Download presentation

Presentation is loading. Please wait.

Published byJamya Pace Modified over 3 years ago

1
Greedy Random A Novel Algorithm for Vehicle Routing Optimization Dominik R. Rabiej 39 th National Junior Science and Humanities Symposium Orlando, Florida April 28, 2001

2
2 Overview Vehicle Routing Introduction Ten Artificial Intelligence Algorithms Greedy Random: Best-Performing Why is Greedy Random successful? Analysis and Conclusion

3
3 Vehicle Routing CVRTW: Capacitated Vehicle Routing with Time Windows –Customers: Location, Time Windows, Demand –Vehicles: Capacity, Travel on Routes –Central Depot: Start & Finish for Vehicles NP-Hard Problem

4
Key Central Depot Customer Vehicle Route Closed Open

5
5 Algorithm & Engine Inspiration: Human-Guided Simple Search Engine: Problem-Specific Optimization Algorithm –Problem-Independent –Drives Engine –Evaluates Situation, Determines Next Step

6
Set ParametersSelect Algorithm Run AlgorithmRun CVRTW Engine Done? Yes No Pre-computed Solution with Parameters Optimized Solution

7
7 Initial Experiment Ten Distinct Algorithms –Multiple Runs on Standard Benchmarks Which will optimize best? –Best Fewest Vehicles Used Tie-Breaker: Aggregate Distance Why?

8
Key Central Depot Customer Vehicle Route Closed Open Random Circle Random Routes

9
9 Initial Experiment Results

10
10 Greedy Random Significantly better at 95% confidence How and why?

11
2.Greedy Random will move a random customer from route A to B. 1.Initial Solution. 1 B A 3.Moving that customer causes B’s vehicle to arrive late at another customer. 3 B A 4.The CVRTW Engine re-optimizes, improving upon the initial solution. 4 B A 2 B A

12
12 Investigative Experiments Analyze GR’s performance Experiments –Feasible/Infeasible –Variable Priorities –Multiple Initial Random Moves –Steepest Greedy Random

13
13 Feasible/Infeasible Feasibility –All customers receive their complete shipments within their time windows and no vehicle runs out of product Hypothesis –Greedy Random derives its success from use of infeasible space: temporarily invalidated solution space

14
14 Feasible/Infeas. Results

15
15 Experiment Summary Feasible/Infeasible –Infeasibility must be moderate Variable Priorities –High/Medium is better than Low Multiple Initial Random Moves –One initial random move is best Steepest Greedy Random –Search technique independence

16
16 Discussion GR derives success from a single catalytic initial random move Initial random moves that resulted in improvements made significantly less change in infeasibility than those that did not

17
17 Results Comparison

18
18 Conclusion Greedy Random Created and Analyzed Single Catalytic Initial Random Move –Non-drastic, shifts search space Highly Portable –Separation of Algorithm & Engine –Easily applicable to other areas of optimization

19
19 Future Work Other Applications of Greedy Random –Job Shop Scheduling Integration of Neural Networks –Adaptive evaluation of random moves –Increase Greedy Random’s efficiency

20
20 Acknowledgements Dr. Neal Lesh, Mitsubishi Electric Research Laboratory, Cambridge, MA Mitsubishi Electric Research Laboratory 2000 Research Science Institute at the Massachusetts Institute of Technology Dr. Daniel Mihalko, Western Michigan University, Mathematics & Statistics

21
21 Further Information http://dominik.net/research/gr/ Pending Publication in an Artificial Intelligence Journal

Similar presentations

Presentation is loading. Please wait....

OK

ABC Technology Project

ABC Technology Project

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on traumatic brain injury Ppt on construction company profile Ppt on communication skills training Ppt on power grid control through pc Ppt on viruses and bacteria pictures Ppt on mary kay ash Ppt on regional transport officer Ppt on standing order act 1946 ford Free ppt on albert einstein Ppt on ozone depletion in australia