Installation vs. echelon stock policies for multilevel inventory control
Management Science
Centralization of Stocks: Retailers Vs. Manufacturer
Management Science
Computers and Industrial Engineering - Supply chain management
A Stochastic Petri Net Approach for Inventory Rationing in Multi-Echelon Supply Chains
Journal of Heuristics
A genetic algorithm for joint replenishment based on the exact inventory cost
Computers and Operations Research
Order acceptance using genetic algorithms
Computers and Operations Research
On the Structure of Lost-Sales Inventory Models
Operations Research
On the Optimal Policy Structure in Serial Inventory Systems with Lost Sales
Operations Research
Cost optimization in the (S-1,S) lost sales inventory model with multiple demand classes
Operations Research Letters
Managing multi-customer service level requirements with a simple rationing policy
Operations Research Letters
Computers and Operations Research
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We consider a static divergent two-stage supply chain with one distributor and many retailers. The unsatisfied demands at the retailers' end are treated as lost sales, whereas the unsatisfied demand is assumed to be backlogged at the distributor. The distributor uses an inventory rationing mechanism to distribute the available on-hand inventory among the retailers, when the sum of demands from the retailers is greater than the on-hand inventory at the distributor. The present study aims at determining the best installation inventory control-policy or order-policy parameters such as the base-stock levels and review periods, and inventory rationing quantities, with the objective of minimizing the total supply chain costs (TSCC) consisting of holding costs, shortage costs and review costs in the supply chain over a finite planning horizon. An exact solution procedure involving a mathematical programming model is developed to determine the optimum TSCC, base-stock levels, review periods and inventory rationing quantities (in the class of periodic review, order-up-to S policy) for the supply chain model under study. On account of the computational complexity involved in optimally solving problems over a large finite time horizon, a genetic algorithm (GA) based heuristic methodology is presented.