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Dr. Alexander Souza
Alexander is the founder of algomia. He is specialized on algorithms, data science, and programming in Java. He studied Computer Science and Mathematics at TU Munich and Rensselaer Polytechnic Institute. Later on, he did his PhD at ETH Zurich and was researcher and assistant professor at the University of Freiburg and Humboldt University Berlin. Afterwards, he moved to the economic world, where he built optimizers, decision support systems, and information extraction software. His main projects are in the logistics domain, especially railway optimization, and in the health care sector. He teaches Algorithmics at the University of Zurich.
Our continous innovation process and viable solutions kit helps you harness data sources, combine relevant information, exploit these algorithmically, and drive innovation.
Companies often sit on a treasure trove of data waiting for productive use. When they come from various sources, the challenge of harnessing the data arises. In order to make information that is yet undiscovered but possibly relevant accessible for management processes and business decisions, the appropriate approach and software support becomes an issue. This is where Algomia starts - we make data sources usable and specifically pursue individual queries in order to filter out relevant information and make it usable for decision-making processes. We develop tailor-made algorithms and pave the way for sustainable innovations and sound management decisions. We rely on our own Viable Solutions Kit as a central tool.
In a three-phase continuous innovation process, we develop solutions for your company together with you – step by step.
The aim of the Envisioning workshop is to jointly identify opportunities, goals and challenges as well as to develop sound visions of solutions. The identification of relevant data sources plays an equally important role here.
The results of the workshop consist of solution formulations and recommendations for action for each corresponding business case as well as an offer for the Solution Sprint in Phase 2.
The aim of the Solution Sprint is the implementation a Minimal Viable Product (as proof of concept) based on the findings gained from the workshop, as well as the in-depth exploration of relevant data sources including analysis.
In the Solution Sprint, the Viable Solutions Kit can, for example, be used to create forecasts, reduce unnecessary risks and expenses, and make important decisions at an early stage of development. Following this phase, an offer for an Innovation Budget will be prepared.
In this phase, the Minimal Viable Product is further developed and optimized. Within the variable scope of the innovation budget, you benefit from all the advantages of the Viable Solutions Kit.
The Innovation Budget includes an agreed contingent of working hours, to be used for consulting and development tasks. Furthermore, the licence costs for the Viable Solutions Kit are covered. It is intended that the Innovation Budget repeats over time, hence allowing for a continuous innovation process.
The viable solutions kit is a software system that bundles many aspects of data-oriented solutions: web-based server, database, data piplining, algorithmics, and a web-based user interface.
The Viable Solutions Kit supports the Data Driven Innovations process, can hence be used in many ways, and is not focused on specific industries. Its functions can be individually extended and adapted to create a specific solution that is tailored to your business case.
Enables new data sources and allows pipelining into downstream business logic.
Extracts information and summarizes data on charts or clear reports.
Executes algorithms to support business-critical decisions or formulate recommendations for action.
Provides capabilities for user interfaces defined by the domain-expert.
An optimizer is a software that finds a solution for your planning challenge in a mathematical way. We are specialized on constructing and implementing custom-made optimizers.
The Resource Scheduling problem consists of assigning jobs to resources in such a way that a valid plan is objtained. This involves precedence relations between jobs, release dates, deadlines, and job synchronization. The goal is to maximize the number of jobs that can be scheduled together.
algomia has designed and implemented a Resource Scheduling optimizer. This solution has been applied in several contexts successfully. It has helped the scheduling of critical tasks during the go-live of a planning software in logistics. It can also be applied in the scheduling of appointments, medical personnel, rooms, and equipment in the health-care domain. References can be given upon request.
The Crew Planning challenge in railway optimization is the task of gathering train legs into tours for train drivers. Each tour must satisfy a multitude of constraints. The main objective is to minimize the number of tours (and additional secondary goals).
algomia is involved in the design and implementation of a Crew Planning optimizer in the Swiss railway industry. The mandate covers the design of algorithms, support in their implementation, and testing. References can be given upon request.
Information extraction and content search are the basis for modern business intelligence. We design and implement these systems tailor-made to your case.
In the area of Information Extraction, the main challenge is to transform raw data into useful information. This often involves the detection to text in images, enabling full-text search, pattern recognition in text, and destillation of the gained insights into crisp reports and statistics.
Dr. Souza has been involved in many of these projects in the health-care domain. The scope varied from software for document scaning, achive exploration, and pattern recognition in unstructured text in dossiers. References can be given upon request.