This article formulates the daily flower inventory problem as a stochastic decision model with Poisson‑distributed demand, derives the optimal order quantity analytically, and validates it through Python simulations that compute expected profit for varying order levels.
This article describes the design, hardware and software setup, implementation, testing phases, and performance analysis of a Go‑based backend system that simulates a Spring Festival red‑envelope service capable of handling up to 10 billion requests with peak loads of 60 k QPS per server.
This article explains how to build a Python version of the popular Chinese card game ZhaJinHua, covering deck preparation, player enrollment, card dealing, hand‑type detection, scoring rules, winner determination, and statistical analysis of thousands of simulated rounds.
This article explains the fundamentals of D3.js force-directed graph layouts, covering the underlying physics-inspired algorithms, node initialization, quad-tree collision detection, Barnes-Hut optimization, link forces, and simulation steps, and provides practical code examples and tips for handling common rendering issues.
This curated list groups recent explanatory and simulation model articles—covering clustering analysis, linear regression, queueing theory, Markov chains, and Monte Carlo methods—into easy-to-navigate sections for quick reference, helping students and practitioners locate relevant resources efficiently.
This article models a single-mechanic repair shop as a single-server queue with exponentially distributed arrivals and uniformly distributed service times, then uses Python to simulate one workday and 1,000 workdays, reporting average daily serviced customers and average customer waiting time.
Monte Carlo simulation, a computer-based random sampling technique originating from the Manhattan Project, offers a powerful way to approximate solutions for complex systems with inherent randomness, bypassing unrealistic analytical assumptions by leveraging massive repeated experiments to estimate probabilities and unknown variables.
This article outlines the design and implementation of a minute‑level order‑fulfillment timeliness simulation platform, detailing its background, objectives, challenges, architecture built on Alibaba Cloud DataWorks, core workflow nodes, domain model, ER diagram, JSON task templates, and future extensions for supply‑chain routing.
BMW has launched a fully functional digital twin of its German factory in the metaverse, complete with 120 virtual employees, demonstrating how high‑fidelity simulations using tools like Omniverse, Character Creator, Actorcore, and mobile scanning apps enable risk‑free testing, rapid iteration, and new workflows across industries.
This tutorial demonstrates how to use Python and the Pygame library to create a visual simulation of the solar system, covering required assets, step‑by‑step code for initializing the window, loading planet images, calculating orbital positions, adding background music, and running the main animation loop.
This tutorial explains how to install the gym and highway‑env packages, configure the simulation environment, process state and action representations, implement a DQN network in PyTorch, and train the model while visualizing performance metrics for autonomous driving tasks.
This article walks through creating a Python program that simulates the popular Chinese card game ZhaJinHua, covering deck construction, random dealing to five players, hand‑type evaluation, scoring, and ranking, with complete source code and a concise explanation of each step.
This article explains how linear congruential generators produce pseudo‑random numbers, demonstrates their use with step‑by‑step examples, visualizes the distribution of generated values, and applies the method to design a simple gacha system while discussing its statistical properties and practical limitations.
This article provides a step‑by‑step Python programming tutorial for the traditional Chinese two‑player game "Gan Zi Lao Hu Ji Chong", detailing numeric mappings, conversion functions, round evaluation, and probability simulation to help learners build and test the game logic.
This article explains the classic water‑jug interview problem using two cups of 9 L and 15 L, derives the reachable volumes via greatest‑common‑divisor logic, and provides complete C++ code to determine feasibility and output the pouring steps for any target volume.
This article explains how the author designed and implemented a Go‑based backend that can simulate up to 100 million concurrent users and process billions of red‑packet requests, detailing the hardware setup, performance goals, load‑balancing techniques, monitoring, and the results of multi‑stage stress tests.
This article explains the classic Monty Hall problem, walks through the probability reasoning that switching doors raises the win chance from 1/3 to 2/3, and provides a complete Java program that simulates ten‑hundred‑thousand trials to verify the result.
