Workforce planning for last mile delivery: incorporating multi-period dynamics and postponed deliveries

Adopting mixed workforce of in-house and ad-hoc drivers is prevalent in last mile delivery. This paper studies a tactical level workforce planning problem to determine, in the planning phase, the number of in-house drivers to hire, who will delivery goods together with ad-hoc drivers in the subsequent operational phase. Particularly, we consider multi-period deliveries and allow for postponed deliveries. The objective is to minimize the sum of fixed costs of in-house drivers, variable costs of in-house and ad-hoc drivers, and the penalty costs for postponed deliveries. We develop an integer optimization model and an easy-to-use method to apply the model in data-driven setting. Utilizing data from an online-to-offline retailer in Chongqing as a case study, we conduct numerical tests and sensitivity analysis to evaluate the model's performance, demonstrating its efficacy in reducing costs and improving service level.