Optimising movement in difficult frontier regions.

An Inquiron Lab project

www.inquiron.com © Inquiron 2015

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.

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.

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.

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.

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?

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)

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

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?

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

A final question: Are we thinking big enough?

An Inquiron Lab project

www.inquiron.com © Inquiron 2015