This document introduces a smart logistics project that uses artificial intelligence to optimize logistics operations. It discusses using AI for transportation strategy, warehousing strategy, network strategy, and dispatch control. The goal is to maximize service while minimizing costs under constraints. Example applications discussed include spare parts logistics transport optimization, constraint conditions multi-target optimization, and algorithms that were developed and combined successfully for AI logistics. The project aims to fully optimize supply chain logistics in the future using strong AI and brain-inspired intelligence.

1. —Introduction to the smart logistics project
Shanghai Jiuzhang Intelligent Technology Co., Ltd.
The Institute of Science and Technology for Brain-inspired
Intelligence (ISTBI), Fudan
Transport All Goods Easily

2. 2
Contents
Project introduction
Development schedule2
1

3. 3
Artificial
intelligence
Internet of
things
UnmannedBig data
 Logistics：Real world supply chain management
 AI logistics target levels and constraint levels
 AI logistics digitalization, platform
based,automation
 Project content and innovation points
 Project target exhibit
 Application cases in real world：How AI help
logistics suppliers realise better social targets
Project target：help realise intelligent logistics which
meets real world needs

4. 4
Transportation strategy
Warehousing strategy
Network strategy
Dispatching
control
Forcast
group
Decision
making
planning
Cost
Output
Budgetary
constraints Institutional
constraints
AI
strategy
 Massive variables
 Multi-dimension
 Multi-size
 Dynamic
 Multi-target
 Strong constraint
 Non-formulization
 Non-Structured data
Maximize service & minimize cost under constraint
conditions for logistics optimization

5. 5
AI helps grapple with the challenge of the future logistics
management development
Automation
Optimization
algorithm
Machine
Learning
 Unmanned warehouse
 Unmanned transportation
 Overall industries supply channel integration
 Real time and dynamic
 Non-structured logistics big data mining
 Forecast

6. 6
Project characteristics and innovation points
Full supply chain
（B2B+B2C/road，rail，waterway）
Dynamic and real time
Non structural data graphical
representation
Brain-inspired intelligence new
algorithm new model experiment
platform
Based on current situation,
Look for future
(Artificial + intelligence )
Artificial intelligence 
Enhanced artificial intelligence
Optimization/enhanced learning
as the core

7. 7
Classic case：Some large-scale automobile logistics
company project outline
• Production line
material
distribution
• After-sales
logistics
• Warehousing
planning
• Parts logistics
Warehousing Capacity
RouteScheduling
Data
Optimiz
ation
Study
Forecast

8. 8
Classic case：Spare parts logistics transport optimization
learning algorithm
 Spare parts logistics transportation optimization needs overall
scheme :Transportation route optimization,loading optimization,and queuing
optimization.
 Overall optimization algorithm supports: 100,000 orders per day,with mileage
of 10 million KM.

9. 9
Classic case：constraint conditions multi-target
combinational optimization
distrib
ution
distrib
ution
distrib
ution
Loading
diagram
Route
diagram
Loadi
ng
Queuing（Flow
control）
Unloa
ding

10. 10
Classic case ：difficulties
1. Large number of variables
2. More restrictions
3. Nonlinear , unformulated
4. Large solution space : >1062
algorithm
1. Accuracy
2. Adaptive, learnable strategy updates
3. Intelligent algorithm and business
adaptation coordination
Business
13 Loading constraint
8 Transfer library constraint
12 Loading constraint
8 Resource constraint
8 Time window constraint
14 Multiple rounds of transport constraints
constraints

11. Innovation points：Solutions to optimization
algorithm + learning algorithm
Intelligent Logistics Simulation
Module
Standardiz
ation
interface
Overall intelligent optimization
search algorithm
Analog
backout
Inheritance
algorithm
Taboo
search
Parallel
interfac
e
Machine learning
module
SVM Reinforceme
nt learning
1. W. Lu et al Intelligent Solution System towards
Parts Logistics Optimization ERUO2018 (Abstract,
Contributed Talk)
2. Y. Huang, B. Chen, W. Lu et al. Intelligent Solution
System towards Parts Logistics Optimization
WCGO2019 (Regular Paper Accepted)

12. 12
Classic case: algorithms combination of sucessfully
researched and developed AI logistics
① SVM——Disassemble order strategy
② Greedy algorithm——Orders preprocessing
③ Community clustering——Initialization
④ Parallel analog backout algorithm
⑤ Self-adaption backout algorithm
⑥ Pruning searching algorithm with priority queue——Route
planning
⑦ Dynamic planning——Loading + route planning
⑧ Intensive learning,Q-Learning——Loading + route planning
⑨ Tree search algorithm——Post-optimization+route
combination
Generality
algorithm
development
Professionality
algorithm
research and
development

13. Classic case:the result better than some world
famous logistic management software
Result of a famous
business software
Our result
Load mileage 42235.5 39857.8
total mileage 84006.4
Dynamic loading rate 55% 52%
Number of vehicles 118 155
Number of cars 315
Running time 30分钟 10分钟
number of orders 2959 2959
The system under
algorithm has been at
online operation.

