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A branch and price approach for an airport vehicle routing problem

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(1)

A Branch and Price

Approach for an Airport

Vehicle Routing Problem

Michaël Schyns

QuantOM Research Center

(2)

Agenda

• Problem statement

• Goals

• Branch-and-Cut-and-Price for VRPTW

• Impact of the parameters

(3)

Problem Statement

• Project initiated by a main European

freight airport

• Set of planes (TW)

• Set of trucks (C

i

)

• Demand evolves…

(4)

Project goals (work in

progress)

• Model and solve the problem

– (Scheduling or) VRPTW

– Exact method (and/or heuristic: ant colonies)

• "Survey" of the exact methods for the VRPTW

– 30 last years of research: lots of "things"

– Selection of the methods, tricks and parameters:

impact

!

• Improvements?

(5)

Exact methods for the

VRPTW

Main papers

1.Baldacci R., Mingozzi A. and R. Roberti, 2012, Recent exact algorithms for solving the vehicle routing problem under capacity and time windows constraints, European Journal of Operational Research, 218, 1-6.

2.Cordeau JF., Desaulniers G., Desrosiers J., Solomon M. and F. Soumis, 2002, Vehicle Routing Problems with Time Windows, In: Toth P. and D. Vigo (Editors), "The Vehicle Routing Problem", Siam monographs on Discrete Mathematics and Applications, 157-195.

3.Desaulniers G., Lessard F. and A. Hadjar, 2008, Tabu Search, Partial Elementary, and Generalized k-Path Inequalitites for the Vehicle Routing Problem with Time

Windows, "Transportation Science", 42(3),387-404.

4.Desrochers M., Desrosiers J. and M. Solomon, 1992, A new optimization algorithm for the vehicle routing problem with time windows, "Operations Research", 40, March-April, 342-354

5.Desrosiers J. and M.E. Lübbecke, 2005, A primer in column generation, In: Desrosiers, Desaulniers, Solomon (Editors), "Column Generation", Springer, (GERAD, 25th anniversary)

6.Feillet D., 2010, A tutorial on column generation and branch-and-price for vehicle routing problems, "4OR-Q J Oper Res", 8, 407-424

7.Feillet D., Dejax P., Gendreau M. and C. Gueguen, 2004,An Exact Algorithm for the Elementary Shortest Path Problem with Resource Constraints: Application to

some Vehicle Routing Problems, Networks, 44, 216-229

8.Feillet D., Gendreau M. and LM Rousseau, ?after 2007?, New Refinements for the Solution of Vehicle Routing Problems with Branch and Price, Gerad, http://... 9.Gambardella L., Taillard E. and G. Agazzi, 1999, MACS-VRPTW: a multiple ant colony system for Vehicle Routing Problems with time windows, In: Corne D., Dorigo M. and F. Glover (Editors), "New Ideas in Optimization", McGraw-Hill.

10.Gutiérrez-Jarpa G., Desaulniers G., Laporte G. and V. Marianov, 2010, A branch-and-price algorithm for the Vehicle Routing Problem with Deliveries, Selective Pickups and Time Windows", "European Journal of Operational Research", 206, 341-349.

11.Irnich, S. and G. Desaulniers, 2005, Shortest path with resource constraints, In: Desrosiers, Desaulniers, Solomon (Editors), "Column Generation", Springer, (GERAD, 25th anniversary)

12.Jepsen M., Petersen B., Spoorendonk S., D. Pisinger, 2008, Subset-Row Inequalities Applied to the Vehicle-Routing Problem with Time Windows, Operations Research, 56(2), pp497-511.

13.Kallehauge B., Larsen J., Madsen O. and M. Solomon, 2005, Vehicle routing problem with time windows, In: Desrosiers, Desaulniers, Solomon (Editors), "Column Generation", Springer, (GERAD, 25th anniversary)

14.Laporte G., 1992, The Vehicle Routing Problem: An Overview of exact and approximate algorithms, "European Journal of Operational Research, 59, 345-358. 15.Lozano, L., Medaglia, A. L., 2012, An Exact Algorithm for the Elementary Shortest Path Problem with Resource Constraints, Tech. Rep. COPA-2012-2, Universidad de los Andes.

