Development and application of a cost-driven decision model for store replenishment logistics in the fast-food sector

In this paper, we address the store replenishment problem (SRP) in the fast-food sector, which is concerned with minimising the logistics costs associated with replenishing stores in a network over a fixed time horizon. We develop a cost function that captures transportation, labour, truck availability, and route-time overage costs and compare its performance against traditional, one-dimensional objectives, such as minimising only the travel distances or the fleet size. We formulate the SRP as a mixed-integer program and present a modified nearest-neighbour-based clustering heuristic to pre-generate a set of potential delivery routes. The model concurrently determines the fleet size, the delivery routes, and chooses between single-driver and team routes. Using real-world data from our industry collaborator we show that the proposed heuristic yields solutions with lower costs than the industry baseline. Furthermore, we show that superior results are obtained from an objective with multiple cost components compared to traditional one-dimensional objectives.