Dr Wolfgang Garn
I worked as Programme director of Business Analytics and Acting Head of the Business Transformation Department at the University of Surrey. My research interests are in the area of Applied Mathematics, Operational Research and Business Analytics. I worked on analytics solutions, optimisations and simulation of processes for several companies. Formerly I was a Mathematician at Telekom Austria in the Department of Operations Research. My duties entailed Network Optimisations, Transportation Modelling, and Market Analysis amongst others. One of my major achievements was a mathematical model for a nationwide transition strategy from a copper to a fibre network. At the Defence Technology Centre (DTC) I worked on Agent and Decision Support Systems for MoD funded research. One of the outcomes enabled autonomous Agents to evaluate and act on military effects using Artificial Intelligence (Bayesian Networks). My role as Senior Scientist and Project Manager for Eurobios involved me with key clients such as Serco, Biffa, Unilever, DHL and BP. In this context mathematical solutions for environmental and delivery services were implemented. I am the CEO and founder of Smartana, which offers SMART Analytics solutions and consulting services to businesses. I am/was a member of the Institute for Operations Research and Management Sciences, and the Society for Modelling & Simulation. I earned my PhD in Simulation and Optimisation of Telecommunication processes at the Vienna University of Technology, in the area of Technical Mathematics and Computer Science.
Affiliations and memberships
- Business Analytics such as optimisations and mathematical programming and modelling
- Operational Research such as logistics, transportation, routing, scheduling
- Management Science, combinatorial optimisations, network flows, meta heuristics, e.g. genetic algorithms, simulated annealing
- Applied mathematics, Artificial Intelligence, Discrete Event Simulation (DES), Queueing Systems
- Kernel Density Estimators (KDE), Decision Support Systems (DSS), Bayesian Networks, Statistical Learning and Machine Learning.
- Machine learning techniques to gain insights into the sustainability of homes
- The rise of the machines - learning for environmental good and cost savings (Blog, May 24, 2017)
- Are we “boiled” for choice? (Blog, August 3, 2017)
- AI detects roof status using Drones (BA MSc dissertation summary, October 10, 2017)
- To build an advanced predictive tool to provide improved business decision making that promotes sustainable living for the social housing sector and provides efficient savings to housing providers.
Indicators of esteem
Reviewer for the ...
- European Journal of Operational Research
- Neurocomputing Journal
- International Journal of Production Economics
- International Conference on Information Systems
- and many more
Postgraduate research supervision
PhD - students
I am looking for PhD students! You should have a strong quantitative background (e.g. mathematics, computer science, physics, etc.). You should be interested in Business Analytics, Artificial Intelligence and/or Management Science (Operational Research).
Potential PhD topic areas:
- Business Analytics,
- Operational Research, or
- Artificial Intelligence.
Please send me a short email to express your interest.
- PhD studentships, PhD scholarships, PhD funds or PhD sponsorships might be possible,
- general information for PhD applicants.
Current PhD students
- Business Analytics
- Business Analytics arms you with the expertise in analysing data and creating knowledge - leading to competitive advantages for businesses. Artificial intelligence, machine learning and management science power decisions in business. You’ll gain data-led insights and optimise businesses by using descriptive, predictive and prescriptive analytics. It equips you with state-of-the art and new emerging tools to solve business transforming challenges. General enquiries: email@example.com, programme enquiries: firstname.lastname@example.org
- Machine Learning and Visualisations - Semester 2 (2020)
- Machine Learning & Visualisations are used to gain business insights for decision making. Data will be sliced, diced and visually analysed. Artificial Intelligence and statistical learning will be introduced. Techniques will be used for prediction, estimation or classification.
- Module catalogue (MANM354)
- Data Analytics - Semester 1 (2020)
- This module is the science of examining raw data in order to support businesses and organisations in their decision making. On one hand this module looks at relationships of entities in databases using the Structured Query Language to extract relevant information efficiently. On the other hand it introduces unstructured data concepts. Special focus is given to Big Data providing knowledge, analysis and practical skills to gain additional business and customer insights. Fundamental statistical techniques to extract the essential management information are shown.
