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BUS AND DRIVER SCHEDULING IN URBAN MASS TRANSIT SYSTEMS Guy Desaulniers GERAD Research Center Ecole Polytechnique Montréal Travel and Transportation Workshop Institute for Mathematics and its Applications Minneapolis, November 13, 2002

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OVERVIEW Introduction Bus scheduling Driver duty scheduling Simultaneous bus and driver scheduling

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URBAN BUS TRANSPORTATION Provides: Interesting, complex and challenging problems for Operations Research Because: Large savings can be realized A large number of resources is involved

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LARGE NUMBERS Nb of drivers 2-3 x nb of buses Nb of daily trips 10-20 x nb of buses Nb Lines Nb Buses Nb Depots Twin Cities1329405 Montréal20615007 Paris246386023 NYC298486018

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OPERATIONS PLANNING PROCESS Frequencies Timetables Bus schedules Driver schedules (Duties + Rosters) Lines

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GOAL OF THIS TALK Review latest approaches based on mathematical programming for Bus scheduling Duty scheduling Simultaneous bus and duty scheduling Where do we stand with these approaches ?

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BUS SCHEDULING PROBLEM DEFINITION One-day horizon Several depots Different locations Different bus types (standard, low floor, reserve lane in opposite direction, …)

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PROBLEM DEFINITION (CONTD) Time Line 2CE Line 1AB trip 7:00 7:40

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PROBLEM DEFINITION (CONTD) Time Line 1AB DepotsLine 2CE Deadheads

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PROBLEM DEFINITION (CONTD) Time Line 1AB DepotsLine 2CE

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PROBLEM DEFINITION (CONTD) Constraints Cover all trips Feasible bus routes Schedule Starts and ends at the same depot Bus availability per depot Depot-trip compatibility Deadhead restrictions

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PROBLEM DEFINITION (CONTD) Objectives: Minimize the number of buses Minimize deadhead costs Proportional to travel distance or time Fuel, maintenance, driver wages No trip costs

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NETWORK STRUCTURE 1 2 3

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SOLUTION METHODOLOGIES Multi-commodity + column generation Set partitioning + branch-and-price-and-cut

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A. Löbel (1998) Vehicle scheduling in public transit and lagrangean pricing Management Science 44

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MULTI-COMMODITY MODEL

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COLUMN GENERATION On the multi-commodity formulation Two pricing strategies Lagrangean pricing Standard LP solution is often integer If not, rounding procedure

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LAGRANGEAN PRICING For fixed dual variables, solve Lagrangean relaxation 1 Relax trip covering constraints Obtain a minimum cost flow problem Lagrangean relaxation 2 Relax flow conservation and depot capacity constraints Add Obtain a simple problem that can be solved by inspection

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RESULTS Real-world instances Depots TripsDepots/trip CPU (hrs.) 4 3,413 1.7 0.7 9 2,424 4.9 12 12 8,563 2.2 14 49 24,906 1.6 10* * Not to optimality

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A. Hadjar, O. Marcotte, F. Soumis (2001) A branch-and-cut approach for the multiple depot vehicle scheduling problem Les Cahiers du GERAD, G-2001-25

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SET PARTITIONING MODEL

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BRANCH-AND-PRICE-AND-CUT Arc elimination throughout the search tree Heuristic feasible initial solution Reduced cost test 1 2 3 0.5 Odd cycle cuts (facets)

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RESULTS Randomly generated instances Depots Trips Depots/tripCPU (hrs.) 290022.5 450043 6 65

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VARIANTS Fueling constraints Departure time windows Desaulniers, Lavigne, and Soumis (1998) Bianco, Mingozzi, Ricciardelli (1995) Departure time windows and waiting costs Desaulniers, Lavigne, and Soumis (1998)

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DUTY SCHEDULING PROBLEM DEFINITION One-day horizon One depot Bus blocks are known Bus deadheads are known

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PROBLEM DEFINITION (CONTD) ABBACE One bus block Trips Deadheads Relief point: location where a change of driver can occur Relief points: all termini additional locations

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PROBLEM DEFINITION (CONTD) ABBACE One bus block Task: indivisible portion of work between two consecutive relief points along a bus block Trip tasks and deadhead tasks Tasks

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PROBLEM DEFINITION (CONTD) ABBACE FGGFHBBH Duty: Sequence of tasks assigned to a driver Piece of work: Subsequence of tasks on the same block Pieces of work Break Duty type: Depends on work regulations

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PROBLEM DEFINITION (CONTD) Constraints Cover all tasks (continuous attendance) Feasible duties Schedule Work regulations for each duty type –Number of pieces –Min and max piece duration –Min and max break duration –Min and max working time –Valid start time interval Min number of duties of each type

