Oracle Spatial Summit

Transcription

1 Oracle Spatial Summit 2015 Fast, High Volume, Dynamic Vehicle Routing Framework for E-Commerce and Fleet Management Ugur Demiryurek, PhD. Deputy Director, IMSC University of Southern California

2 Integrated Media Systems Center (IMSC) of USC Fast, High Volume, Dynamic Vehicle Routing Framework OVERVIEW A National Science Foundation (NSF) self-sustained Engineering Research Center (ERC) Fundamental and applied research in Data Science focusing on applications with major societal impact Has spun off more than 10 startups since its inception in 1996 Significant contributions to the field of spatiotemporal algorithms and data management CHALLENGES / OPPORTUNITIES NP Hard (Non-deterministic polynomial-time) problem Large scale network and delivery locations Fast and most-accurate solution Different constraints Integration of dynamic (time-dependent) traffic and cost models to NDM SOLUTIONS Oracle Database 11g Enterprise Edition Spatial Option with Network Data Model Proprietary Time-dependent VR solution based on Sweep and Nearest Neighbor heuristics Local Search RESULTS First online time-dependent VRP prototype that Enables fast and near-optimal delivery schedule optimization Scales with large network and delivery locations Integrates dynamic traffic data Facilitates decision making Performance Saves more than 20% travel cost compared with existing VRP solutions 4 ~ 7 faster than state of the art local search based time-dependent VRP algorithm

3 Agenda Vehicle Routing Problem (VRP) Faster, Scalable and More Accurate Optimal vs Approximate VRP Solutions Approach Sweep Heuristic Local Search NDM Integration Demo

4 Current VR Solutions Slow Requires precomputation between location Not Scale Not support large number of delivery locations Static/Inaccurate Not Integrate traffic information, i.e. time independent

5 Next-generation VR Fast Get route plans in seconds/minutes Large Scale More than hundreds of locations Dynamic/Accurate Utilize real-time and historical traffic data

6 More Accurate: Time-dependent Obtaining high fidelity traffic data is becoming cheap silently collectible ubiquitous

7 More Accurate: Time-dependent Traffic patterns varies based on the time of the day, day of the week and seasons Speed (m/h) Speed (m/h) :00 7:30 9:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 6:00 7:30 9:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 North South Time North South Access to very large traffic sensor dataset in LA to generate patterns! Time

8 More Accurate: Time-dependent t 6 n n 4 t 5 n 4 SP={n 1,n 2,n 4 } t 2 n 3 n n 4 Path={n 1,n 2,n 4 }, Cost=5 Path={n 1,n 3,n 4 }, Cost=6 n 3 n 1 t n 2 n 4 Path={n 1,n 3,n 4 }, Cost=3

9 More Accurate: Time-dependent Time-dependent route planning Recommends different paths for different departure-times

10 Problem Definition :TD-VRP Route Route :00 7:30 9:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 2 Route 2 Route 4 Customer Depot

11 Optimal vs Approximate TD-VRP Solution Exact Solution with Mixed Integer Programming (MIP) NP-Hard problem For 20 stops, there are 20! = 2,432,902,008,176,640,000 (2.4 quintillion > quadrillion> two trillion>billion) For 100 stops, there are 100!=9,332,621,554,000,000,000,000,000,000, 000,000,000,000,000,000,000,000,000,000,000, 000,000,000,000,000,000,000,000,000,000,000, 000,000,000,000,000,000,000,000,000,000,000, 000,000,000,000,000,000,000,000,000,000,000, 000,000,000,000,000,000,000,000,000.. (157 zeros) alternatives for ordering

12 Optimal vs Approximate TD-VRP Solution Running time of TD-VRP optimal solution with N <= 10

13 Approach Heuristic Methods Sweep - cluster first, route later Nearest Neighbor - localize neighbors Clarke & Wright - based on saving heuristic Meta-Heuristic Method Local Search

14 Approach Heuristic Methods Sweep - Cluster first and route later Nearest Neighbor Search Clarke-Wright - Based on saving heuristic Meta-Heuristic Method Local Search

15 Approach - Sweep Heuristic depot

16 Approach - Sweep Heuristic Clustering depot

17 Approach - Sweep Heuristic Routing depot Parallel TSP in each Cluster Efficient

18 Approach - Sweep Heuristic Not accurate particularly for different location distribution

19 Approach - Local Search Solution space search based method Neighborhood Solution Generation Initial Solution Current Solution Accept and continue? Neighborhood Solution

20 Approach Local Search

21 Approach Local Search Only apply operators on a location and its nearest neighbors Most of the effective changes are those applied to a delivery location and its nearby locations. ivery l

22 Approach Local Search Improve efficiency Adaptively select most promising locations Compute shortest path only when it is necessary Towards Fast and Accurate Solutions to Vehicle Routing in a Large-Scale and Dynamic Env., SSTD15

23 Experimental Evaluation Road Network Dataset Los Angeles (LA) network with 304,162 nodes Dynamic Traffic Data sensors on freeways and arterials in LA 1 sensor/reading per minute Collecting and archiving past 4 years Experimental Setup Source, destinations and departure time t s are determined uniformly at random Average results computed from 100 random queries

24 Comparison of Efficiency SSBLS is 4~7 times faster than TDRTR.

25 Comparison of Accuracy* SSBLS does not compromise accuracy.

26 Efficiency and Accuracy # Location = 50 # Location = 500 Running Time Accuracy Gap (%) Running Time Sweep <1s 15% <1s 19% Clarke-Wright <5s 12% <1min 15% Local Search <1min 2% <7min 3% Accuracy Gap (%)

27 Oracle NDM Integration

28 Architecture VRP Visualization Client Tier VRP Demo VRP Algorithms: Code can be integrated into existing JAVA API Algorithms TD Vehicle Routing Algorithm Nearest neighbor heuristics Sweep heuristic Spatial Local Search JAVA API ShortestPath Dijkstra NDM network analysis: Using java API Application Tier NDM Network Analysis Engine LinkCostCalculator TD Graph Model Network data management: Using SQL package Database Back End LA Network Database Schema Metadata Node/Link/Path/ Subpath tables Partition Table Partition Blob Table

29 TD Road Network Integration Create Node/Link/Path tables required by NDM.

30 Fastest Path Computation via NDM Implement LinkCostCalculator interface for time-dependent cost calculation Implement getlinkcost function required by LinkCostCalculator

31 Fastest Path Computation via NDM Implement existing LinkCostCalculator for time-dependent cost TD Shortest Path Calculation via ShortestPathDijkstra API Call shortestpathdijkstra API

32 Output from NDM TD Shortest path calculation Output from NDM

33 Vehicle Routing Demo

34 Acknowledgement

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