- Module catalogue (MANM301)
- Supply Chain Analytics (level M) - Semester 2 (until 2019, now known as Operational Analytics)
- Operational Research/Management Science is used to solve supply chain (SC) aspects analytically. Mathematical programming techniques examine the Supply Chain's underlying transportation network which connects suppliers via transshipment nodes to its demand locations. Best locations for warehouses (or transshipment nodes) are determined using quantitative methods. Decision Science is used for in rational decision making under uncertainty. For instance optimal inventory levels are determined for warehouses and manufacturing using mathematical models. All kinds of business activities are optimised to give businesses a competitive advantage by maximising profit and minimising costs.
- Module catalogue (MANM304)
- Book recommendation: Introduction to Management Science: Modelling, Optimisation and Probability
- Introduction to Management Science - Semester 1 (until 2019)
- Methods and tools are used to tackle challenges occurring in the business and industrial environment. The obtained results are used for qualified decision making. This is an Applied Mathematics course.
- Module catalogue (MAN2093)
- Book recommendation: Introduction to Management Science: Modelling, Optimisation and Probability
- Information Systems Development (PG) - Semester 2 (2012)
- A hands-on approach to the development of Information Systems - using practical State-of-the-Art methods, tools and techniques.
- Module catalogue (MAN114)
- Issues in Operations Management (UL2) - Semester 1 (2010/2011)
- This lecture explores a set of critical areas in Operations Management in depth which is based on mathematical models (Operational Research/Management Science approach).
- Module catalogue (MAN2086)
- Project Management & Computer Lab. (PG, UG) - Semester 2 (2012)
- Business Research Project (FHEQ6 - year 3) - Semester 2 (2014)
- To analyse and critically evaluate existing work in order to deliver value to businesses. This module introduces statistical and quantitative methods.
- Module catalogue (MAN3116)
- Business Process Management (PG) - Semester 2 (2013)
- Shows the relationship between operations management and information systems, with hands-on experience in SAP. This include Discrete Event Simulations.
- Module catalogue
The contents covers topics such as:
- Waiting Line Models (e.g. Multiple Server Waiting Line)
- Transportation and Network Models (e.g. Shortest Route)
- Inventory Management (e.g. Economic Order Quantity model)
This will enable the reader:
- To increase the efficiency and productivity of business firms
- To observe and define ?challenges? in a concise, precise and logical manner
- To be familiar with a selected number of classical and state-of-the art Management Science methods and tools to solve management problems
- To create solution models, to develop and create procedures that offer competitive advantage to the business/organisation
- To communicate and provide results to the management for decision making and implementation
Through modelling and experimentation organisations and individuals can reshape their cognitive maps to develop and recognise new opportunities to improve their processes through improving the presentation and use of information.
services has been decreasing in a county in South of England. Hence,
methods that increase patronage, focus subsidies and stimulate the bus
industry are required. Our surveys and market research identified and
quantified essential factors. The top three factors are price, frequency,
and dependability. The model was further enhanced by taking into
account real time passenger information (RTPI), socio-demographics
and ticket machine data along targeted bus routes. These allowed the
design of predictive models. Here, feature engineering was essential
to boost the solution quality. We compared several models such as
regression, decision tress and random forest. Additionally, traditional
price elasticity formulas have been confirmed. Our results indicate
that more accuracy can be gained using prediction methods based on
the engineered features. This allows to identify routes that have the
potential to increase in profitability - allowing a more focused subsidy
Transformative learning theory argues that learning mainly occurs at the moment when meaning perspectives and meaning schemes change. It further postulates that transformation of meaning is initiated by reinterpreting past experiences and reframing them based on new experiences. Such a learning process is often activated by a ?distorting dilemma? where previous meanings are suddenly no longer valid. The theory of constraints provides a framework for problem solving which includes methods to facilitate logical thinking processes and contains a tool named the current reality tree. This tool facilitates interpreting reality through a guided and rules based reasoning process which is based on methods of validity testing to prove assumptions of reality. This thesis is concerned with the increase of purchasing efficacy through a transformative learning process facilitated by the application of the logical thinking processes. Ultimately, the study presents a methodology to increase purchasing efficacy by transforming mind-sets and to increase the level of consciousness by utilizing a logical thinking process framework. However, some limitations, such as a regional and cultural focus on the Philippines, as well as the specific context in which this research was carried out, should be considered. These shortcomings are mentioned to motivate scholars to enter future research avenues in purchasing and supply management, in organizational change processes and in the application of the logical thinking process framework.
