In last mile delivery, to manage delivery default risks and ensure delivery completion, reserve personenel are placed. This is due to driver procurement having to be planned and executed about one-month ahead, when delivery demands could only be roughly predicted. Although reserve drivers occasionally work as final defense, it regularly lowers driver utility, and a method to place reserve drivers balancing delivery default risk and driver utility is required. Previous work tackled this problem

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... y stochastic staffing problem approaches, but there existed a limit in feature modelling and result interpretability, which created a gap in algorithms and procurement manager decision making. The proposed method aims to fill this gap, by taking a transdisciplinary approach of traditional scheduling, probability modelling, and explainable AI. In doing so, a flow of first creating a staffing schedule based on fixed staffing number demands, and then determining a fixed number of reserve personnel required for each staffing window, was designed. A probablity distribution of required personnel number per delivery is calculated in doing so, and this distribution is used as a easy to understand decision support tool for delivery managers. Through a case study using delivery demand data of a Japanese EC-logistics company, the proposed method was shown capable of lowering reserve drivers, with having a high potential of no delivery defaults.