A Decision Support System for Controlling Network Flow Using an Iterative Optimization Algorithm. Lianne van Sweeden Tim Bijl Sander Vlot

Transcription

1 A Decision Support System for Controlling Network Flow Using an Iterative Optimization Algorithm Lianne van Sweeden Tim Bijl Sander Vlot

2 Team ORTEC Who are we? Lianne van Sweeden Applied Mathematics Delft University of Technology, NL Tim Bijl Industrial Engineering and Management University of Twente, NL Sander Vlot Systems & Control Delft University of Technology, NL August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 2

3 Today s presentation Outline Challenge Approach Algorithm Results Conclusions August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 3

4 This year s challenge A Decision Support System for Controlling Network Flow Challenge Capacitated transportation network Multiple competing agents (companies) One disrupting agent (government) Case 1: single company Government: disrupt Case 2: multiple companies Government: disrupt and protect Road congestion August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 4

5 This year s challenge Main focus Challenge Goal: create a decision support system for controlling network flow Government perspective Important aspects User-friendly tool Enable scenario analysis (e.g. disruption budget versus impact) Fast algorithm, good solutions Flexibility and scalability August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 5

6 Today s presentation Outline Challenge Approach Algorithm Results Conclusions August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 6

7 Our approach Hierarchical decomposition Approach Min s.t. profit of some agents and losses of other agents Disruption allocation problem disruption budget allocation constraints Construction heuristic Min s.t. congestion prices Congestion pricing problem shared link capacity constraints Construction heuristic Max s.t. individual profit Individual network flow problem network flow constraints Exact solution August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 7

8 Today s presentation Outline Challenge Approach Algorithm Results Conclusions August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 8

9 Algorithm Nested loop Algorithm Start Optimize individual network flows Determine congestion prices Allocate disruption End Inner loop Outer loop August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 9

10 Algorithm Individual network flow Algorithm Start Optimize individual network flows Determine congestion prices Allocate disruption End Inner loop Outer loop Exact mathematical programming approach Aim(m)s to optimize individual profit Subject to network constraints LP model, solved with CPLEX August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 10

11 Algorithm Congestion pricing Algorithm Start Optimize individual network flows Determine congestion prices Allocate disruption End Inner loop Outer loop Iterative heuristic approach Detect congestion; overflow needs redirection Solve reduced capacity model Shadow prices = cost of alternative routes flow l, j capacity l flow l, j current flow l, j overflow(l) August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 11

12 Algorithm Disruption allocation Algorithm Start Optimize individual network flows Determine congestion prices Allocate disruption End Inner loop Outer loop Construction heuristic Goal: maximize disruption impact by reducing arc capacity Based on shadow prices of network flow model LP model, solved with CPLEX August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 12

13 Today s presentation Outline Challenge Approach Algorithm Results Conclusions August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 13

14 Results and demo GUI Results August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 14

15 Results and demo Summary Results Single agent case Running times ~ 0.1 sec Disruption budget β (%) Profit reduction (%) Multiple agent case Running times ~ 4 sec Disruption budget β (%) Profit reduction (%) Agent 1 Agent August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 15

16 Results and demo Additional results Results Effect of increasing disruption budget for single agent case August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 16

17 Today s presentation Outline Challenge Approach Algorithm Results Conclusions August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 17

18 Conclusions Conclusions The challenge Decision Support System for controlling network flow The approach Tri-level optimization problem Hierarchical decomposition: 2 construction heuristics and 1 exact method The results Scalable Decision Support System in AIMMS Competitive running times Good and intuitive results August 26, th AIMMS/MOPTA Optimization Modeling Competition Team ORTEC 18

19 A Decision Support System for Controlling Network Flow Using an Iterative Optimization Algorithm Lianne van Sweeden Tim Bijl Sander Vlot