One country in the region where this problem is particularly visible, and which has been heavily scrutinised by the ILO and implementation partners in particular for having high concentrations of what is referred to in the donor lexicon as Worst Forms of Child Labour (WFCL) is Ghana. A major focus of these assessments is artisanal and small-scale mining (ASM), low-tech, labour-intensive mineral extraction and processing which, throughout Ghana and most other areas in sub-Saharan Africa, is mostly poverty-driven, providing employment to otherwise incomeless families. The campaign spearheaded by the ILO under the auspices of the WFCL agenda to eliminate child labour from ASM in Ghana and the wider sub-region builds a case around how young boys and girls carry out arduous work and are generally being exploited at sites.
Recent research, however, has revealed that the child labour ?problem? in Ghana and rural sub-Saharan Africa more broadly is far more nuanced than has been diagnosed by donors. The ASM sector is no exception: research undertaken over the past decade has shown that the growth of its activities linked to a wider de-agrarianisation process ? specifically the movement of rural families into the nonfarm economy, in response to the inability of agriculture to sustain, fully, their economic needs ? to which the child labour ?problem? diagnosed is inextricably linked. Specifically, the ASM sector, being the region?s most important rural nonfarm activity, has become a popular ?off farm? destination for hundreds of thousands of families and other jobless masses. This movement has naturally contributed to the increased ?presence? of children at artisanal mines, where, contrary to the position of donors, work undertaken rarely extends beyond tasks similar to those carried out on family farms. The case of Ghana, the location of one of the largest and more dynamic ASM economies in sub-Saharan Africa, illustrates this very clearly.
The aim of this thesis is to build on these observations by engaging more critically with the main debates on child labour with a view to articulating more comprehensively why children are pursuing ?hazardous? work in ASM camps across the region. It does this by analysing key policy documents, conducting observations and semi-structured interviews with policymakers, non-governmental organisations (NGOs), community leaders, educators, parents and children. Together, these sources of information broach a rich range of issues for analysis and allow for the exploration and construction of broader discourses in connection with the main themes and theories of this research study.
This thesis provides a more comprehensive picture of the child labour phenomenon in rural sub-Saharan Africa. Findings suggest that many of the so-called ?exploited? children in ASM are engaging in what ILO officials themselves would consider light work akin to the chores countless young African girls and boys perform on family farms; that children?s earnings are being used to alleviate the economic hardships of their households but that work is generally taking place outside of school hours and during school vacations; and that for some children, the sole motivation for working at mines is to generate sufficient money to pay for school fees.
Overall, the research study informs debates on child labour, education and family hardship
heuristics with test-instances being in the Euclidean plane showed similar solution quality and runtime. On average, the nearest node solutions are approximately one percent better. The closest vehicle heuristic is especially important when the nodes (customers) are
not known in advance, e.g. for online routing. Whilst the nearest node is preferable when one vehicle has to be used multiple times to service all customers.
The second contribution is a closed form formula that describes the mTSP distance dependent on the number of vehicles and customers. Increasing the number of salesman results in an approximately linear distance growth for uniformly distributed nodes in a Euclidean grid plane. The distance growth is almost proportional to the square root of number of customers (nodes). These
two insights are combined in a single formula. The minimum distance of a node to n uniformly distributed random (real and integer) points was derived and expressed as functional relationship dependent on the number of vehicles. This gives theoretical underpinnings and is in agreement with the distances found via the previous mTSP heuristics. Hence, this allows to compute all
expected mTSP distances without the need of running the heuristics.