Partners

Who we are?
AMCS is Ireland’s largest indigenous software company. We are a market
leader in the environmental service industry. AMCS employs over 1300+
people across 12 countries. We are Headquartered in Limerick, Ireland and
have offices in North America, Europe, Asia, and Australia.
 
What we do?
We deliver enterprise cloud-based solutions worldwide. AMCS supports over
2,650 customers across 22 countries. Our team is at the cutting edge of
technology and innovation. We help our customers to reach a more
sustainable future through reducing their carbon footprint and to work in a
more environmentally conscious way.
Sustainability at the heart of AMCS
Sustainability is not just a “nice to have” anymore. It’s an imperative. AMCS is
setting a new standard for business success, where sustainability improves
performance and allows companies to scale, power growth, and enable them
to leave the planet better than you found it – Performance Sustainability.
Performance Sustainability allows companies to take control of their
profitability and create better outcomes for employees, customers, our
communities, and the planet.
 
Your AMCS Story begins here...
We offer a fast-paced and innovative workplace where ongoing support,
collaboration, flexibility, and communication are prioritized. Working at AMCS,
you can expect a competitive salary and benefits package. On top of this, we
are always looking at ways to promote career progression and develop our
workforce.
 
What is it like to work at AMCS?
"The great thing about working at AMCS is the people. They really make the
job enjoyable" - Aindreas Hodgins, Product Owner
From Product Development, Software Support and Professional Services to
Business Support, Marketing and Sales, our people share what makes
working at AMCS Group so unique and meaningful to them.
Watch our video to learn more. – https://youtu.be/64cBXanxUew
 
Life at AMCS
 Choose how you work, hybrid or onsite?
 Gain extensive training, guidance, and support.
 Earn a competitive package with benefits.
 Work in an environment that is truly collaborative, innovative, and
supportive.

 Have a clear pathway for your progression.
 Make an impact and be part of digital ways to a cleaner world!
View our open roles below and start Your AMCS Story.
https://www.amcsgroup.com/careers/
 
Vacature:
https://www.amcsgroup.com/careers/associate-consultant-167561/

Thesis projects:

VRPs with Interdependent Orders
Project group: Kristian Milo Hauge (AMCS)

Project background:
In the world of waste collection, it is not always only about collection and waste. Specifically for bulk waste in skip containers, the container itself is often part of the optimisation. This includes
delivering, emptying, and sometimes returning the containers to their owners. A container that is not owned by the customer can be delivered to another customer who has requested the same type
of container, instead of simply being returned to a depot. This can save a lot of driving to and from depots. However, because of this, and because a vehicle can only carry a handful of full containers at
a time, a route often consists of stops that are tightly connected to each other and even depend on each other. Scheduling of terminal stops and choice of terminals can also depend on and/or affect
the orders on a route. When there is a lot of interdependence between stops, an algorithm that is well suited for optimising other types of VRPs may not be well suited for this problem.

Project assignment:
Given real-life data, the task is to design and implement an algorithm for solving the described VRP.

Prerequisites:
Courses in Operations Research and knowledge of metaheuristics. Good programming skills are an
advantage.
 

Very Large TSP with Geographical Constraints
Project group:
Kristian Milo Hauge (AMCS)

Project background:
In a high-density VRP such as residential waste collection or letter distribution, most addresses in a city or region need to be visited. At AMCS we are experimenting with a model where all stops are
placed in one long “master sequence”. This master sequence is in some ways just modelled as a very long route. But it is way too long to be used as it is. However, real operational routes are created
from this master sequence in a process that involves little to no optimisation. Routes can then be created with varying lengths and durations depending on demand and resources on a given day. This
adds robustness and flexibility to a master plan. To maintain some sort of order, the master sequence should to some extent finish an area before entering a new area. This also makes it feasible/practical to divide the master sequence across different days when routes are created. The definition of an “area” is not strict.

Project assignment:
Given real-life data, the task is to design and implement an algorithm that can build a master sequence that displays the requested geographical features. The goal is to be able to handle tens of
thousands of stops.

Prerequisites:
Courses in Operations Research and knowledge of metaheuristics. Good programming skills are an advantage.
 

Sequence Building with Narrow Time Windows
Project group:
Kristian Milo Hauge (AMCS)

Project background:
Given a set of orders, a route/sequence needs to be built with the following constraints:

- A small portion of the orders (< 10%) will have time windows. Time windows can be as
narrow as 30 minutes. - A feasible solution is not guaranteed due to time windows and order locations, so soft time windows are allowed

– there will be an increasing cost the larger a time window violation.
- A good solution must be produced as fast as possible. A route consists of around 100-200 orders.

Project assignment:
Given real life data, implement an algorithm that can build an order sequence give the listed
constraints.

Prerequisites:
Courses in Operations Research and knowledge of metaheuristics. Good programming skills are
necessary.
 

 

Large Scale Residential VRP
Project group: Kristian Milo Hauge (AMCS)

Project background: Residential VRPs involve most addresses in a town or city. The number of orders can therefore be
very high, think 10000 to 100000. Even though these problems have few or no restrictions, they are
still challenging to solve due to their size.

Project assignment: Given real life data, implement an algorithm for solving large VRPs.
Ideas for extensions/focus areas:
- With no restrictions, routes are expected to not overlap. Seek to reduce/avoid this.

- Routes that are neighbours are often expected to be separated along the most logical lines. These lines are usually easy to identify or compare for the human eye and are often related
to features such as highways, rivers, or “gaps” between orders such as parks or industrial areas.
- Having a distance matrix available is useful, but for very large VRPs, memory is precious.
How many distances do we need to calculate and cache?

Prerequisites: Courses in Operations Research. Good programming skills are an advantage.
 

Building Nice-Looking Order Sequences for Side-Loaders
Project group:
Kristian Milo Hauge (AMCS)

Project background:
A side-loader is a term used for a vehicle, such as a garbage truck, that can only service orders on one side of the road. A normal road in a residential neighbourhood that is serviced by a side-loader
must therefore be visited twice, once in each direction. At the same time, U-turns are not allowed on the route. The vehicle is too large and the roads not wide enough.
Determining a good sequence for the orders on a route usually focuses on KPIs such as the total driving time or the total distance covered. But the shortest or fastest route is not always the best. It
is sometimes also expected that a vehicle finishes the area it started to service before it moves on to the next. And this can be a challenge for side-loaders due to their limited options for movement and
service. A side-loader route can start in one area and then “wander off” to different areas before returning much later to service the remaining orders in the first area.

Project assignment:
Given real life data, implement an algorithm for building an order sequence for a side-loader route. The algorithm should consider the typical concrete KPIs, total driving time and/or route length, but
also the desire that routes are “pretty” and do not have too many “left over” orders in an area; orders that are not serviced until much later.

Prerequisites:
Courses in Operations Research. Good programming skills are an advantage.