Scheduling and routing problems at TNT Some solutions and some - - PowerPoint PPT Presentation

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Scheduling and routing problems at TNT Some solutions and some - - PowerPoint PPT Presentation

Jun 26, 2023 27 likes •193 views

Vehicle routing at TNT Algorithm Future research Scheduling and routing problems at TNT Some solutions and some future research directions orensen 1 J. Vandenbergh 1 D. Cattrysse 1 M. Sevaux 2 K. S 1 Centre for Industrial Management, KULeuven

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Vehicle routing at TNT Algorithm Future research

Scheduling and routing problems at TNT

Some solutions and some future research directions

  • K. S¨
  • rensen1
  • J. Vandenbergh1
  • D. Cattrysse1
  • M. Sevaux2

1 Centre for Industrial Management, KULeuven

{kenneth.sorensen, joos.vandenbergh, dirk.cattrysse}@cib.kuleuven.be

2 University of South Brittany

marc.sevaux@univ-ubs.fr

VIP – Oslo

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  • rensen et al.

Scheduling and routing problems at TNT

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Vehicle routing at TNT Algorithm Future research TNT’s express division Routing problem Objective calculation

TNT

TNT’s express division in 2007 Main business: B2B parcel delivery 4.1 million parcels a week > 200 countries > 2,300 depots, hubs and sortation centres > 26,700 road vehicles 47 aircraft > 75,000 staff Revenue of e6.01 billion in 2006 Operating income e580 million

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Scheduling and routing problems at TNT

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Vehicle routing at TNT Algorithm Future research TNT’s express division Routing problem Objective calculation

Vehicle routing at TNT

Pick-up in milk-runs (dynamic!) Unloading/sorting/loading in depot/sortation centre FTL and airplane transport to destination depot Unloading/sorting/loading Delivery in milk-runs

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Scheduling and routing problems at TNT

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Vehicle routing at TNT Algorithm Future research TNT’s express division Routing problem Objective calculation

Problem delineation

One depot Many (thousands) of known customers

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Scheduling and routing problems at TNT

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Vehicle routing at TNT Algorithm Future research TNT’s express division Routing problem Objective calculation

Problem delineation

One depot Many (thousands) of known customers Customers are divided into zones

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Scheduling and routing problems at TNT

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Vehicle routing at TNT Algorithm Future research TNT’s express division Routing problem Objective calculation

Problem delineation

One depot Many (thousands) of known customers Customers are divided into zones Zones have a known “centre”

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Scheduling and routing problems at TNT

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Vehicle routing at TNT Algorithm Future research TNT’s express division Routing problem Objective calculation

Problem delineation

One depot Many (thousands) of known customers Customers are divided into zones Zones have a known “centre” Routing is done on zone level (e.g. 8 zones per truck) Intra-zone optimization is done by the driver

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Vehicle routing at TNT Algorithm Future research TNT’s express division Routing problem Objective calculation

Geographic zones

Geographic delivery region is divided into zones

Historically often based on postal codes

Advantages

Easy sorting Containers correspond to zones Last-minute changes are possible Easier routing

Problem: how to determine zones

Robust Easy to sort Easy to route “Right size”

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Scheduling and routing problems at TNT

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Vehicle routing at TNT Algorithm Future research TNT’s express division Routing problem Objective calculation

Two sub-problems

Routing problem

Minimize transportation cost Minimize distance to baseline solution Route balance

Clustering problem: determine zones

Later!

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Scheduling and routing problems at TNT

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Vehicle routing at TNT Algorithm Future research TNT’s express division Routing problem Objective calculation

Objective calculation (inter-zone routing only)

Transportation cost = total travel time

Travel times between clusters Intra-cluster expected travel and drop-off time (estimated a priori)

Distance to baseline solution

Hamming distance of zone–truck assignments 0 if zone is assigned to the same truck in both solutions 1 if zone is assigned to different truck in both solutions

Route balance

Sum of squared deviations from average travel time

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Vehicle routing at TNT Algorithm Future research Objective integration Operators Structure

“Multi-objective” algorithm

Normalized weights f n

i (x) = fi(x) − f min i

f max

i

− f min

i

Additive objective function f (x) =

  • i

αif n

i (x)

with

  • i

αi = 1

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Vehicle routing at TNT Algorithm Future research Objective integration Operators Structure

Algorithm operators

“Multiple neighbourhood” local search

Swap customers Insert customers 2-opt

Archive of non-dominated solutions

Fixed archive size Minimum Hamming distance to closest solution

Perturbation move

Break up one or more routes and re-route zones customers

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Vehicle routing at TNT Algorithm Future research Objective integration Operators Structure

Algorithm overview

Algorithm 1: “Multi-objective” algorithm for routing at TNT Read baseline solution; Set weights αi; while stopping conditions not satisfied do repeat Local search using neighbourhoods k and weights αi; k ← (k + 1) mod k; until no more improvement ; if solution satisfies archiving conditions then Add solution to archive; Remove dominated solutions from archive; Perturb solution; Update weights αi;

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Vehicle routing at TNT Algorithm Future research

Future research

Get some results

Compare to current way of working

Improve the algorithm

Add more neighbourhoods Add constructive heuristics for good starting solutions Intelligent neighbourhood search (tabu?) Study diversification strategies . . .

Define zones

Robust Easy to route Size restrictions . . .

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Vehicle routing at TNT Algorithm Future research

Future research (cont.)

“Real” multi-objective

  • ptimization method

Implement in commercial vehicle routing software Strategic supply chain evaluation tool

Location of new distribution centres Use of different vehicle types . . .

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Scheduling and routing problems at TNT

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Vehicle routing at TNT Algorithm Future research

Scheduling and routing problems at TNT

Some solutions and some future research directions

  • K. S¨
  • rensen1
  • J. Vandenbergh1
  • D. Cattrysse1
  • M. Sevaux2

1 Centre for Industrial Management, KULeuven

{kenneth.sorensen, joos.vandenbergh, dirk.cattrysse}@cib.kuleuven.be

2 University of South Brittany

marc.sevaux@univ-ubs.fr

VIP – Oslo

S¨

  • rensen et al.

Scheduling and routing problems at TNT