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V ehicle routing using remote asset monitoring: a case study with Oxfam

This case study focuses on using remote asset monitoring to optimize vehicle routing for Oxfam, specifically for visiting donation banks and shops. The objective is to maximize profit, prevent overfilling of banks, and improve health and safety. The study explores data analysis, assumptions, solution approach, and results.

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V ehicle routing using remote asset monitoring: a case study with Oxfam

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

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

  3. 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

  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

  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,j Y = Bernoulli (P = probability of donation) Z = N(m, s) = amount donated • m= mean daily donation amount, excluding days where no donations are made • sestimated 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. Probability of donation Results (# bank visits) Penalty filllevel

  18. Profit

  19. Distance

  20. Time

  21. Weight

  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?

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