1. Vehicle routing using remote asset monitoring: a case study with Oxfam Fraser McLeod, Tom Cherrett (Transport) Güneş Erdoğan, Tolga Bektas (Management) OR54, Edinburgh, 4-6 Sept 2012 1

2. Background www.oxfam.org.uk/shop

3. Donation banks Oxfam bank sites in England

4. Case study area 4

5. 5 Remote monitoring sensors

6. 6 Remote monitoring data

7. Problem summary (requirements) Visit shops on fixed days Visit banks before they become full Routes required Monday to Friday each week Start/end vehicle depot Single trips each day (i.e. no drop-offs) 7

8. Problem summary (constraints) Heterogeneous vehicle fleet –1 x 1400kg (transit van) –3 x 2500kg (7.5T lorry) Driving/working time constraints Time windows for shops 8

9. Objectives Maximise profit (£X per kg – £1.50 per mile) –where X = f(site) (e.g. 80p/kg from banks; 50p/kg from shops) Avoid banks overfilling –prevents further donations (= lost profit) –upsets site owners –health and safety 9

10. Data (locations, time, distance) Postcodes for 88 sites: –1 depot –37 bank sites –50 shops Driving distances/times between 3828 (= 88x87/2) pairs of postcodes –Commercial software –Times calibrated using recorded driving times 10

11. Data (demand) Weights collected from shops and banks (April 2011 to May 2012) Remote monitoring data (from July 2012) Shop demand = average accumulation rate x no. of days since last collection Bank demand – randomly generated 11

12. Assumptions (bank demand) Demand at bank i, day j = X i,j = max(X i,j-1 + d i,j-1, bank capacity) where d = donations = Y i,j.Z i,j Y = Bernoulli (P = probability of donation) Z = N(  = amount donated  mean daily donation amount, excluding days where no donations are made  estimated from collection data bounded by [0, bank capacity] 12

13. Assumptions (collection time) Collection time = f(site, weight) = a i + b i x i 13

14. Solution approach Look ahead period = 1 day (tomorrow) Minimum percentage level to be collected –(50% and 70% considered) Overfilling penalty (applied to banks not collected from) –fill limit (%) (75% and 95% considered) –financial penalty (£/kg) (£10/kg considered) 14

15. Solution approach Tabu search –Step 1 (Initialization) –Step 2 (Stopping condition): iteration limit –Step 3 (Local search): addition, removal and swap –Step 4 (Best solution update) –Step 5 (Tabu list update) –Go to Step 2 15

16. Results / KPIs 20 consecutive working days 3 random starting seeds Performance indicators –# bank visits –profit –distance –time –weight collected and lost donations 16

17. Results (# bank visits) 17 Probability of donation Penalty fill level

18. Profit 18

19. Distance 19

20. Time 20

21. Weight 21

22. Conclusions & Discussion Bank visits could be substantially reduced But benefits are limited by the requirement to keep shop collections fixed Can we improve our modelling approach? 22