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PROBLEM DEFINITION (CONTD) Objectives Minimize the number of duties Minimize total wages

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SET PARTITIONING MODEL

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NETWORK STRUCTURE Block 1 Block 2 Block 3 Deadhead task Trip task Resource constraints are used to model working rules Walking

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SOLUTION METHODOLOGY Heuristic branch-and-price

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R. Borndörfer, M. Grötschel, A. Löbel (2001) Scheduling duties by adaptive column generation ZIB-Report 01-02 Konrad-Zuse-Zentrum für Informationstchnik, Berlin

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HEURISTIC BRANCH-AND-PRICE Master problem solved by Lagrangean relaxation Constrained shortest path subproblem solved by Backward depth-first search enumeration algorithm Lagrangean lower bounds are used to eliminate possibilities

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HEURISTIC BRANCH-AND-PRICE Depth-first branch-and-bound At each node, 20 candidate columns are selected Probing is performed for each candidate One variable is fixed at each node No new columns are generated if the decision made does not deteriorate too much the objective function value

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RESULTS 1065 tasks 3 duty types 1h20 of CPU time Reduction in number of duties from 73 to 63

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SIMULTANEOUS BUS AND DUTY SCHEDULING – PROBLEM DEFINITION One-day horizon One depot Bus blocks are unknown Bus deadheads are unknown

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PROBLEM DEFINITION (CONTD) Find Feasible bus blocks Feasible duties Such that Each trip is covered by a bus Each trip task is covered by a driver Each selected deadhead task is covered by a driver

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BUS AND DUTY SCHEDULING NETWORK STRUCTURE Potential deadhead task Trip task Walking

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SOLUTION METHODOLOGIES Mixed set partitioning / flow model + column generation / heuristic Set partitioning + branch-and-price

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R. Freling, D. Huisman, A.P.M. Wagelmans (2000) Models and algorithms for integration of vehicle and crew scheduling Econometric Institute Report EI2000-10/A Erasmus University, Rotterdam

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MIXED SET PARTITIONING / FLOW MODEL

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COLUMN GENERATION / HEURISTIC LP relaxation is solved by column generation Master problem is solved by Lagrangean relaxation Relax all driver-related constraints Obtain a single-depot bus scheduling problem Pricing problem is solved in two phases Solve an all-pairs shortest path problem to generate pieces of work Combine these pieces to form negative reduced cost feasible duties

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COLUMN GENERATION / HEURISTIC Once the LP relaxation is solved Fix the bus blocks as computed in the last master problem Solve a duty scheduling problem by column generation

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RESULTS Real-world instances TripsTrip tasks Gap (%) CPU (hrs.) 113 0.00.33 148 2.91.5 238 3.667.8

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K. Haase, G. Desaulniers, J. Desrosiers (2001) Simultaneous vehicle and crew scheduling in urban mass transit systems Transportation Science 35

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SET PARTITIONING MODEL

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BUS AND DUTY SCHEDULING NETWORK STRUCTURE Potential deadhead task Trip task Walking

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EXACT BRANCH-AND-PRICE Bus constraints are generated dynamically Multiple subproblems One per duty type and possible start duty time Partial pricing Exact branching strategies

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HEURISTIC BRANCH-AND-PRICE Early LP termination Depth-first search without backtracking Multiple branching decisions on columns

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EXACT RESULTS Real-world instances Minimize number of duties TripsTrip tasks Gap (%) CPU (min.) 851707.10.7 14328605.9 177354043.1 2044080192.6 2625240354.7

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HEURISTIC RESULTS Real-world instances Minimize number of duties TripsTrip tasks Gap (%) CPU (min.) 1773544.15.3 20440808.2 262524036.1 3787562.070.3 4639260.2199.2

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VARIANTS Parkings Multiple depots Desaulniers (2001), TRISTAN Huisman (2002), IFORS

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FUTURE RESEARCH ON SIMULTANEOUS BUS AND DUTY SCHEDULING Target duty working time (in progress) Reducing solution times For master problem Dual variable stabilization For constrained shortest path subproblems Generate pieces of work instead of duties Multiple depots (started) Integrate timetabling aspects

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CONCLUSION Bus scheduling Optimal or close-to-optimal solutions for large to very large instances Duty scheduling Good solutions for medium to large instances Simultaneous bus and duty scheduling Optimal or close-to-optimal solutions for small to medium instances

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GILBERTS MEASURE Accuracy: very high Speed:low to medium Simplicity:very low Flexibility:medium to high

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THANK YOU !

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