This article describes how to design, implement, and evaluate a scalable backend that can simulate 10 billion WeChat red‑envelope requests by supporting up to 1 million concurrent users and handling 30 k–60 k QPS per server using Go, Linux tools, and custom monitoring.
This article presents five programming problems from Hulu's 2022 campus recruitment—including particle simulation, Sophie‑N number counting, optimal activity point on a tree, devil‑maze navigation, and non‑intersecting triangles—complete with problem statements, input/output specifications, sample cases, and detailed solution approaches.
In the Tencent Geek Challenge, Wang Haosheng leveraged a custom Tetris simulator and intensive hand‑play—enhanced with limited AI tools—to clear all 10,000 blocks, achieving 1.179 million points and third place, illustrating that manual strategy outperformed the AI despite extensive competition and technical analysis.
This article reviews the selection of a 2D physics engine for mechanics experiments, compares Box2D and Nape, details eight test scenarios highlighting precision, constraint, mass‑density, friction, and buoyancy problems, and proposes practical solutions to improve simulation accuracy and usability.
DiDi’s autonomous‑driving simulation suite combines a high‑fidelity vehicle‑environment simulator, a massively parallel cloud platform, and a robust data management system—leveraging neural‑net caching, mixed‑deployment resources, and virtual‑clock techniques—to replay historic drives, generate extreme virtual scenarios, and efficiently evaluate Level‑4+ algorithms at scale.
The Intelligent UAV Path Planning Simulation System leverages open‑source SITL and FlightGear to model dynamics, visualize 2D/3D environments, and generate exportable flight routes that enable coordinated multi‑drone missions with high precision, addressing traditional UAV planning limitations.
This article outlines a comprehensive framework for designing decision‑type products, covering their evolution stages, core elements of model‑data‑strategy, domain modeling techniques, data‑to‑knowledge transformation, business and process value, and a feedback‑driven decision loop with evaluation and simulation.
This article explains what decision‑type products are, outlines their evolution stages, and details the essential design roadmap—including domain modeling, data value extraction, process standardization, system integration, and building a sustainable decision‑feedback loop—to help product teams create automated, data‑driven solutions that continuously improve business outcomes.
This article explains a promotional bottle‑exchange problem, presents example inputs and outputs, and demonstrates two greedy‑algorithm implementations in Java that compute the maximum number of bottles one can drink when empty bottles can be exchanged for new ones.
This article details the design, implementation, and performance testing of a Go‑based backend that simulates a Spring Festival red‑packet system capable of handling up to 6 × 10⁴ QPS and one million concurrent connections, demonstrating how to achieve 10 billion request processing in a controlled environment.
This article presents a comprehensive technical overview of urban aerial highways for low‑altitude UAV traffic, covering the background, spatiotemporal big‑data foundations, safety‑radius modeling, risk assessment, network and drone models, centralized and distributed control algorithms, simulation platforms, experimental results, and future research directions.
NetEase Yanxuan built a cloud‑native supply‑chain simulation platform that standardizes data flows, achieves 97% forecasting accuracy, processes billions of orders daily, and reduces decision cycles, enabling over thirty major business choices with cost savings of tens of millions.
DARPA’s ACE program showcased the AI‑driven fighter Falco, built with the open‑source AdeptRL reinforcement‑learning framework, which defeated an experienced US Air Force instructor in a 1‑v‑1 simulated F‑16 dogfight, highlighting both the promise and current limitations of autonomous combat systems.
This article explains how to overcome the limitations of PC‑based simulators for in‑vehicle navigation development by using a Thrift‑based proxy (Sandwich) to forward car‑side signals to a PC, enable bidirectional debugging, and project map rendering back to the vehicle display.
SuNing’s AI R&D Center and Logistics Institute created a digital twin platform that maps physical warehouse systems to virtual models, automatically generates simulation scenarios, optimizes strategies, and feeds results back to improve overall storage operations, achieving over 15% efficiency gains.
