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

• Conext & Motivation

• Introduction to VRP

• Our Problem: Heterogenous + Green VRP

• How we solve it?

• Results

• Conclusions and Future Research

Content

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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).

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).

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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

3. Heterogeneous & Green

Heterogeneous VRP, Different types of

vehicles:

• EV: Electric Vehicles

• ICE: internal combustion engine

• PHEV: plug-in- hybrid electric vehicles.

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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

4. How we solve it? (1/2)

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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

5. How we solve it? (2/2)

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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

6. Results

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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.

7. Conclusions & Future research

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• 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).

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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