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An end-to-end predict-then-optimize clustering method for intelligent assignment problems in express systems
Machine Learning · Submitted 18 Feb 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Results show that this kind of one-stage predict-then-optimize method is beneficial to improve the performance of optimization results, namely the clustering results.
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The abstract, sentence by sentence
Express systems play important roles in modern major cities. Couriers serving for the express system pick up packages in certain areas of interest (AOI) during a specific time. However, future pick-up requests vary significantly with time. While the assignment results are generally static without changing with time. Using the historical pick-up request number to conduct AOI assignment (or pick-up request assignment) for couriers is thus unreasonable. Moreover, even we can first predict future pick-up requests and then use the prediction results to conduct the assignments, this kind of two-stage method is also impractical and trivial, and exists some drawbacks, such as the best prediction results might not ensure the best clustering results. To solve these problems, we put forward an intelligent end-to-end predict-then-optimize clustering method to simultaneously predict the future pick-up requests of AOIs and assign AOIs to couriers by clustering. At first, we propose a deep learning-based prediction model to predict order numbers on AOIs. Then a differential constrained K-means clustering method is introduced to cluster AOIs based on the prediction results. We finally propose a one-stage end-to-end predict-then-optimize clustering method to assign AOIs to couriers reasonably, dynamically, and intelligently. Results show that this kind of one-stage predict-then-optimize method is beneficial to improve the performance of optimization results, namely the clustering results. This study can provide critical experiences for predict-and-optimize related tasks and intelligent assignment problems in express systems.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Express systems play important roles in modern major cities. | background 1.00 | 0.03 | 0.01 | asserted 1.00 |
| s1 | Couriers serving for the express system pick up packages in certain areas of interest (AOI) during a specific time. | background 1.00 | 0.05 | 0.01 | asserted 0.99 |
| s2 | However, future pick-up requests vary significantly with time. | background 0.86 | 0.16 | 0.02 | asserted 1.00 |
| s3 | While the assignment results are generally static without changing with time. | background 0.84 | 0.11 | 0.02 | asserted 0.97 |
| s4 | Using the historical pick-up request number to conduct AOI assignment (or pick-up request assignment) for couriers is thus unreasonable. | gap 0.51 | 0.22 | 0.04 | asserted 1.00 |
| s5 | Moreover, even we can first predict future pick-up requests and then use the prediction results to conduct the assignments, this kind of two-stage method is also impractical and trivial, and exists some drawbacks, such as the best prediction results might not ensure the best clustering results. | gap 0.74 | 0.12 | 0.06 | asserted 0.91 |
| s6 | To solve these problems, we put forward an intelligent end-to-end predict-then-optimize clustering method to simultaneously predict the future pick-up requests of AOIs and assign AOIs to couriers by clustering. | contribution 0.98 | 0.91 | 0.21 | asserted 0.98 |
| s7 | At first, we propose a deep learning-based prediction model to predict order numbers on AOIs. | method 0.98 | 0.90 | 0.03 | asserted 1.00 |
| s8 | Then a differential constrained K-means clustering method is introduced to cluster AOIs based on the prediction results. | method 0.99 | 0.80 | 0.04 | asserted 0.99 |
| s9 | We finally propose a one-stage end-to-end predict-then-optimize clustering method to assign AOIs to couriers reasonably, dynamically, and intelligently. | contribution 0.75 | 0.90 | 0.32 | asserted 0.99 |
| s10 | Results show that this kind of one-stage predict-then-optimize method is beneficial to improve the performance of optimization results, namely the clustering results. | result 0.94 | 0.93 | 0.75 | asserted 0.97 |
| s11 | This study can provide critical experiences for predict-and-optimize related tasks and intelligent assignment problems in express systems. | interpretation 0.75 | 0.31 | 0.11 | hedged possibility 0.70 |
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The paper
Original abstract
Express systems play important roles in modern major cities. Couriers serving for the express system pick up packages in certain areas of interest (AOI) during a specific time. However, future pick-up requests vary significantly with time. While the assignment results are generally static without changing with time. Using the historical pick-up request number to conduct AOI assignment (or pick-up request assignment) for couriers is thus unreasonable. Moreover, even we can first predict future pick-up requests and then use the prediction results to conduct the assignments, this kind of two-stage method is also impractical and trivial, and exists some drawbacks, such as the best prediction results might not ensure the best clustering results. To solve these problems, we put forward an intelligent end-to-end predict-then-optimize clustering method to simultaneously predict the future pick-up requests of AOIs and assign AOIs to couriers by clustering. At first, we propose a deep learning-based prediction model to predict order numbers on AOIs. Then a differential constrained K-means clustering method is introduced to cluster AOIs based on the prediction results. We finally propose a one-stage end-to-end predict-then-optimize clustering method to assign AOIs to couriers reasonably, dynamically, and intelligently. Results show that this kind of one-stage predict-then-optimize method is beneficial to improve the performance of optimization results, namely the clustering results. This study can provide critical experiences for predict-and-optimize related tasks and intelligent assignment problems in express systems.
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What it cites · 4 sentences without a confident label
Uses the method or tool of 1 paper
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As for the GCN, we utilize the first-order approximation version proposed by [ref] , as shown in Equation [ref].
Semi-Supervised Classification with Graph Convolutional Networks · (1) GCN-based prediction model · 1.00
Cites as support 1 paper
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First, the criteria by which we train the prediction model differ from the ultimate criteria on which we evaluate the clustering model [ref] .
Task-based End-to-end Model Learning in Stochastic Optimization · Introduction · 0.98
Cites as background 1 paper
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Generally speaking, the prediction part is a machine learning- or deep learning-based model, such as the fully connected layer, the GCN layer, etc [ref] .
Task-based End-to-end Model Learning in Stochastic Optimization · Related works · 0.81
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