Replenishment And Shipment Policy In The Integrated VMI&TPL System

Considering their limited logistics capacity, many suppliers seek to cooperate with TPL (Third-party Logistics) to implement VMI (Vendor-managed Inventory) program. Based on the above research context, this dissertation focuses on the TPL's replenishment and shipment policy in the integrated VMI&TPL mode, and the research work can be divided into two main parts. Firstly, due to the complexity of the integrated VMI&TPL supply chain, this research investigates the roles of TPL in the integrated VMI&TPL supply chain through simulation, using the relevant theory of System Dynamics. Secondly, assumming that TPL is in charge of the replenishment and shipment operations, this research optimizes thereplenishment and shipment policy under four different demand environments in a two echelon VMI&TPL supply chain. Specially, the main innovative research work and the conclusions are summarized as follows:Firstly, based on the VMI-APIOBPCS model, by expanding the serial supply chain into the distribution supply chain and introducing TPL into the supply chain, this research establishes the integrated "production-inventory-transportation" model in the VMI&TPL supply chain, and then analyses the system performances under the step and stochastic demand environment. The simulation results show that, after introducing TPL, the supplier's production rate becomes smoother, system inventory level becomes lower, and the retailer's service rates become higher.Secondly, in finite time horizon with deterministic demand, the optimal replenishement and shipment policy holds the "Zero-Inventory Ordering" property. And based on this property, this research designs a polynomial algorithm with O(T3) to search out the optimal replenishment and shipment policy. Comparing with the operation cost in the VMI, the numerical results show that the operation cost can be reduced by introduing the TPL, because TPL can consolidate the shipments for the retailers, and such consolidation policy can reduce the inventory level in supply chain. In addition, under the demand with the time windows, when TPL's shipment cost is linear in the shipment quantity, it is easy to arrange the shipment to satisfy demand with different time windows, and then this dissertation designs a polynomial algorithm with O(T3) to optimize the TPL replenishment and shipment policy. When the TPL's shipment cost is a stepwise function, the above problem is NP-hard, and this research considers the special time windows, and it is optimal to satisfy the demand with earlier time window. Then based on this rule, this research also proposes a polynomial algorithm with O(T4) to find the optimal replenishment and shipment policy for TPL.Thirdly, under stochastic demand environment, TPL adopts the based-time and based-quantity policies to consolidate the retailers' orders, in order to minimize the average operation cost. For the two shipment policies, this research designs two effective algorithms to optimize the TPL's replenishment and shipment policy respectively. By comparing the performances of the two shipment policies, the numerical results show that the time-based policy outperform the quantity-based counterpart when the retailer's waiting cost is large.Forthly, under multiple retailers environment, the retailers have different perferrences about the delivery time, and the TPL's optimal shipment policy does not necessary satisfy the "Zero-Inventory Ordering" property. However, the reailers' optimal shipment periods hold Nested Structure. According to this structure, this research proposes an effective algorithm to search out the optimal shipment policies for all retailers in each replenishment cycle. Furthermore, according to WW model and algorithm, this research optimizes the optimal replenishment polcy in the whole plan horizon.In addition, when the number of retailers is large, the above algorithm is much more complex, and then this research proposes two effective heuristics to approximate the TPL's replenishment and shipment policy.