A Machine Learning Approach to Daily Capacity Planning in E-Commerce Logistics

An Integrated Framework for Dynamic Vehicle Routing Problems with Pick-up and Delivery Time Windows and Shared Fleet Capacity Planning

“…An early study [39] in the field of logistics used multiple forecasting methods (a moving average method, autoregressive (AR) model, and the autoregressive integrated moving average (ARIMA)) to forecast the delivery volumes for different products based on historical shipment data and input constraints. Therefore, in our previous works [9,40], state-of-the-art regression models were employed to predict the daily delivery capacity of a fleet starting their routes from a cross-dock depot and for a specific time slot. In another study, the authors of [41] developed a capacity planning platform to be used in many different service areas and offered to employees, selecting suitable service providers whose earnings are determined through fixed additional fees.…”

“…The raw features that are processed at this stage include the cross-dock, time slot, delivery ID, courier ID, date, district, and delivery address. In the feature extraction process in our previous works [9,40] for the capacity planning stage, we incorporated time-based features (such as the day of the week, month, year, and special days like Black Friday and Christmas). Also, we aggregated historical deliveries from the same cross-dock with the same time slot over the last day, the last 3 days, and the last week.…”

“…For prediction of the total daily delivery capacity regarding cross-docks and time slots, the proposed approach, which is the extended version of our previous work [9], is composed of the steps demonstrated in Figure 1: (1) data preparation for analysis of the raw delivery data and removal of outliers, (2) selection of the raw features to be used in modeling, (3) extraction of time-based and aggregated features for regression, based on the historical data of the cross-dock in all the slots, (4) feature selection, (5) model selection using a number of state-of-the-art algorithms with the best parameters, (6) generation of the capacity prediction model, and (8) updating of the selected model to learn recent data and adapt to unexpected changes on a daily basis. Furthermore, several regression models were investigated to predict the delivery capacity.…”

“…To the best of our knowledge, there is no single solution in the literature to cover all aspects of such a problem. The proposed framework in this paper solves two different routing problems simultaneously and also employs the capacity planning aspect proposed in a previous work with the support of machine learning methods [9].…”