Transportation resource scheduling in food retail industry

Abstract

The objective of this thesis is to find an appropriate analytical method for scheduling the daily driver tasks in the grocery industry. The goal is to maximize driver utilization. A "Bin-packing" approach is employed to solve the problem. A Bin-packing problem concerns packing a list of items into the minimal number of unit capacity bins. In our problem, the drivers correspond to the bins and the daily delivery tasks are equivalent to the items, where we use time units to measure bin capacity. The model is applied to characterize the operation of a grocery company. Several bin-packing algorithms are implemented on two weeks of delivery data, which represent the company's transportation demand. The driver requirements are calculated and compared with their actual assets. Driver requirements are assessed on a per-day basis, considering the volatility in transportation demand over the course of the week. The performance of a given bin-packing algorithm is measured by how well it maximizes driver utilization and balances the workload among the drivers. The model's solution generated labor savings and proved that better resource allocation is possible by considering the demands of the various dispatching locations and the days of the week. Extension of the current model to capture the time window constraints of the delivery locations is proposed for future further research.