Introduction

1. Name: Hu Botao Nickname: Amber School: Fuzhou No.1 Middle School Research: Informatics, Mathematic Modeling Take part in Olympiad in Informatics Key words:Simulate 模拟 Annealing 退火，降温Logistics 物流 Algorithm 算法 Optimization 优化 Efficient 效率 Introduction

2. The Application of Simulated Annealing for Logistics Amber Amber Laboratory - ADN.cn

3. Index • Actuality of logistics 物流管理的地位与现状 • Mathematic model 一个典型问题的模型 • Design of Simulated Annealing退火算法的设计原理 • Simulated Annealing Algorithm退火算法的实现 • Analyze分析退火算法的应用前景

4. Actuality of logistics Challenge • ChallengeThe problems in logistics usually haveso many conditions that they are very complex to solve.It is important to find a good method by mathematic tools for them. That’s the operations research in logistics.

5. Mathematic model • In logistics, there are many problems of combination optimization. • Traveling Salesman Problem (TSP)It’s a typical and useful problem in them.

6. Mathematic model Description: • A salesman transports the products to all the cities in the map. • The salesman visits each city once and only once, and returns to the starting city. • Find the shortestroute to transport to lower cost. Flash demo

7. Mathematic model • The total distance of the plan (order) P is the function - the sum of all distances in plan. • The aim of TSP is to get the minimum of this function f(P).

8. Design of Simulated Annealing • Simulated Annealing (SA)It’s an algorithm to find the minimum of the function. The function of the problem is discrete and complex. the discrete function of the problem minimum

9. Design of Simulated AnnealingRule of nature • Consider the rule of nature: Substance always closes to the lowest energy state. For example: 1.The river always goes to the lower height. 2.The thing at a high temperature always goes to the lower temperature.

10. Design of Simulated AnnealingCooling Process • Simulated Annealing (SA)It simulates cooling the solid which is a behavior of physical process. At first the solid is at a high temperature, namely has a high energy. With the solid cooling, its energy is getting lower and lower. At last, the solid reaches the lowest energy at the normal temperature. The process of cooling is known as annealing.

11. Design of Simulated AnnealingSupposition lowest energy Similarity? minimum Lowering energy figure Discrete function figure • Notice the lowest energy state has certain similarity to the minimum of the function!!! We can design an algorithm that can find the minimum automatically as substance closing to the lowest energy state.

12. Simulated Annealing Algorithm • I’m Sorry.Because of the time limit, I jump over introducing the detail of SA.

13. Simulated Annealing AlgorithmDemonstration • TSPSA This program is the experiment version written by me, which uses Simulated Annealing algorithm to solve TSP. The left figure of windows shows the current solution in Annealing every 5 times. We can see that the solution is getting better and better.

14. AnalyzeEfficiency • The rate of the value of the solution and the used time is quite higher than the way to get the real minimum in traditional way.When the traditional algorithm runs for years, SA runs in only seconds.“Time is money.” It satisfies the need of time for moderns. • For economy, the management used an efficient algorithm is important for the progress of the company in modern markets. • I think SA satisfies those conditions.

15. Analyze Expandability • SA is not only used in solving TSP. It is the general algorithm for optimization.It can solve all the complex problems forming functions. • SA isn’t only a algorithm, but also a good thought to solve problems.It can be combined with some other algorithm for concrete problems.

16. Creation My creation points: • Creation: simulates the nature to get the minimum of the function创意独特：模仿自然界的物理行为，来求解函数最小值 • Efficiency: The rate of the value of the solution and the used time is quite higher than getting the real minimum in traditional way.高性价比：SA算法比传统算法速度快的多了，解也以1的概率趋于最优解。在解的质量与时间的比上效果良好。传统算法要运行几年的情况，SA只需几秒。

17. Thank you. Amber hupo001(at)gmail(dot)com ADN.cn