Consistent Stochastic Inventory Routing Management (COSIMA)

The project integrates two core logistics decisions in supply chains typically investigated independently or sequentially: inventory management and transportation routing optimization. Although this integration is not new and known as the Inventory Routing Problem in the literature, there is little methodological decision support for real-world problems under uncertainty. We therefore propose to extend the existing knowledge with regard to several important real-world characteristics and develop new approaches for the integration. Stochastic demands at the retailers are typically non-stationary and correlated, emergency shipments are allowed and service level requirements have to be met. Moreover, we incorporate consistency, i.e. delivery at a certain retailer should always take place within the same time interval. This leads to a two-stage multi-echelon inventory problem with routing, stochastic lead times and non-equidistant review periods. Starting from an analysis of both individual domains and including the above real-world problem characteristics, we take the decisions of the respective other domain as given parameters and develop and investigate new anticipatory and iterative schemes for the integration of both domains. For benchmarking purposes, we develop exact solutions using stochastic dynamic programming approaches. For the solution of real-world sized problems, we will develop and in particular integrate heuristic approaches from the inventory and routing domains. Uncertainty will be modeled and represented using scenarios and sampling-based methods. Heuristic solution methods will build on approximate dynamic programming, template-based adaptive large neighborhood search, and branch & regret. The resulting approaches will be validated using artificial problem instances and real-world test cases.

Every year, around 400 million hectoliters of beer are produced and distributed to restaurants, bars, retailers, and event locations in Europe. Although the beer consumption per capita and year in Europe is relatively stable at around 70 liters, the actual demand does not only vary from country to country (in the range between 10 and 145 liters per capita and year), but is also very sensitive to exogenous factors such as weather conditions, weekends, holidays, and sports events, which are often driven by randomness. This randomness affects breweries, as well as all kinds of beverage companies, even though many companies already changed their distribution to some kind of vendor-managed inventory system. Vendor managed inventory means that the supplier decides when and how much to deliver to the customer. The vendor has real-time access to the actual inventory level of the customer. In our case, the brewery has always the information of how many bottles of beer are stored in the stock-rooms of the restaurants. These data can be generated by point-of-sale data of the cash-register. Vendor managed inventory provides more flexibility and control for the decisions that need to be made, such stochastic influences can still cause production and distribution plans to fail. Shortages or excess stock at the customers' on-site storage facilities are the logical consequence. Furthermore, the quality of service observed by customers is strongly depending on the reliability and punctuality of the deliveries, i.e. on different days, a customer has to be serviced by the same driver at roughly the same time to make sure that the driver knows the local conditions and the customer knows when to be present for the delivery. The aim of this project was to develop innovative new solution approaches for the distribution and stocking decisions that need to be made by beverage companies under consideration of a stochastic demand environment. In order to solve the problem, matheuristic solution techniques based on adaptive large neighborhood search were developed. The method showed very good performance in solving problems of real-world size. We determine (close to) optimal vehicle routes and lot sizes for the delivery of beverage products to the customer locations. The importance of this topic arises from the large amounts of beverage products produced, distributed and sold in Europe every year. Improving the distribution and stock levels by exploiting available stochastic information leads to large savings not only in terms of money but also in CO2 emissions caused by the reduction of unplanned extra deliveries. Furthermore, the improved planning also leads to reduced average stock levels and thus to less money tied up in stock.