14. Classic case:Business oriented exact solution

15. 15
Contents
Project Introduction
Development plan2
1

16. 16
Business mode：Through technology innovation
cooperation,realise economic and social benefits
APP
Platform service
Vehicle & goods
match
Transport
management
AaaS
Algorithm:providing
customized algorithm
for logistics
enterprises
Algorithm
Scheduling
algorithm,route
optimization
Site selection
service ,Loading service
Economic benefits ：
Reduce carbon emission
Reduce source consumption
Social benefits ：
Realize cost-reducing & increase
benefits in whole society
Increase economic operation efficiency
Independent innovation：
Basic algorithm theory innovation
Basic software indepentent innovation
SaaS
Algorithm:providing
cloud/personalization
service mode

17. 17
1、Develop a platform for optimization of
vehicle and goods matching：Goods which
can walk automaticly
• Freight transportation
platform like Didi or Uber
• Intelligently matched order
• Intelligently arranged route
and vehicle dispatch
• Route optimization
• Increase the income of the
freight carrier
• Reduce no-load rate
• Reduce carbon emission
• Reduce transactional cost
of vehicle and goods
matching
• Reduce loss and resource
waste

18. 2、Develop SaaS System: Flexiblely embed existing
logistics management system
AaaS : jiuzhang VRP
Input：
Information of
vehicles
Orders info
Distance matrix
Output：
Scheduling
Optimized routine

19. 3、AaaS（Algorithms as a Service）
Service of consultation plus algorithm provided
according to the clients’ business pain points.
Rrequirement survey
Interview research
Data survey
Establish a plan
Modeling
Adjust algorithm
Conduct tests
Select Range
Test summarisation
Promotion plan
Training and extension
Summarize and improve
starting

20. 20
Future development and expectation
算法开发
场景模拟
应用验证
“Strong” Artificial
intelligence and brain-
inspired intelligence
Business→Mathe
matics→ Business
 Unmanned equipments
 Transport method
integration and
optimization（highway、
railway、air transport、
waterway transport）
 B2B and B2C integration
 Coordination trans legal
person and industries
（Connect data,platform
and equipments. ）

21. 21
Future overall optimization of logistic supply chain,
supporting the full load of a vehicle, express
transportation、spare parts, delivery,etc.
Solution Description
Route planning
Route optimization for multi-vehicle,multi-goods and multi-sites.Include two categories：1，Without
alternative loading and unloading.
2，With alternative loading and unloading（will load halfway）
Load planning
How to load the goods into the vehicle. 1，With the highest space use rate. 2，In the order of loading
and unloading.
Include：1，Loading schedule with the order of loading and unloading. 2， Loading schedule without the
order of loading and unloading.
3，Consider the center of gravity for the boxes transported in UAV.
Node location
Choose intermediate supply point（or the front warehouse）.
In order that, 1，New network under logistic load is the most economical.（distance, cost)
Entry and exit queuing management
According to the vehicle entry time,unloading time,and with the help of the electronic fence,arrange the
order and time of vehicle entry and exit. Reduce the traffice jam at the gate of factories.
Three dimensional volume
calculation
With photo or surround video taking, 1，calculate the standard cuboidal object’s 3-dimensional volume. 2，
Determine the irregular objects’ outsourcing line.
Parts entering the factory
According to the spare parts’ location,route schedule and manufacturing plan,decide the transportation
plan and dynamic scheduling of the spare parts to entering the factory.
Supply and demand forecast
Forecast the future sales or some short time supply and demand according the current sales data, to help
arrange transport capacity beforehand.
Intelligent disassemling orders Dynamicly and intelligently deliver the orders.
Loading optimization Optimization for loading and unloading,including the optimizaton of alternative loading and unloading.
Dynamic scheduling Dynamicly ajust transport schedule according to the real transport state
Storage optimization Supply optimal warehousing level according to sales and supplying ability of the supplying chain.
AI platform AI calculation platform

22. 22
Medium-term planning
function
Plan scheduling
function
Order
combination
planning
Row
scheduling
design
Predictive
warning function
Data
prediction
Abnormal
warning
Short-term planning
function
Scheduling
Daily
scheduli
ng plan
Emergency
intelligent
scheduling
Load/Unload
line
Loading
and
unloadin
g
Route
planning
Time
flow
control
Long-term planning function
Long-term site
planning
location
strategy
Supply
chain
strategy
Wareho
using
Configuration
transformation plan
Network
configura
tion
Short
barge
admissi
on
Convec
tion
cycle
Overall customization
Module
combination
System
customization
Further help finish Supply chain algorithm solution for the
manufacturing industry, retail industry, etc.

23. 物件
装卸
销售
线路
规划原材料
排程
调度
仓储
协同
决策
演化
销售
预测预测
场景
识别
识别
Optimization
decision algorithm
prototype model
library
Deep learning
prototype model
library
Meta learning super heuristic algorithm
framework
• Mathematical model
of the problem
• Data and annotate
• Initialisation
• Evaluation system
• Others
static
customizedalgorithm
Intelligent learning and decision customized algorithm development
platform
Algorithm framework/Basic
function and class
Algorithm
engineer
Selfadaptedcustomizedalgorithm
Seek the
solution
Modeling

24. 24
 Contact：
– Xinjie Zhang ：
 E-mail：zhangxinjie@hotmail.com
 Tel：13311931669
 http://www.jzlog.cn:8000/
– https://istbi.fudan.edu.cn/lnen/
Thanks！