Simulation enables low‑cost, time‑agnostic testing of e‑commerce supply‑chain strategies—covering procurement, inter‑warehouse allocation, and order dispatch—by combining precedent, experimentation, and modeling, offering a more flexible and comprehensive alternative to AB‑testing for evaluating policies, algorithms, and configurations.
This tutorial demonstrates how to build a vivid fireworks simulation in Python using Pygame, covering module imports, window setup, color configuration, a Fireworks class definition, particle dynamics, and the main game loop for rendering and animation.
By extracting hour‑level activity logs and training supervised models—including CART, GBDT, and neural networks—on user tags, the team simulated short‑term metrics for new reward campaigns, enabling earlier prediction of next‑day retention and shortening experiment cycles despite delayed T+1 data.
This article outlines Pony.ai's comprehensive autonomous driving infrastructure, describing traditional internet back‑end components, additional vehicle‑mounted systems, large‑scale simulation, data challenges, and the reliability, performance, and flexibility practices needed to support rapid growth and safe robotaxi operations.
The article traces the evolution of autonomous driving from early concepts to modern implementations, highlighting Pony.ai’s technical innovations in sensor fusion, high‑definition mapping, simulation, data processing, software iteration, and the challenges of scaling vehicle fleets for commercial deployment.
The article presents a comprehensive overview of Pony.ai's autonomous driving infrastructure, covering the core infrastructure team’s responsibilities, vehicle onboard systems, simulation platform, data architecture, and supporting services, while discussing the technical challenges and engineering practices employed to achieve scalability, reliability, and high performance.
A Python Monte‑Carlo simulation of a daily dice‑betting game shows that even with a 50% win rate per round, 83% of gamblers go bankrupt within eight years, highlighting the randomness of gambling profits and the near‑zero chance of beating the casino.
The article explains Alibaba’s Sigma container‑scheduling system and its Cerebro simulation platform, detailing how they improve resource utilization, reduce costs during large‑scale events like Double‑11, and address challenges such as fragmentation, rapid scaling, image distribution, and accurate workload forecasting.
Meituan’s AI‑driven “super brain” dispatch system combines big‑data‑fueled preparation‑time predictions, operations‑research optimization and reinforcement‑learning‑based routing to solve a dynamic vehicle‑routing problem in seconds, cutting average delivery time from 41 to 28 minutes, lowering per‑order cost over 20 % and reducing power use by 19 %.
A Python simulation of 1,000 gamblers playing daily dice bets shows an 83.3% bankruptcy rate, with half going broke within a year, revealing why even a 50% win chance leads most players to ruin despite occasional high earners.
This article demystifies the common myth that programming requires advanced mathematics, outlining the essential arithmetic, number‑system concepts, and occasional higher‑level topics like geometry, physics, and cryptography, while showing how simple simulations can reveal the impact of small statistical changes.
The author recounts attending a Phoenix Project‑based DevOps simulation in Beijing, describing role misunderstandings, the use of Kanban boards, bottleneck handling, one‑piece flow, and communication challenges, and concludes with recommendations on who should participate in such training.
This article explains the key storage I/O characteristics—such as I/O size, read/write ratio, random versus sequential access, and workload patterns—across typical application types, providing reference values and guidance for storage simulation and performance testing using tools like Iometer.
After attending the Phoenix Project DevOps simulation in Beijing, I reflect on five key lessons—understanding role responsibilities, leveraging visual tools, focusing on value‑driven goals, applying one‑piece flow, and reducing hand‑offs—to improve operational efficiency and foster a DevOps mindset.
This article introduces the Monte Carlo method and demonstrates its versatility through five examples covering π estimation, integral calculation, traffic‑jam simulation, product thickness reliability, and securities market profit forecasting, highlighting its simplicity, power, and broad applicability.
