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Wu Bin
Professor、Master's Supervisor
School of Economics & Management
Email
seasimen
aliyun
com
Website
https://jgy.njtech.edu.cn/info/1023/4672.htm
h-index
445
Citations
9
h-index
Calculated based on number of publications stored in Pure and citations from Scopus
2008
2025
Research activity per year
Overview
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Network
Research output
(30)
Similar Profiles
(1)
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Dive into the research topics where Bin Wu is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Weight
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Computer Science
Artificial Bee Colony
50%
Artificial Bee Colony Algorithm
100%
Automated Guided Vehicle
33%
Band Selection
33%
Benchmark Instance
25%
Bin Packing Problem
37%
Candidate Solution
25%
Combined Effect
25%
Computational Experiment
25%
Computing Resource
25%
Contract Model
50%
Customer Satisfaction
50%
Deep Q-Network
33%
Deep Reinforcement Learning
50%
Differential Evolution
50%
Digital Twin
50%
Digital Twin Technology
25%
Disaster Relief
33%
Dynamic Network
50%
Equilibrium Point
25%
Feasible Solution
33%
Feature Selection
50%
Global Optimization
50%
Harmony Search
50%
Historical Information
50%
Historical Knowledge
50%
Hybrid Algorithm
20%
Hyperspectral Image
50%
Iteration Process
25%
Job Shop Scheduling Problem
50%
Local Optimization
50%
Local Search Algorithm
25%
Local Search Method
25%
Memetic Algorithm
50%
Nave Bayes
50%
Non-Dominated Sorting Genetic Algorithm II
50%
Objective Feature
50%
Optimization Problem
50%
Performance Optimization
16%
Reference Point
50%
Resource Limitation
25%
Resource Requirement
25%
Scheduling Problem
50%
Search Experience
16%
Search Mechanism
33%
Search Neighborhood
25%
Supply Chain
50%
Technical Support
50%
Unique Solution
50%
Vehicle Routing
62%
Engineering
Adaptive Inertia Weight
12%
Automatic Machine
25%
Computer Simulation
50%
Construction Planning
10%
Critical Path
25%
Customer Experience
12%
Customer Satisfaction
50%
Decoding Technique
25%
Desalination
50%
Discrete Value
16%
Encoding Method
12%
Engineering
16%
Hydrophilicity
50%
Individual Aspect
16%
Initial Population
33%
Intelligent Transportation
16%
Interrelationship
16%
Iteration Process
25%
Learning System
50%
Local Search
33%
Machine Learning Method
16%
Mean Value
16%
Mode Control
25%
Model Problem
25%
Molecular Level
16%
Optimal Condition
16%
Optimisation Problem
50%
Pareto Frontier
25%
Peak Period
10%
Population Increase
16%
Pressure Drop
16%
Q-Learning
50%
Real Number
12%
Reverse Osmosis Membrane
33%
Routing Problem
100%
Search Neighborhood
25%
Search Space
16%
Search Strategy
66%
Simulation Result
16%
Time Dependent Network
12%
