MarketingJava

Java Python GROUP PROJECT (40%)

A group assignment is part of the course requirement for this module. The project serves as a means to familiarize students with data and information mining beyond the confines of the text and also to assess the students' ability to apply the knowledge and skills learnt. In addition, students will also have the opportunity to work as a team, network and improve their interpersonal skills.

AIMS

This project aims to:

1. Provide students the opportunity to pragmatically apply concepts learnt

2. Develop students’ research, analytical, critical thinking and interpersonal skills

3. Relate and reinforce concepts learnt

GUIDELINES

1. Students are to work in groups (between four to six people).

2. Each group shall conduct research and analysis, interpret and recommend the promotion mix for a new fast-moving consumer good (FMCG).

3. The group will consolidate their theory and findings and prepare a presentation for the selected FMCG.

4. Readings: Students are expected to read ahead of the session plan the required textbook relevant chapters.

5. The poster/presentation may include the following:

· Project title

· Introduction

· Theoretical / principles used

· Description of the promotion mix

· Recommendation and conclusion

6. Students are to submit their Group Project on the session as informed by the lecturer. Any late submission will be awarded zero mark         

单向拓扑结构下异构车辆编队的分布式模型预测控制(Matlab代码实现)内容概要:本文围绕“单向拓扑结构下异构车辆编队的分布式模型预测控制”展开,介绍了基于Matlab的代码实现方法。研究聚焦于多智能体系统中的车辆编队控制问题,针对异构车辆(即动力学特性不同的车辆)在单向通信拓扑下的协同控制挑战,采用分布式模型预测控制(DMPC)策略实现编队稳定性与跟踪性能。该方法通过构建局部优化问题,结合邻居节点的状态信息进行协同决策,有效降低集中式控制的计算负担,并提升系统的可扩展性与鲁棒性。文中还可能涉及避障、通信延迟、动态环境等实际因素的建模与仿真验证。; 适合人群:具备自动控制、车辆工程或机器人相关背景,熟悉Matlab编程,有一定优化算法和多智能体系统基础的研究生及科研人员;尤其适合从事智能交通、自动驾驶编队控制方向的研究者。; 使用场景及目标:① 实现异构车辆在单向通信条件下的稳定编队控制;② 学习并应用分布式模型预测控制(DMPC)解决多智能体协同问题;③ 借助Matlab代码开展算法仿真、性能对比与科研复现工作。; 阅读建议:建议结合控制理论基础知识(如MPC、优化算法、图论)进行深入理解,重点关注系统建模、代价函数设计、约束处理及分布式求解流程。可通过修改车辆参数、拓扑结构或加入噪声干扰等方式进行拓展实验,以加深对算法性能与局限性的认识。
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