THE INVENTORY ROUTING PROBLEM WITH THIRD PARTY LOGISTICS

Abstract

There are two key planning issues in supply chain: inventory management and transportation. In this research, the inventory control and transportation of syrup concentrate and final products for one bottling company working for a beverage company is studied. Operation of most of beverage companies is based on a franchised distribution system. In this operation, syrup concentrate is produced by a beverage company and sold to bottlers. Bottlers, in turn, mix the syrup concentrate with different ingredients to produce various products and distribute them to retailers.

Unsatisfied orders have several harmful effects on the bottling company. The bottling company may not satisfy all demands due to its small fleet size, which is not able to cover all deliveries in the right timeframe. One method for preventing missed orders is sending orders to some retailers in advance to hold for future use. This allows the fleet to be free to service the rest of the retailers. This policy is possible if those retailers have available capacity to keep products. Another way to deal with this problem is by renting vehicles, which increases the fleet size. The last option for delivering to a retailer when the owned fleet is not able to do so is outsourcing shipping and/or warehousing. The bottling company contracts with a Third Party Logistics Provider (TPLP), who is responsible for delivery of final products to some of the bottler's retailers. Also, TPLPs can store commodities in their warehouses and deliver products to retailers at the right time if there is no available capacity in the bottler's warehouses.

This problem belongs to Inventory Routing Problem (IRP) with some new features such as options for rental vehicle and TPLPs. IRP is a well-studied problem in Operation Research but most of the studies take a single period into account. In contrast, the proposed model in this study includes several time steps in which a decision in one time step can affect future time steps. The proposed model is a multi-tier, multi-plant, multi-warehouse, and multi-product model which considers non-homogeneous fleet. No model in the literature considers all of these characteristics simultaneously.

In this research heuristic methods are developed to solve large problems for which optimization packages cannot find even a feasible solution. Two heuristic methods are proposed for this problem, which are based on fix-and-run algorithm. Three improvement phases are also developed to enhance the final solution of heuristics. The proposed heuristic methods in this research can find an appropriate feasible solution with only a small gap from an upper bound and in reasonable running time.