See Webinar Recording at https://resource.alibabacloud.com/webinar/detail.htm?webinarId=13 Gain an introduction to how Big Data and AI is currently used in industry to deliver smart logistics. This webinar covers various practical components including vehicle-cargo matching, route planning, and delivery optimization as well as a case study on China's major food delivery platform Ele.me. The webinar also includes a segment on how data science teams integrate big data contests with their real-world AI applications as well as an introduction to Alibaba Cloud's own Tianchi Big Data Contest. This webinar is ideally suited for IT managers from large enterprises who wish to improve their understanding of Big Data and AI technology, as well as researchers and developers who are interested in solving real-world machine learning challenges. More Webinars: https://resource.alibabacloud.com/webinar/index.htm Tianchi: https://tianchi.aliyun.com/

1. Presenter: Rui Xiong
January 2018
How to Leverage
Big Data to Deliver
Smart Logistics

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What You Will Learn
Background on the Logistics Industry
Future Challenge: Helping Balloons Navigate the Weather
AI Solutions in Logistics & Delivery Optimization
Tianchi Big Data Contest Platform
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Background on the Logistics Industry
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18%
8.7%
8.5%
Industry Background
Logistics Cost / GDP
￥13 Trillion/ USD $2 Trillion
• Transportation: 54.1%
• Inventory Management: 33.2%
• Administration: 12.7%
Reference: Yu, Chunge. "The Analysis of the China National Logistics Costs Structure." Management & Engineering 21: 77.
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5. Intelligent
logistics
Cost
Efficiency
Demand
Delivery
Inventory
Predictable
Controllable
Reachable
Objectives of Intelligent Logistics
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AI Solutions in Logistics & Delivery Optimization
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7. Intelligent
Logistics
Demand
Prediction
Inventory
Management
Delivery
Optimization
• Daily, weekly, monthly prediction
• Different scenarios
• What, when and how much to replenish
• Improve inventory turnover rate
• Vehicle-cargo matching
• Route planning
• Distribution network layout design
• Warehouse coverage of facilities
• Inventory setting for each warehouse
Overall
Supply Chain
Optimization
Overview of Intelligent Logistics
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8. High cost
• large numbers of couriers
Low efficiency
• random dispatch,
backlog of orders
Intelligent dispatch
Route planning
Integrated management
9 million
order processing capability
500 millisecond
response time
29 minute
average delivery time
Hundred of millions
RMB cost reduction
Case Study:
Ele.me
Problem Solution Result
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Vehicle-
Cargo
Matching
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Route
Planning
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Delivery Optimization
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10. Route
Calculation
Vehicle, Driver, Cargo, Traffic, Weather, Time, etc.
User-loss
Analysis
Matching Module
Basic Feature Analysis
Driver
Evaluation
Order
Evaluation
Data
Order
PredictionComplex Feature Analysis
Vehicle-Cargo Matching
Historical
Recommendation Engine
Real Time+
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11. Order Prediction
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under-fitover-fit
• Time Series Model: AR Model
• Machine Learning Model: Linear, Non-Linear Models
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12. Demand Prediction
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Classification
Regression
Model
Rules
Commodities Set
Merge
Commodities With
Abnormal Sales
Commodities With
Normal Sales
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• Root Mean Square Error (RMSE)
decreases by 20-25% compared to
regression
• Robustness and strong generalization
Demand Prediction
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14. Route Planning
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• Shortest Path Problem
a) Dijkstra's Algorithm
b) A* Algorithm
• Vehicle Routing Problem (VRP)
a) Genetic Algorithm
b) Adaptive Large Neighborhood
Search Algorithm
http://home.ku.edu.tr/~daksen/ISOLDE2014-ALNS-for-SPIRP.pdf
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2010
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15. User’s Transportation
Management System
Facility, Order, Vehicle,
Historical Data, etc.
Execution
Data Cleaning
And Mining
Vehicle-routing
Algorithm
User’s Data
External data
RDS
Alibaba Cloud
Relational Database
Service
Route Optimization
Engine
MaxCompute
Alibaba Cloud’s Big data computing
①
②
③
Human Adjustment
Final Delivery Plan
AMAP YUNDI
Delivery Plan
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Route Planning: Framework
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Route Optimization
Gantt Chart for Vehicle
Arrangement
3D Cargo Layout
Manual Adjustment &
Result Confirmation
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Optimized
Route Plan
Route Planning: Core Functions
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TIANCHI Big Data Contest Platform
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https://tianchi.aliyun.com/?_lang=en_US
A global contest platform focused on
big data
A community of data talents:
110,000+ developers
2,800+ academic institutes &
business groups
75 countries and regions worldwide
Provides professional solutions
What is TIANCHI
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Crowd-
sourcing
Data
Sets
Forum
ContestsA world leaderboard
of data talents
Practical solutions from
collective input
Data sets and
computing
resources
A Learn-Explore-Share
one-stop community
What TIANCHI Offers
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DATA MINING
KDD CUP 2017
Highway Tollgates Traffic Flow Prediction
CIKM AnalytiCup 2017
Short-term Quantitative Precipitation Forecasting
OPERATION
RESEARCH
THE MET OFFICE 2017-2018
Future Challenge—Helping Balloons Navigate the Weather
INFORMS 2016
The Last Mile Rush
ARTIFICIAL
INTELLIGENCE
IJCAI 2017
Customer Flow Forecasts on Koubei.com
IJCAI 2016
Brick-and-Mortar Store Recommendation with Budget Constraints
IJCAI 2015
Repeat Buyers Prediction after Sales Promotion
TIANCHI International Contests
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• Estimate the average travel time from
designated intersections to tollgates
• Predict average tollgate traffic volume
Task
• The road network topology in target areas
• Vehicle trajectories
• Historical traffic volume at tollgates
• Weather data
(Nasdaq AMAP and the government)
KDD CUP 2017
Data
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• Estimate the average travel time from
designated intersections to tollgates
• Predict average tollgate traffic volume
• The road network topology in target areas
• Vehicle trajectories
• Historical traffic volume at tollgates
• Weather data
( Nasdaq AMAP and the government)
KDD CUP 2017
Task 1: To estimate the average time from designated intersections to tollgates
Source: http://www.kdd.org/kdd2017/announcements/view/announcing-kdd-cup-2017-highway-tollgates-traffic-flow-prediction
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Source: http://www.kdd.org/kdd2017/files/Task1_1stPlace.pdf
Solution From Team Convolution
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Feature Engineering
• More than 100 features extracted
• Feature selection based on CV
experiments and online feedback
• Multiple feature combinations
used for ensemble, solving
feature conflict

