In this presentation the authors (Angel A. Juan, Jarrod Goentzel, Tolga Bektas) discuss the vehicle routing problem with multiple driving ranges. The presentation describes an integer programming formulation and a multi-round heuristic algorithm that iteratively constructs a solution to the problem. Using a set of benchmarks adapted from the literature, the algorithm is employed to analyse how distance-based costs are increased when considering ‘greener’ fleet configurations – ie, when using electric vehicles with different degrees of autonomy.

1. 1
Angel A. Juan
Computer, Multimedia &
Telecommunication Dept.
IN3 - UOC, Barcelona, SPAIN
Tolga Bektas
University of Southampton,
Southampton, UK
Jarrod Goentzel
Massachusetts Institute of
Technology, Cambridge, MA, USA
Routing fleets with multiple driving ranges: is it
possible to use greener fleet configurations?
Presenting: Pol Arias, MSc
Visiting Researcher
IN3 - Smart Logistics & Production

2. • Conext & Motivation
• Introduction to VRP
• Our Problem: Heterogenous + Green VRP
• How we solve it?
• Results
• Conclusions and Future Research
Content
2

3. 1. Context & Motivation
3
CAN WE GO GREENER?
• Reducing “carbon footprint” and
addressing other environmental issues
• Cost risk associated with dependence
on oil-based energy
• Availability of government subsidies
• Advances in alternative energy
technology
ICEs and PHEVs have almost unlimited
driving-range capabilities (they can rapidly
refuel at any service station throughout the
route). However the driving range for an
electric vehicle is constrained by the amount
of electricity stored in its battery (it cannot
rapidly recharge during the route).

4. 2. Introduction – VRP and Rich VRP
Vehicle Routing Problem (VRP), is a
combinatorial problem seeking to service a
number of costumers with a fleet of vehicles
(Dantzig and Ramser, 1959).
4
Customers
(demand)
Edge in a route
Depot
(resources)
Caceres-Cruz, J., Arias, P., Guimarans, D., Riera, D., &
Juan, A. A. (2014). Rich Vehicle Routing Problem: Survey.
ACM Computing Surveys (CSUR), 47(2), 32.
Rich VRP (Caceres et al., 2014) adds real
life constraints to the base problem:
• Heterogeneous VRP
• Green VRP
• Open Routes VRP

5. 3. Heterogeneous & Green
Heterogeneous VRP, Different types of
vehicles:
• EV: Electric Vehicles
• ICE: internal combustion engine
• PHEV: plug-in- hybrid electric vehicles.
5
Green VRP, tries to minimize de ecological
impact of different factors:
• Fuel consumption
• Electric Engines
Objective: minimise the distance cost of
using the greenest configuration

6. 4. How we solve it? (1/2)
6
CVRP* = CVRP with route
maximum distance
Round 1: ICE
MDR: unlimited
Assumed fleet:
2 ICEs + 3 EVs
1
1
2
3
4
3 4
5
2

7. 5. How we solve it? (2/2)
7
Notes:
(a) Number of rounds <= Number of veh. types
(b) If heterogeneity index  0 then Multi-round
sols  VRP sols
Round 1: ICE
MDR: unlimited
Assumed fleet:
2 ICEs + 3 EVs
1
2
3 4 5
1
2
3
4
5
CVRP* = CVRP with route
maximum distance

8. 6. Results
8
Changing the distribution to greener vehicles we maintain the distance cost
associated to each problem (benchmark’s used cost). At the same time we can choose
between different configurations that can be used as a decision making tool.

9. 7. Conclusions & Future research
9
• Support the hypothesis that hybrid and electric vehicles can be used in routing
problems without necessarily incurring significantly higher distance-based costs.
• Numerical experiments demonstrate that this approach provides attractive
solutions for all tested benchmarks.
• Models that monetize the carbon footprint could be incorporated to evaluate
solutions across an economic-environmental continuum.
• The impact of topography could be explored.
Caceres-Cruz, J., Arias, P., Guimarans, D., Riera, D., &
Juan, A. A. (2014). Rich Vehicle Routing Problem: Survey.
ACM Computing Surveys (CSUR), 47(2), 32, (indexed in
ISI SCI, 2013 IF = 4.043, Q1).
Juan, A. A., Goentzel, J., & Bektaş, T. (2014). Routing fleets with
multiple driving ranges: Is it possible to use greener fleet
configurations?. Applied Soft Computing, 21, 84-94, (indexed in
ISI SCI, 2012 IF = 2.140, Q1).

10. 10
Angel A. Juan
Computer, Multimedia &
Telecommunication Dept.
IN3 - UOC, Barcelona, SPAIN
Tolga Bektas
University of Southampton,
Southampton, UK
Jarrod Goentzel
Massachusetts Institute of
Technology, Cambridge, MA, USA
Routing fleets with multiple driving ranges: is it
possible to use greener fleet configurations?
Presenting: Pol Arias, MSc
Visiting Researcher
IN3 - Smart Logistics & Production