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

Background

www.oxfam.org.uk/shop

Donation banks

Oxfam bank sites in England

4

Case study area

5

Remote monitoring sensors

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)

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

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

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

12

Assumptions (bank demand)

• Demand at bank i, day j = Xi,j = max(Xi,j-1 + di,j-1, bank capacity)

where d = donations = Yi,j.Zi,jY = Bernoulli (P = probability of donation)Z = N(m, s) = amount donated

• m = mean daily donation amount, excluding days where no donations are made

• s estimated from collection data• bounded by [0, bank capacity]

13

Assumptions (collection time)

• Collection time = f(site, weight) = ai + bi xi

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)

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

16

Results / KPIs• 20 consecutive working days • 3 random starting seeds• Performance indicators

– # bank visits– profit– distance– time– weight collected and lost donations

17

Results (# bank visits)Probability of donation

Penalty fill level

Exist -ing

50% 70% Exist -ing

50% 70% Exist -ing

50% 70%0

50

100

150

200

250 240

8165

240

125

96

240

135

102

Minimum collection

bank

vis

its in

20

days

p = 0.8p = 0.5p = 0.2

18

Profit

Exist -ing

50% 70% Exist -ing

50% 70% Exist -ing

50% 70%0

102030405060708090

100

57.608 59.659 57.99664.049 65.036 64.535 65.754 65.727 64.804

Minimum collection

proft

(£/1

000)

p = 0.8p = 0.5p = 0.2

19

Distance

Exist -ing

50% 70% Exist -ing

50% 70% Exist -ing

50% 70%0

4000

8000

12000

16000

20000

13714 13331 1309313819 14270

13544 14036 14500 13938

Minimum collection

dist

ance

(km

)p = 0.8p = 0.5p = 0.2

20

Time

Exist -ing

50% 70% Exist -ing

50% 70% Exist -ing

50% 70%0

100200300400500600700800900

1000

738684 671

737 725 700745 728 712

Minimum collection

time

(hou

rs)

p = 0.8p = 0.5p = 0.2

21

Weight

Existing 50% 70% Existing 50% 70% Existing 50% 70%0

4

8

12

16

20

0.3652 0.2138 0.35650.2975 0.0773 0.1903 0.1762 0.0581 0.1329

Minimum collection

wei

ght c

olle

cted

& lo

st d

onati

ons

(kg/

1000

)p = 0.8p = 0.5p = 0.2

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?