Will Yuan's Blog

LeetCode Records

Posted on 2025-04-12 Edited on 2025-04-16 Word count in article: 2.7k Reading time ≈ 10 mins.

[1] Two Sum

Problem

https://leetcode.com/problems/two-sum/description/

Solution

Use enumeration or hash table.

A Common Mathematical Model for Truck Dispatching in Mining Areas

Posted on 2025-04-04 Word count in article: 563 Reading time ≈ 2 mins.

I have recently read some literature on truck scheduling in mining areas which is generally a combinatorial optimization problem. The following is a brief summary of a common scheduling model in the literature.

Parameter Description

Parameter	Description	Unit
$i$	Loading area number, $i=1,⋯,m$	—
$j$	Unloading area number, $j=1,⋯,n$	—
$r$	Truck number, $r=1,⋯,K$	—
$G$	Total Target Mineral Production	$t$
$T$	Length of each work shift	$h$
$K$	Maximum number of trucks available	—
$C$	Rated load capacity of each truck	$t$
$t_l$	Average loading time of each truck	$h$
$t_u$	Average uninstall time of each truck	$h$
$v_f$	Average speed of a fully loaded truck	$km/h$
$v_e$	Average speed of an empty truck	$km/h$
$c_f$	The transportation cost per unit distance when the truck is fully loaded	$RMB/km$
$c_e$	The transportation cost per unit distance when the truck is empty	$RMB/km$
$d_{ij}$	The shortest path distance from loading area $i$ to unloading area $j$	$km$
$f_j$	Requirements of unloading area $j$	$t$
$g_i$	Total amount of minerals in loading area $i$	$t$
$h_j$	The maximum unloading amount of unloading area $j$	$t$
$B_c$	The maximum number of loading times at all loading points within one shift	—
$a_i$	Mineral grade in loading area $i$	%
$e$	Desired mineral grade	%
$\alpha$	Maximum allowed grade error	%
$x_{rij}$	The number of full trips of truck $r$ from the loading area $i$ to the unloading area $j$	—
$y_{rji}$	The number of empty trips of truck $r$ from the unloading area $j$ to the loading area $i$	—

Bug Resolution: ssh: connect to host github.com port 22: Connection refused

Posted on 2025-01-10 Edited on 2025-04-04 Word count in article: 347 Reading time ≈ 1 mins.

Bug Description

Git version: 2.42.0.windows.2
Error: ssh: connect to host github.com port 22: Connection refused

I have been able to update and publish website content normally before, but today when I use hexo d to update my blog, I encountered an error as shown below:

A Brief Summary of Deep Q-Network

Posted on 2024-11-23 Edited on 2024-11-24 Word count in article: 1.1k Reading time ≈ 4 mins.

Deep Q-Network (DQN) is a deep reinforcement learning method, first proposed by Google DeepMind team in 2013 [Paper]. This algorithm introduces a neural network to replace the cumulative expected return storage form in the Q-learning algorithm, and can effectively solve problems with high-dimensional state spaces. The DeepMind team creatively combines convolutional neural networks in deep learning and Q-learning in reinforcement learning to train agents to learn how to control characters in Atari games to achieve good game performance.

Neural Network

The DQN algorithm takes the state of the agent as the input of the neural network, and then uses the neural network to calculate the Q values corresponding to all actions.

Online Network and Target Network

Project Introduction | Path Planning of Robots in Intelligent Warehouse

Posted on 2024-10-24 Edited on 2025-04-04 Word count in article: 661 Reading time ≈ 2 mins.

This project is also part of my master's thesis, and it includes two aspects: global path planning of each robot and collaborative planning of multiple robots. My master's thesis (Written in Chinese) can be downloaded here.

Global Path Planning for Each Robot

Abstract:

To solve the routing problem in Mobile Robot Fulfillment System, firstly, a method based on traditional A* algorithm is proposed, and then a method based on improved A* algorithm considering task priority and utilization frequency of path nodes is further designed. Finally, a Markov decision process describing the problem is established, and a method based on Q-learning is proposed. Results of computational experiments show that these algorithms can quickly solve the problem. The methods based on the traditional A* algorithm and Q-learning can both obtain the shortest paths, and the method based on improved A* algorithm can effectively balance the traffic flow and significantly reduce the number of potential collisions.

Comparison between Traditional A* Algorithm and Improved A* Algorithm

Project Introduction | Task Assignment of Robots in Intelligent Warehouse

Posted on 2024-10-24 Edited on 2024-10-28 Word count in article: 528 Reading time ≈ 2 mins.

More details about this project can be found in this paper. Here is a brief introduction to the project. This project is part of my master's thesis. My master's thesis (Written in Chinese) can be downloaded here.

Abstract:
This paper studies a task scheduling problem in the context of the mobile robot fulfillment system (MRFS), a parts-to-picker storage system where mobile robots bring movable racks to workstations. It determines the assignment of tasks of transporting racks to a fleet of robots with the objective of makespan minimization. A mixed integer programming model is presented to describe the problem. Aimed at quickly finding good solutions to this NP-hard problem, two heuristic rules and an ant colony optimization algorithm are developed. Computational experiments are conducted to evaluate the performance of the proposed heuristic solution procedures. It shows that the ant colony optimization algorithm generally has the best performance.

Mathematical Models

A mixed integer programming model is presented to describe the problem as follows:

Simulation and optimization of hospital registration window based on FlexSim

Posted on 2024-10-24 Edited on 2025-04-04 Word count in article: 1.2k Reading time ≈ 4 mins.

This was a course project during my undergraduate studies and was published in a Chinese journal. The course paper can be downloaded here. The following is a translation of the paper.

Abstract:

The queuing problem in hospital is becoming more and more serious. This paper investigates the queuing system of registration window of a hospital in Nanjing. The data of arrival time interval and service time of each window are collected, and its distributions are fitted by Matalab. The model of the system is set up and simulated with FlexSim. According to the simulation results, the optimization scheme is put forward. By increasing the number of windows opened to patients from 3 to 4, the average waiting time is reduced by 430 seconds, and the queuing time is reduced by 69%.
Key words: hospital；registration window；queueing system；simulation；FlexSim

Introduction

How to Use MongoDB in Python

Posted on 2024-07-19 Word count in article: 1k Reading time ≈ 4 mins.

Install MongoDB Community Server

MongoDB is a NoSQL database product. MongoDB uses collections to organize documents, and each document is composed of key-value pairs.

More information about MongoDB: https://en.wikipedia.org/wiki/MongoDB

Download and install Mongo from here: Download MongoDB Community Server | MongoDB

A Basic Implementation of Rapidly Exploring Random Tree

Posted on 2024-07-08 Edited on 2025-04-04 Word count in article: 212 Reading time ≈ 1 mins.

Introduction

The Rapidly Exploring Random Tree (RRT) is widely used in robot motion planning. The algorithm was developed by Steven M. LaValle and James J. Kuffner Jr.

For more information about RRT, please refer to the following papers:

The pseudocode of basic RRT is as follows: