose a genetic algorithm to solve it. Moreover, design of experiments is employed to calibrate the parameters of the algorithm for different problem sizes. At the end, we present a numerical example to demonstrate the application of the proposed methodology.
خلاصه ماشینی:
"For a single-vendor multi-buyer system, Nachiappan and Jawahar (2007) formulate a model and solve the problem by a heuristic based genetic algorithm.
Some of these meta-heuristic algorithms are simulating annealing (Aarts and Korst, 1989; Taleizadeh et al, 2008a), threshold accepting (Dueck and Scheuer,1990), Tabu search (Joo and Bong, 1996), genetic algorithms (Pasandideh and Niaki,2006; Najafi and Niaki, 2006; Taleizadeh et al, 2008b, 2009a, 2009b, 2009c; Pasandideh et al, 2009) neural networks (Abbasi and Niaki, 2007), ant colony optimization (Dorigo and Stutzle, 2004), fuzzy simulation (Taleizadeh et al,2009a, evolutionary algorithm (Laumanns et al, 2002;Taleizadeh et al, 2009b, and harmony search (Lee and Geem,2004; Geem et al, 2001).
, n , we define the variables and the parameters of the model as follows: ASj supplier setup cost per cycle ABij buyer setup cost per cycle Qij order quantity Rij demand rate hBij holding cost per unit per time unit KBij buyer inventory cost after running VMI system KSj supplier inventory cost after running VMI system T supplier optimum production cycle n number of buyers m number of products q store positive inventory level Sj shortage cost per unit per time unit fj space occupied by each unit F available warehouse space for all products Cj providence cost per unit X maximum capital TCnoVMI total costs of all products in traditional system TCVMI total costs of all products in VMI system 3.
A Hybrid Method of Pareto, TOPSIS and Genetic Algorithm to Optimize Multi-Product Multi-Constraint Inventory Systems with Random Fuzzy Replenishment, Journal of Mathematical and Computer Modelling."