16.Righini G. and M. Salani, 2006, Symmetry helps: Bounded bi-directional dynamic programming for the elementary shortest path problem with resource

constraints, "Discrete Optimization", 3, 255-273.

17.Righini G. and M. Salani, 2008, New Dynamic Programming Algorithms for the Resource Constrained Elementary Shortest Path Problem, "Networks", 51(3), 155-170.

(6)

Exact methods for the

VRPTW

• Two models and approaches:

– Initially: Branch and cut (arc flows model)

– Mainly: Branch and price (route formulation) (one)

Route model

– …

D

3

2

1

(7)

Branch and (Cut and)

Price

Branch & Bound

(Integer

solution)

Node

optimisation

Relaxation

(simplex)

Integer Problem  Branch and Bound

Node: simplex

Each column of the table corresponds to one route

Let's first assume that all (usefull) feasible routes

could be incorporated  Ok

But it is rarely the case… Problem

Good news: we don't need all the routes but just a

subset for the basis

Start with a few routes and construct new

promising ones dynamically (those that would have

a negative reduced cost)

Column generation

B&B+CG Branch & Price

B&B+CG+cuts  Branch and Cut and Prices

CG+cuts  Column and Cuts

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Branch and (Cut and)

Price

Branch & Bound

(Integer

solution)

Column

Generation

Master Problem

(simplex)

Column

Generation

Pricing

(Shortest Path

Problem)

• Var selection: max impact…

• Branching strat: 0, 1 …

• Node selection: dive, broad…

• Branching on "arcs"!!!

Very 1st routes: elementary, heuristics…

1st routes node: all previous ones

Cleaning: remove "unused" routes

Stabilisation of the dual variables?

Model: set partitioning, set covering

Theta in IN

Cuts: k-cycle, SRC, ng-routes…

SRC: #, threshold…

Algo: SPPRC (+k-cycle), ESPPRC, Pulse?

ESPPRC: forward, bidir, DSSR

Pulse: delta, horizon

Heuristics: specific, relax dom…

Stronger resource vector (Feillet)

Label selection: cyclic, cyclic&sorted

1 nb best routes

1 nf first routes

Strengthening Time Windows

Cleaning

• Code optimisation, good practices, hardware improvements,

luck

Strategies, parameters, tricks

Closest one

Closest one

Ants

Ants

Diving

Diving

Frequency, best…

Frequency, best…

Theta in {0,1}

Theta in {0,1}

FP!

FP!

Min cost, short, long…

Min cost, short, long…

1

1

nf first of nb best

nf first of nb best

Time limits

Time limits

Routes selection

Routes selection

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Simulations

• Java: memory 4GB,Eclipse development env.

• IBM ILOG CPLEX 12.5 (simplex Master P)

• Ordinary laptop, windows 7

• Airport instances and Solomon's benchmark

• More than 20 "parameters" with lots of different

possible values

http://www.mschyns.be/demonstration/vrp

– More than 500 simulations up to now

– Tools to compare the results

– Bibliography

(10)

Results and conclusions

• I'm crazy! Work in progress

• Parameters: does-it matter? YES

A slight change in some parameters and no more solution!

 Limited robustness!  Best choices

New question: for hard instances, is it more complex to solve the

problem or to find the right values of the parameters?

• Network topology and data: the airport instances are hard! Large

TW and overlaps, parkings on a same line (symmetry)…

• New trend: improve the lower bound with cuts

• Old abandoned recipes could be reconsidered!

• First results for the airport:

– From 5 to 3 trucks for a classical shift

– No more TW violation

(11)

Thank you

Project:

http://www.mschyns.be/demonstration/vrp

QuantOM:

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