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Maintaining normal stock amount can reduce ordering cost and improve service level, but excessive stock needs expensive inventory holding costs and occupies too much floating capital, so it is necessary to seek a balance between the stock holdings and inventory cost. Using AnyLogic software, the single point inventory system simulation model is built based on Agent method in this paper. Through comparing the two continuous replenishment strategies, the (R, S) and (Q, R) strategies, the simulation results show that (R, S) strategy is better than (Q, R) strategy. Then the optimal inventory policy to minimize inventory cost with a certain service level is analyzed by the optimization experiment.

Inventory is the term used for the future, temporarily idle resources, which can be used to maintain normal operation, reduce order costs and improve service level while the supply and demand changes constantly. Too much stock needs expensive inventory holding costs and occupies too much floating capital, so inventory control in logistics engineering is more important than other logistics activities and it is necessary to seek a balance between the stock holdings and inventory cost to minimize the total inventory cost with a certain service level or maximize demand service level with an acceptable cost. At present, research works about inventory system simulation are very popular. Many papers focus very much on the impact of information coordination and information-sharing on inventory control in supply chain, the bullwhip effect, and the optimal inventory strategy, etc. There are four major methods to model the complex system: Agent-based Modeling and Simulation, System Dynamics model, Petri Net and Object-Oriented Technology. Paper [1] researched on the information coordination in the activities ofdecision making of production planning and procurement planning in order to fulfill process using the means of multi-agent based modeling and simulation. Papers[2,3] developed a basic model in a production-inventory control system using difference equations to discuss the relationship between stability and bullwhip effect in the supply chain system. Paper[4]studied the integrated stochastic inventory problem for a two-stage supply chain consisting of a single retailer and a single supplier. By using batch shipment policy, the paper obtained the result that the expected total cost can be significantly reduced. Paper[5] investigated the effect of product perishability and retailers’ stockout policy on system total cost, net profit, service level, and average inventory level in a two-echelon inventory–distribution system. In addition, the paper developed an approximate inventory model to the system performance measures. Paper[6] showed how to model a problem to find optimal number of replenishments in the fixed-order quantity system as a basic problem of optimal control of the discrete system. Paper[7] studied the inventory system of an online retailer with compound Poisson demand. The retailer normally replenishes its inventory according to a continuous review (Q, R) policy with a constant lead time. Multi-location inventory models are one of the most widely investigated fields in mathematical inventory theory, but the analytically tractable models suffer from various restrictive assumptions. To overcome these restrictions simulation can be used[8] Paper [9] described a simulation model of a multi-echelon inventory cost optimization problems using Arena and OptQuest to gain the reorder point and order size of the retailers. . The above literature shows that great progress has been made in research on inventory system in supply chain. This paper describes the single point inventory system simulation model using AnyLogic software and adds comparison of two continuous inventory policies based on paper [9].

Based on agent method, this paper describes the single point inventory system simulation model using AnyLogic software. In this model the customers’ demand and lead time can’t be determined ahead. A comparison is made between two continuous replenishment strategies. Simulation experiment verifies the exactness of this model and concludes that (R, S) policy is better than (Q, R) policy. At last, optimization experiments are made to testify the optimize storage policy under certain conditions