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Source: http://www.kdd.org/kdd2017/files/Task1_1stPlace.pdf
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Tree Based Model
XGBOOST
• Level-wise growth strategy
• Stable
LightGBM
• Leaf-wise growth strategy
• Good algorithm for category features
• Fast
Model Tuning
• Feature selection based on CV experiments and online feedback
• Multiple feature combinations used for ensemble, solving feature
conflict
Sample Weight
• Use 1/label as sample weight, make the little values learn better
Solution From Team Convolution

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Source: http://www.kdd.org/kdd2017/files/Task1_1stPlace.pdf
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One Hot Embedding
• Using embedding layer for basic feature
• Similar locations’ embedding vector is close
Early Interaction vs Later Interaction
• The balance of learning between embedding and statistics
MLP vs RNN
• Multiple Layer Perception: Powerful Expression Ability, Learn
Feature Interaction
• Recurrent Neural Network: Good to model sequence relationships
but unstable for generalization
Solution From Team Convolution

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Source: http://www.kdd.org/kdd2017/files/Task1_1stPlace.pdf
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Model Level Ensemble
• Different ML models, including xgb, lightbm, dnn,
rnn
• Loss function changes, weighted norm-2 and fair
loss to approximate norm-1 loss
• Label transform, such as customized log transform
and sample weight transform
• Model parameter, such as nn structure, gbdt
parameters
Results
• Chose 13 base models based on multiple weighted
cv and leaderboard feedback
• Achived 0.1748 mean absolute percentage error
(MAPE) on leaderboard
Solution From Team Convolution

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• The Olympic
Games in the field of
data mining
• 3582 teams
participated, the most
of all previous KDD Cups
KDD CUP 2017
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Future Challenge—Helping Balloons Navigate the Weather
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• Business demands for delivery route optimization
• Importance of meteorology to air transports
• Future Prospect

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Q & A
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• www.alibabacloud.com
• www.tianchi.aliyun.com/?_lang=en_US
• www.alibabacloud.com/campaign/bigdatacontest
ruixiong.srx@alibaba-inc.com
Further Resources
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