Swarm Logistics

FAQs


1. Routing is how to get from A to B (e.g. Google maps or any other solution).
2. Vehicle Tour-planning is how to get the right sequence for 1 vehicle for the stops/ freight orders A, B, C, D, E, F (extensions like time windows and legal drivers times increase the complexity of the problem exponentially this is already NP complex).
3. Dispatching,orchestration or fleet Tour-planning is how to plan and optimize your fleet with e.g. 100 vehicles and 3000 freight orders with constraints like payload, volume, time etc. (extensions like heterogeneous fleets e.g. 3,5t and 40t increases the problem by a huge factor).
4. Swarm Logistics is collaboration of vehicles of even competing logistics companies with flexible time and location trans-shipments implemented by a server-less solution with edge computing.
Our Auto-Dispatcher (as the first feature of the Business Operating System) includes all 3 levels and is applicable for automated dynamic adaption, Level 4 is in the making.
Comment: Business Operating System automates the processes of a transportation company completely. (Dispatching, pricing, controlling, cost accounting, matchmaking vehicle/ driver, bidding on freight, predictive analytics etc)


In general a Business Operating System (B.O.S.) is a Robo-Manager that aggregates and processes data a manager would need to steer a company. The focus of the system is business decision making over a production program.
The B.O.S. enables whole companies, single organisational entities, down to an intelligent machine to be a part of the "Machine Economy".
We are building the B.O.S. for transportation companies and it consist of different features.


We do not provide frontends, but if you need a frontend or a Transport Management System that is compatible with our solutions, we can provide you with a solution through our partner. We collaborate with the industry leaders to offer you the best product for your business.
We are specialised in the algorithmic hard tasks, the backend, while others have more experience in terms of usability (UI/UX) and software integration with ERP or warehouse systems.


Swarm Logistics is collaboration of vehicles of even competing logistics companies with flexible time and location trans-shipments implemented by a server-less solution with edge computing. Each vehicle can be seen as own profit center.


We built our system architecture from the ground up to be compatible to autonomous vehicles. While autonomous vehicles are built to know how to drive, we provide them with the information where to drive.


We use multiple forms of AI (Artificial Intelligence).
For a collective decision making between the vehicles of a fleet or competing fleets we use so called "Swarm Intelligence". This field of AI is inspired by the behaviour of ant and bee colonies.
Furthermore we use "Machine Learning" in different features of our solution, where our algorithms get better over time and improve the results by a huge factor.
Each feature of the Business Operating System (B.O.S.) uses "artificial neural networks" in a different way and for a different task.



We are working on a blockchain based coordination platform, between trucks as a sport market for secondary freight (trans-shipment between vehicles). It is distributed, transparent and secure, where competing transportation companies are working together as equals without an intermediary. The network participants own the network. (Hanseatic league principal). There is no single point of failure. Smart contract of the blockchain are the perfect foundation for swarm intelligence.
When deployed the blockchain based platform will replace the monolithic server based platform. The Swarm Logistics Network requires 5G due to the latency.



We started building algorithms and systems for the logistics industry, but they are also applicable for any kind of fleets like cars, trucks, AGVs, delivery robots or for "Mobility as a Service" (MaaS)" in open environments (e.g. streets).
Nevertheless logistics is by far the most complex problem set, especially the last mile delivery. e.g. shuttle buses have 9 seats with 9 stops, where in the last mile problem set a courier has up to 200 stops a day and is part of normally a fleet of up to 100 vehicles per depot.
We believe that in the near future vehicles will change and there will be a mixed form of transport for passengers and cargo. Our system is built to be compatible for this scenario.


Our system can do both. Fore a more precise solution we recommend actually to use geolocations especially, where addresses are not available like construction sites.


1. Pick-up and delivery stands for that an entity (e.g. cargo etc.) is picked up at one location and delivered in the same sequence as picked-up. (pick-up A, deliver A, pick-up B, delivery B etc.)
2. Multi pick-up and delivery is the case, when the pick-up and deliveries of multiple entities are random. (pick-up A, pick-up B, pick-up C, delivery B, pick-up D, delivery A etc.)
3. Round trips pick up all entities from one point e.g. a depot and delivery the entity to multiple points in random order. (pick-up A, B, C, D: delivery B, delivery A, deliver C etc.)


We also built custom systems for customers, if the problem set is located in the mobility and optimization environment.
Furthermore we can adapt our existing system to different problem sets or ad additional constraints.
For problem sets, where optimization is just one part of the task, we can consult our trusted partners: Transport Management System provider, cloud provider, integration partners, research institutes etc.


Introducing heterogeneous vehicles into the optimization give us the advantage to introduce an cost optimization rather than just minimizing the distances.
The optimization gets much more complex (by a huge factor) but with extraordinary benefits.
Imagine the situation, where you have a heterogeneous fleet of vehicles, e.g. a 3.5t vehicle and a 40t truck. When the optimization is based on minimizing the driven kilometres/miles or time, what was the state of the art until now, the vehicles would be treated the same, but it is clear that these 2 vehicles have a completely different cost structure.
Another situation could be where 1 vehicle in your fleet has 1 driver/personnel on board and on the second vehicle you have a driver and a loader. Again it is clear that the cost structure changes.
Introducing a heterogeneous vehicle structure optimization showed in a pilot project that from 35% cost savings, 15% derived from taking the heterogeneity of vehicle costs into account.
If your fleet consists of mixed vehicles in size or brand, you will highly benefit from this unique feature.
EXAMPLE