An exact algorithm for the vehicle routing problem with backhauls
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An Exact Algorithm for the Vehicle Routing Problem with Backhauls. A Thesis Submitted to the Department of I ndustrial Engineering and the Institute of Engineering and Science of Bilkent University in Partial Fulfillment of the Requirements For the Degree of Master of Science by

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An Exact Algorithm for the Vehicle Routing Problem with Backhauls

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An exact algorithm for the vehicle routing problem with backhauls

An Exact Algorithm for the Vehicle Routing Problem with Backhauls

A Thesis

Submitted to the Department of Industrial Engineering

and the Institute of Engineering and Science

of Bilkent University

in Partial Fulfillment of the Requirements

For the Degree of

Master of Science

by

Cumhur Alper GELOĞULLARI

Supervisor

Assoc. Prof. Osman OĞUZ

28.08.2001


Outline

Outline

  • Importance of Routing Problems

  • Problem Statement

  • Literature Review

  • The Algorithm

  • Computational Experiments

  • Conclusion


Motivation

Motivation

  • Logistics:

    “That part of the supply chain process that plans, implements and controls the efficient, effective flow and storage of goods, services, and related information from the point of origin to the point of consumption in order to meet customers’ requirements”

  • Logistics: a means of cost saving

  • Distribution costs constituted 21% of the US GNP in 1983.

  • VRPs play a central role in logistics.


Problem statement

Problem Statement

The basic Vehicle Routing Problem (VRP):

Customers

D


Problem statement1

Problem Statement

The basic Vehicle Routing Problem (VRP):

Minimizetotal distance traveled

subject to

each customer is serviced

each route starts and ends at the depot

capacity restrictions on the vehicles


Problem statement2

Problem Statement

The VRPs exhibit a wide range of real world applications.

  • Dial-a-ride problem

    • House call tours by a doctor

  • Preventive maintenance inspection tours

  • Collection of coins from mail boxes

  • Waste Collection

  • School Bus Routing


  • Problem statement3

    Problem Statement


    Problem statement4

    D

    Linehaul customer

    Backhaul customer

    Problem Statement

    The Vehicle Routing Problem with Backhauls (VRPB):

    • linehaul (delivery) customers

    • backhaul (pick up) customers


    Problem statement5

    Problem Statement

    • The VRP replaces deadhead trip back to the depot with a profitable activity.

      • Yearly savings of $160 millions in USA grocery industry.


    Literature review

    Literature Review

    Related Problems: The TSP and m-TSP

    • Traveling Salesman Problem (TSP)

    • Multiple Traveling Salesman Problem (m-TSP)

      • m-TSP is a special case of the VRP.


    Literature review1

    Literature Review

    Exact Algorithms for the VRPB

    • Vehicles are assumed to be rear-loaded.

    • Two exact algorithms for the VRPB:

      • Toth & Vigo (1997)

      • Mingozi & Giorgi (1999)


    The algorithm

    The Algorithm

    The VRPB under consideration is

    • Asymmetric

    • Linehaul and Backhaul customers can be in any sequence

      in a vehicle route

    • Both homogenous and heterogenous fleet


    The algorithm1

    The Algorithm

    PRELIMINARIES:

    • L : # of linehaul customers

    • B : # of backhaul customers

    • di : demand of (or amount supplied by) customer i

    • m : # of vehicles

    • Qk : capacity of vehicle k

    • cij : distance from customer i to customer j

    • a route is denoted by Rk = {i1=0, i2, i3......., ir=0}

    • q(Rk) = capacity required by route Rk


    The algorithm2

    The Algorithm

    • VRPB = m-TSP subject to capacity constraints

    • m-TSP is a relaxation of the VRPB.

    • A feasible solution to the m-TSP is not necessarily a feasible solution for the VRPB.


    The algorithm3

    The Algorithm

    The Default Algorithm

    • Step 1: Solve the corresponding m-TSP. Let be its solution.

    • Step 2: Check whether is feasible for the VRPB.

    • Step 3: If feasible, stop

      optimal solution for the VRPB is obtained.

      else

      add inequalities valid for the VRPB but violated by

      goto step 1.


