Scheduling and routing problems at TNT

Transcription

1 Some solutions and some future research directions K. Sörensen 1 J. Vandenbergh 1 D. Cattrysse 1 M. Sevaux 2 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

2 TNT 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

3 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 So rensen et al.

4 Problem delineation One depot Many (thousands) of known customers

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

6 Problem delineation One depot Many (thousands) of known customers Customers are divided into zones Zones have a known centre

7 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

8 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

9 Two sub-problems Minimize transportation cost Minimize distance to baseline solution Route balance Clustering problem: determine zones Later! So rensen et al.

10 (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

11 Multi-objective algorithm Objective integration Operators Structure Normalized weights i (x) = f i(x) fi min f max f min f n Additive objective function i i f (x) = i α i f n i (x) with i α i = 1

12 operators Objective integration Operators Structure 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

13 overview Objective integration Operators Structure 1: Multi-objective algorithm for routing at TNT Read baseline solution; Set weights α i ; while stopping conditions not satisfied do repeat Local search using neighbourhoods k and weights α i ; 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 α i ;

14 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...

15 (cont.) Real multi-objective optimization method Implement in commercial vehicle routing software Strategic supply chain evaluation tool Location of new distribution centres Use of different vehicle types...

16 Some solutions and some future research directions K. Sörensen 1 J. Vandenbergh 1 D. Cattrysse 1 M. Sevaux 2 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