Journey management and optimisation in difficult frontier regions. www.inquiron.com @inquiron

1. Optimising movement in difficult
frontier regions.
An Inquiron Lab project
www.inquiron.com © Inquiron 2015

3. The lab’s current focus is to
increase the efficiency of
movement for organisations in
frontier regions.
We do that by collecting data on
journeys, which we then present
to users clearly and effectively to
help them make better
decisions.

4. The challenge we face is how to
move people safely and
securely through a complex
environment.
Resources are limited and the
organisation is often dispersed.
Road conditions are poor, and
hazards are common.

5. The problems we are trying to solve include
the following:
Scheduling: how to task drivers and
teams.
Optimisation: how to increase
efficiency while allowing for an
acceptable level of redundancy.
Routing: how to choose the safest, not
the shortest or quickest path.
This is a combination of operations research,
computer and data science.

6. The vehicle routing problem(1) is made more
complex in our setting.
We have more uncertainty in our
journeys. Movements are often escorted by
security teams, who stay with them at their
work locations for an uncertain period of time
and aren’t then available for retasking.
Importantly, the shortest or quickest path
isn’t always the safest path.
Question: how do we model efficiency?
(1) The vehicle routing problem (VRP) is a combinatorial optimization and integer programming problem seeking to service a
number of customers with a fleet of vehicles. Proposed by Dantzig and Ramser in 1959,[1] VRP is an important problem in
the fields of transportation, distribution and logistics. Often the context is that of delivering goods located at a central depot
to customers who have placed orders for such goods. The objective of the VRP is to minimize the total route cost.
Determining the optimal solution is an NP-complete problem in combinatorial optimization, so in practice heuristic and
deterministic methods have been developed that find acceptably good solutions for the VRP.

7. In frontier regions, the shortest or quickest
path isn’t always the best; we might want to
avoid places. For example, government
offices during demonstrations.
Dynamic routing aims to introduce the
ability to avoid potential hazards, at certain
times, and route the movement away from
the threat.
How can we modify routing algorithms to
avoid points instead of going via waypoints?

8. Prepare
Time spent driving
Variable (Td)
Wheels up
Time spent waiting on a passenger
Variable (Tw)
Time spent driving
Variable (Td)
Time spent waiting on next task
Variable (Tn)
Standby
Time spent briefing, waiting on a
passenger - Variable (Tw)
Time spent preparing (Tp)
Time spent waiting on a passenger,
debriefing - Variable (Tw)
Mission A -
Round Trip
Mission B -
drop-off
Mission C -
delivery
Wheels down
Wheels up
Wheels down
Ready for next
task
Movement across an oil company’s fleet
involves constraints and stakeholders
different from a taxi or delivery company.
In frontier regions, the driver or team will
spend time preparing via pre-mission checks,
will wait for the passenger prior to the
mission start, with the passenger at site, and
will make sure the passenger is accounted
for when returning to base. They will then
spend time debriefing, before being able to
accept a new task.
Time on Task (Tt) = (Tp) + (Tw) + (Td)

9. Too much time spent waiting
(could be doing something else)
Poorly utilised asset
(get more from this resource)
Optimised movement
(efficient use of resource)
No time to wait
(not much redundancy)
Optimising journeys can be difficult in this
setting.
There are different ways we can measure
efficiency in terms of time spent on task, time
spent driving versus time spent waiting,
distance driven, and number of journeys
undertaken.
What is the best way to model efficiency?
What is the optimal point at which the utility
of the fleet is maximised while allowing
sufficient redundancy in the system for
maintenance and administration?
Hours
spent on
task
Hours spent not on task

10. So, to recap, we’re trying to solve
the following:
How do we schedule limited
resources under high
demand?
How do we route resources
safely and securely?
How do we optimise the trade
off between efficiency and
redundancy?

11. Can you help us solve these
problems?
If so, please contact us at
info@inquiron.com, quoting the
following reference to help us
bring you into our discussion
quickly.
#vehicleroutingproblem

12. A final question:
Are we thinking big enough?
An Inquiron Lab project
www.inquiron.com © Inquiron 2015