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CREW SCHEDULING Past and Future Jacques Desrosiers HEC & GERAD Montréal, Canada.

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Presentation on theme: "CREW SCHEDULING Past and Future Jacques Desrosiers HEC & GERAD Montréal, Canada."— Presentation transcript:

1 CREW SCHEDULING Past and Future Jacques Desrosiers HEC & GERAD Montréal, Canada

2 Voir la présentation Power Point Crew Sched - Complements.ppt pour deux autres transparents

3 The Mathematics behind Vehicle Routing and Crew Scheduling Jacques Desrosiers HEC & GERAD Montréal, Canada Canadian Mathematical Society Montréal, December , 1999

4

5 OUTLINE  Problem Structure  Solution Approaches  Successful Applications  Research Trends  Conclusions

6  PROBLEM STRUCTURE Time-Space Networks Local Schedule Restrictions Task Covering Schedule Composition Non Linear Cost Functions

7 Time-Space Networks

8  SOLUTION APPROACHES Branch & Cut involving... Lagrangean Relaxation Dantzig-Wolfe Decomposition Kelley’s Cutting Plane Method Column Generation

9 Resource Constrained Shortest Path Problem on G=(N,A) P(N, A) :

10 Integer Multi-Commodity Network Flow Structure

11 Crew Scheduling Problems Sub-Problem is Strongly NP- hard Does not possess the Integrality Property Master Problem : Set Partitioning/Covering

12 Branching & Cutting Decisions Branch-and-Cut Decomposition Process applied at all decision nodes

13  SUCCESSFUL APPLICATIONS Vehicle Routing with Time Windows Dial-a-Ride for Physically Disabled Persons Urban Transit Crew Scheduling Multiple Depot Vehicle Scheduling Aircraft Routing Crew Pairing Crew Rostering (Pilots & Flight Attendants) Locomotive and Car Assignment

14 CREW-OPT BUS-OPT ALTITUDE-Pairings ALTITUDE-Rosters ALTITUDE-PBS RAIL-WAYS The GENCOL Optimizer 60 installations around the world … at the Core of Various Software Systems

15  RESEARCH TRENDS Accelerating Techniques Primal - Dual Stabilization Constraint Aggregation Sub-Problem Speed-up Two-level Problems Solved with Benders Decomposition Integer Column Generation with Interior Point Algorithm

16 Acceleration Techniques Column Generator Master Problem Global Formulation Heuristics Re-Optimizers Pre-Processors …to obtain Primal & Dual Solutions

17 Acceleration Techniques... Multiple Columns: selected subset close to expected optimal solution Partial Pricing in case of many Sub-Problems Early & Multiple Branching & Cutting: quickly gets local optima Branching & Cutting: on integer variables !

18 Primal - Dual Stabilization Restricted Dual Perturbed Primal Stabilized Primal

19 Dual SolutionPrimal Solution Primal SolutionDual Solution Approximate Primal & Dual Primal & Dual Solutions Primal - Dual Stabilization...

20 Constraint Aggregation Massive Degeneracy on Set Partitioning Problems A pilot covers consecutive flights on the same aircraft A driver covers consecutive legs on the same bus line Aggregate Identical Constraints on Non-zero Variables

21 Aggregation Algorithm Initial Constraint Aggregation Consider only Compatible Variables Solve Aggregated Master Problem Primal & Aggregated Dual Solutions Dual Variables Split-up Solve Sub-Problem Modify Constraint Aggregation

22 Sub-Problem Speed-up Resource Constrained Shortest Path Labels at each node : cost, time, load, … Resource Projection Adjust A dynamically Generalized Lagrangian Relaxation Results on Sub-Problem cpu time divided by 5 to 10

23 Two-Level Problems Benders Decomposition Algorithm for Simultaneous Assignment of Buses and Drivers Aircraft and Pilots Pairings and Rosters Locomotives and Cars

24 IP(X, Y) for Two-Level Scheduling MIP(X, y) solved using Benders Decomposition Master IP(X) Simplex and B&B(X) Sub-Problem solved by Column Generation MP LP(y) of Set Partitioning SP DP for Constrained Paths B&B(Y) with MIP(X, y) at each node

25 Benders MP Benders SP B & B IP LP DP CG MP CG SP

26 Column Generation with Interior Point Algorithm ACCPM Algorithm (Goffin & Vial) Applications Linear Programming Non-Linear Programming Stochastic Programming Variational Inequalities

27 Integer Column Generation with Interior Point Algorithm Strategic Grant in Geneva –J.-P. Vial et al. Strategic Grant in Montréal –J.-L. Goffin et al. Design of a Commercial Software System

28  CONCLUSIONS Larger Problems to Solve Mixing of Decomposition Methods Strong Exact and Heuristic Algorithms Faster Computers Parallel Implementations Still a lot of work to do !!

29 Some Collaborators Jean-Louis Goffin Pierre Hansen Gilles Savard Marius Solomon François Soumis Jean-Philippe Vial Jean-François Cordeau Michel Denault Guy Desaulniers Daniel Villeneuve


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