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Enhanced Parcel Arrival Forecasting for Logistic Hubs: An Ensemble Deep Learning Approach

Xinyue Pan, Yujia Xu, Benoit Montreuil

TL;DR

The paper addresses the volatile problem of short-term parcel arrivals at logistics hubs by proposing an ensemble deep-learning framework that combines historical arrival patterns with real-time parcel status updates. It decomposes the forecast into unordered and ordered parcel components, using an ANN for Type I and a Random Forest for Type II, then fuses them with an ensemble network. Key contributions include (i) dynamic updating every $I$ minutes, (ii) a destination-share forecasting mechanism for unordered parcels, and (iii) a superior Advanced Ensemble that outperforms Holt-Winters, a direct ANN, and a basic ensemble, achieving a MASE of $0.79$ across horizons. The approach demonstrates significant potential for reducing forecast uncertainty and improving resource planning in logistics hubs, with implications for scalability and cross-domain deployment in supply chains and urban logistics.

Abstract

The rapid expansion of online shopping has increased the demand for timely parcel delivery, compelling logistics service providers to enhance the efficiency, agility, and predictability of their hub networks. In order to solve the problem, we propose a novel deep learning-based ensemble framework that leverages historical arrival patterns and real-time parcel status updates to forecast upcoming workloads at logistic hubs. This approach not only facilitates the generation of short-term forecasts, but also improves the accuracy of future hub workload predictions for more strategic planning and resource management. Empirical tests of the algorithm, conducted through a case study of a major city's parcel logistics, demonstrate the ensemble method's superiority over both traditional forecasting techniques and standalone deep learning models. Our findings highlight the significant potential of this method to improve operational efficiency in logistics hubs and advocate for its broader adoption.

Enhanced Parcel Arrival Forecasting for Logistic Hubs: An Ensemble Deep Learning Approach

TL;DR

The paper addresses the volatile problem of short-term parcel arrivals at logistics hubs by proposing an ensemble deep-learning framework that combines historical arrival patterns with real-time parcel status updates. It decomposes the forecast into unordered and ordered parcel components, using an ANN for Type I and a Random Forest for Type II, then fuses them with an ensemble network. Key contributions include (i) dynamic updating every minutes, (ii) a destination-share forecasting mechanism for unordered parcels, and (iii) a superior Advanced Ensemble that outperforms Holt-Winters, a direct ANN, and a basic ensemble, achieving a MASE of across horizons. The approach demonstrates significant potential for reducing forecast uncertainty and improving resource planning in logistics hubs, with implications for scalability and cross-domain deployment in supply chains and urban logistics.

Abstract

The rapid expansion of online shopping has increased the demand for timely parcel delivery, compelling logistics service providers to enhance the efficiency, agility, and predictability of their hub networks. In order to solve the problem, we propose a novel deep learning-based ensemble framework that leverages historical arrival patterns and real-time parcel status updates to forecast upcoming workloads at logistic hubs. This approach not only facilitates the generation of short-term forecasts, but also improves the accuracy of future hub workload predictions for more strategic planning and resource management. Empirical tests of the algorithm, conducted through a case study of a major city's parcel logistics, demonstrate the ensemble method's superiority over both traditional forecasting techniques and standalone deep learning models. Our findings highlight the significant potential of this method to improve operational efficiency in logistics hubs and advocate for its broader adoption.
Paper Structure (21 sections, 18 equations, 7 figures, 2 tables, 2 algorithms)

This paper contains 21 sections, 18 equations, 7 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. Overview
  5. Arrival Volume Forecast - Type I Unordered Parcels
  6. Arrival Volume Prediction - Type II Ordered Parcels
  7. Ensemble Model
  8. Parcel Destination Share Forecast
  9. Experiments
  10. Data
  11. Unordered Parcel Forecasting
  12. Model Inputs
  13. ANN Architecture and Training
  14. Ordered Parcel Forecasting
  15. Methodologies for Comparison
  16. ...and 6 more sections

Figures (7)

  • Figure 1: (a) The plot illustrates the actual parcel arrival volumes for a particular hub, with the same arrival time but at different observation times. The count of the unordered parcels is represented by the blue line. The amount of parcels that were ordered is shown by the red line. Last but not least, the yellow line illustrates the total volume of the parcel. (b) An example of parcel logistic hub network in China. The grey dots represents the access hubs, blue dots are the local hubs and the red one is the gateway hubs. Grey lines are the paths between hubs.
  • Figure 2: Dynamic Updating Model Framework
  • Figure 3: The plot shows the architecture of a feed-forward network with 2 hidden layers
  • Figure 4: The plot shows the architecture of a random forest model, where $x$ represents input instance from the historical data used for forecasting the travel/dwell time. $x_k$ represents $k$-th sample of the data, $l_k$ is the $k$-th decision tree and $l_{RF}(*)$ is the final predicted travel/dwell time.
  • Figure 5: The structure of the ensemble model combining forecasts from unordered and ordered parcel models.
  • ...and 2 more figures