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quicksort-fcfs-spt-swpt-edd-lpull

Dispatching Rules 1.First Come First Served 2.Earliest Due Date 3.Shortest Processing Time 4.Shortest Weighted Processing Time 5.Largest Penalty Per Unit Lenght

ranger-deep-learning-optimizer

Ranger - a synergistic optimizer using RAdam (Rectified Adam) and LookAhead in one codebase

rong360

用户贷款风险预测

ryven

Flow-based visual scripting for Python

scala_maven

scheduling_service

The Scheduling API is designed to optimize the route of a trip with multiple stops.

scikit-opt

Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

share

资料共享

siamese-rpn

An implementation for Siamese Region Proposal Network with PyTorch

simplescheduler

Comparison of various Priority Dispatching Rules

simrlfab

Simulation and reinforcement learning framework for production planning and control of complex job shop manufacturing systems

sjackman.github.io

The personal web site of Shaun Jackman

software-collection

This is a collection of useful or interesting softwares.

soundcode

𝄞 How does your code sound? And the idea was born...

spring-boot-examples

about learning Spring Boot via examples. Spring Boot 教程、技术栈示例代码，快速简单上手教程。

spring-boot-in-action

Spring Boot 系列实战合集

spring-cloud-examples

Spring Cloud 学习案例，服务发现、服务治理、链路追踪、服务监控等

springall

循序渐进，学习Spring Boot、Spring Boot & Shiro、Spring Cloud、Spring Security & Spring Security OAuth2，博客Spring系列源码

ssm

SSM框架——详细整合教程（Spring+SpringMVC+MyBatis）

standard-and-chaotic-firefly-algorithm

MATLAB implementations of standard and chaos-incorporated versions of the firefly metaheuristic tested on continuous optimization functions.

statistics

Probability and Statistics course notes in LaTeX + Python

streamingdemo

Spark Streaming + kafka + hbase

tad-iros2019

Code of the Unsupervised Traffic Accident Detection paper in Pytorch.

tdengine

An open-source big data platform designed and optimized for the Internet of Things (IoT).

team-learning

Datawhale组队学习计划与课程内容

transferlearning

Everything about Transfer Learning and Domain Adaptation--迁移学习

vechicle-routing-problem-vrp-with-pickup-and-delivery

Pickup-and-Delivery Problems (PDPs) constitute an important family of routing problems in which goods or passengers have to be transported from different origins to different destinations. These problems are usually defined on a graph in which vertices represent origins or destinations for the different entities (or commodities) to be transported. PDPs can be classified into three main categories according to the type of demand and route structure being considered. In many-to-many (M-M) problems, each commodity may have multiple origins and destinations and any location may be the origin or destination of multiple commodities. These problems arise, for example, in the repositioning of inventory between retail stores or in the management of bicycle or car sharing systems. One-tomany- to-one (1-M-1) problems are characterized by the presence of some commodities to be delivered from a depot to many customers and of other commodities to be collected at the customers and transported back to the depot. These have applications, for example, in the distribution of beverages and the collection of empty cans and bottles. They also arise in forward and reverse logistics systems where, in addition to delivering new products, one must plan the collection of used, defective, or obsolete products. Finally, in one-to-one (1-1) problems, each commodity has a single origin and a single destination between which it must be transported. Typical applications of these problems are less than- truckload transportation and urban courier operations.

vrp-cw

Saving Algorithm, 节约算法，又称C-W算法，是由Clarke和Wright于1964年首次提出的，用来解决VRP问题，是重要的物流算法

websites-and-tools

分类收藏一些有用的网站，工具和软件，以及关于Windows、Mac系统、软件操作和使用技巧等，提高做事效率。不断更新...

wechatbot

详细微信公众号机器人搭建步骤