    The algorithm4

    The Algorithm

    Solution of the m-TSP

    • Solve m-TSP with branch & bound

    • Bektaş’ s Formulation

      • decision variable xij


    The algorithm5

    The Algorithm

    Feasibility Check

    Computation of q(Rk):

    Consider the route: {0,4,1,2,3,5,0} where


    The algorithm6

    The Algorithm

    Feasibility Check & Cuts

    1) Route Elimination Constraints:

    Qmax : maximum vehicle capacity

    : # of edges in Rk

    If for a route, Rk ,

    q(Rk) >Qmax

    then Rk is infeasible for the VRPB.

    is valid for the VRPB but violates Rk .


    The algorithm7

    The Algorithm

    Feasibility Check & Cuts

    For the previous example: Let Qmax=30

    The route {0,4,1,2,3,5,0} is infeasible for the VRPB, then add

    to the m-TSP formulation.

    Addition of this constraint prohibits the formation of this infeasible route ONLY .

    1

    2

    3

    4

    5

    D


    The algorithm8

    We add

    The Algorithm

    Feasibility Check & Cuts

    2) Multiple Routes Elimination Constraints:

    Consider the example:

    Route Route #q(Rk)QkVehicle #

    {0,1,2,3,4,0} 1 25  30 1

    {0,5,6,0} 2 22  20 2

    {0,7,0} 3 12  15 3


    The algorithm9

    The Algorithm

    Acceleration Procedures

    Local search:

    • Begin with an initial solution and improve it

    • For the TSP:

      a 2-exchange


    The algorithm10

    The Algorithm

    Acceleration Procedures

    iteration 0: cost=200 iteration 5: cost=207

    iteration 1: cost=202 iteration 6: cost=207

    iteration 2: cost=202 iteration 7: cost=208

    iteration 3: cost=205 iteration 8: cost=209

    iteration 4: cost=206 iteration 9: cost=210


    The algorithm11

    The Algorithm

    Acceleration Procedures

    Representation of the set of routes:

    D

    D

    D

    D


    The algorithm12

    The Algorithm

    Acceleration Procedures

    Local Search Operators:

    Swap Operator:

    i

    i

    j

    j


    The algorithm13

    The Algorithm

    Acceleration Procedures

    Local Search Operators:

    Relocate Operator:

    j

    j

    i

    j

    j

    j


    The algorithm14

    The Algorithm

    Acceleration Procedures

    Local Search Operators:

    Crossover Operator:

    i

    i

    D

    D

    D

    D

    j

    j


    Computational experiments

    Computational Experiments

    • C code using CPLEX Callable Library Routines

    • A total of 720 instances are tested.

    • Two sets of AVRPB instances


    Computational experiments1

    Computational Experiments

    • Homogenous Fleet (identical vehicles) (540 instances)

      • Problem size: 10 - 90 with increments of 10

      • For a given problem size, 3 instances for %B=0, %B=20 and %B=50

      • cij~U[0,100]di~U[0,100]

      • Common vehicle capacity:

      • Number of vehicles:

        where   [0,1].

         = 0.25,  = 0.50,  = 0.75 and  = 1.00


    Computational experiments2

    Computational Experiments

    • Observations

      • As  , the problem gets harder to solve

      • For a given value of  , the problem gets easier as %B

      • Acceleration Procedures work well


    Computational experiments3

    Computational Experiments

    • Acceleration Procedures work well


    Computational experiments4

    Computational Experiments

    • Heterogenous Fleet (different vehicles) (180 instances)

      • Q=100 m=4 Q1=125 Q2=113 Q3=87 Q4=75

      •  = 0.25,  = 0.50%B=0, %B=50


    Computational experiments5

    Computational Experiments

    • For Homogenous Fleet:

      • Time to solve the hardest problem took 42 min.

      • Acceleration procedures provide

        • max improvement of 66% in time

        • min improvement of -4.95% in time

    • For Heterogenous Fleet:

      • Time to solve the hardest problem took 33 min.

      • Acceleration procedures provide

        • max improvement of 28% in time

        • min improvement of -10.48% in time


    Conclusion

    Conclusion

    • First Exact Algorithm for the VRPB such that

      • Asymmetric

      • Linehaul and Backhaul customers can be in any sequence

        in a vehicle route

      • Both homogenous and heterogenous fleet

      • The algorithm can be used for both AVRP and AVRPB


    Further research

    Further Research

    • VRPB with time and distance restrictions

    • VRPB with time windows

    • Other local search procedures


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