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
- Eleanor Mill - researches explainable AI in fraud detection
- Daniel Boos - researched predicting bankruptcy of banks (passed viva on 19/11/2021)
- Vasilis Nikolaou - researches machine learning applications to medicine
- Athary Alwasel - researches hybrid simulation and behaviour in health care
- Lin Fu - researches marketing, operations and systems
Previous doctoral students
- Angélique Gatsinzi – completed successfully in 2019
- Martin Schreiner – completed successfully in 2018
- Katja Hiltl – completed successfully in 2014
- Dirk Muehlenmeister (temporarily withdrawn)
- Frank Altmeyer (temporarily withdrawn)
- Iman Roozbeh (withdrawn)
- Naveed Akhtar Qureshi (completed)
Research student examinations
- PhD – Internal Chair for Dimitra Pappa (2017)
- PhD – External examiner for Olubusola Tejumola (Oct. 2016)
- DBA – Internal examiner for Julia Bartels (2015)
- PhD - Internal examiner and chair for Natasha Mashanovic (2011)
- 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: firstname.lastname@example.org, programme enquiries: email@example.com
- 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) [with interactive online-tutorial]
- 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) [with dedicated book available on Amazon, Kortext, BibliU]
- 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
- Garn, W., 2021. Balanced dynamic multiple travelling salesmen: algorithms and continuous approximations. Computers & Operations Research, Volume 136, p.105509. (free access - here, preprint on arXiv)
- Garn, W. (2018). Introduction to Management Science: Modelling, Optimisation and Probability. Smartana Ltd Publishing.
Purpose Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage. Methods We used the COVID-19 Radiography database , which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model’s performance. From the external test dataset, we calculated the model’s accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity. Conclusions Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19.
Background: Chronic obstructive pulmonary disease (COPD) is a heterogeneous group of lung conditions challenging to diagnose and treat. Identification of phenotypes of patients with lung function loss may allow early intervention and improve disease management. We characterised patients with the ‘fast decliner’ phenotype, determined its reproducibility and predicted lung function decline after COPD diagnosis. Methods: A prospective 4 years observational study that applies machine learning tools to identify COPD phenotypes among 13 260 patients from the UK Royal College of General Practitioners and Surveillance Centre database. The phenotypes were identified prior to diagnosis (training data set), and their reproducibility was assessed after COPD diagnosis (validation data set). Results: Three COPD phenotypes were identified, the most common of which was the ‘fast decliner’—characterised by patients of younger age with the lowest number of COPD exacerbations and better lung function—yet a fast decline in lung function with increasing number of exacerbations. The other two phenotypes were characterised by (a) patients with the highest prevalence of COPD severity and (b) patients of older age, mostly men and the highest prevalence of diabetes, cardiovascular comorbidities and hypertension. These phenotypes were reproduced in the validation data set with 80% accuracy. Gender, COPD severity and exacerbations were the most important risk factors for lung function decline in the most common phenotype. Conclusions: In this study, three COPD phenotypes were identified prior to patients being diagnosed with COPD. The reproducibility of those phenotypes in a blind data set following COPD diagnosis suggests their generalisability among different populations.
This research examines the application of the Theory of Swift, Even Flow (TSEF) by a distribution company to improve the performance of its processes for parcels. TSEF was deployed by the company after experiencing improvement fatigue and diminishing returns from the time and effort invested. The fatigue was resolved through the deployment of swift, even flow and the adoption of "focused factories". The case study conducted semi-structured interviews, mapped the parcel processes and applied Discrete Event Simulation (DES). From this study we not only documented the value of TSEF as a strategic tool but we also developed insights into the challenges that the firm encountered when utilising the concept. DES confirmed the feasibility of change and its cost savings. This research demonstrates DES as tool for TSEF to stimulate management thinking about productivity
In the United Kingdom, local councils and housing associations provide social housing at secure, low-rent housing options to those most in need. Occasionally tenants have difficulties in paying their rent on time and fall into arrears. The lost revenue can cause financial burden and stress to tenants. An efficient arrear management scheme is to target those who are more at risk of falling into long-term arrears so that interventions can avoid lost revenue. In our research, a Long Short-Term Memory Network (LSTM) based time series prediction model is implemented to differentiate the high-risk tenants from temporary ones. The model measures the arrear risk for each individual tenant and differentiates between short-term and long-term arrears risk. Furthermore it predicts the trajectory of arrears for each individual tenant. The arrears analysis investigates factors that provide assistance to tenants to trigger preventions before their debt becomes unmanageable. A five-years rent arrears dataset is used to train and evaluate the proposed model. The root mean squared error (RMSE) punishes large errors by measuring differences between actually observed and predicted arrears. The novel model benefits the sector by allowing a decrease in lost revenue; an increase in efficiency; and protects tenants from unmanageable debt.
The core goal of this paper is to identify guidance on how the research community can better transition their research into payment card fraud detection towards a transformation away from the current unacceptable levels of payment card fraud. Payment card fraud is a serious and long-term threat to society (Ryman-Tubb and d’Avila Garcez, 2010) with an economic impact forecast to be $416bn in 2017 (see Appendix A).1 The proceeds of this fraud are known to finance terrorism, arms and drug crime. Until recently the patterns of fraud (fraud vectors) have slowly evolved and the criminals modus operandi (MO) has remained unsophisticated. Disruptive technologies such as smartphones, mobile payments, cloud computing and contactless payments have emerged almost simultaneously with large-scale data breaches. This has led to a growth in new fraud vectors, so that the existing methods for detection are becoming less effective. This in turn makes further research in this domain important. In this context, a timely survey of published methods for payment card fraud detection is presented with the focus on methods that use AI and machine learning. The purpose of the survey is to consistently benchmark payment card fraud detection methods for industry using transactional volumes in 2017. This benchmark will show that only eight methods have a practical performance to be deployed in industry despite the body of research. The key challenges in the application of artificial intelligence and machine learning to fraud detection are discerned. Future directions are discussed and it is suggested that a cognitive computing approach is a promising research direction while encouraging industry data philanthropy.
This book explores a set of critical areas of Operations Management in depth. 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
In this paper a demand time series is analysed to support Make-To-Stock (MTS) and Make-To-Order (MTO) production decisions. Using a purely MTS production strategy based on the given demand can lead to unnecessarily high inventory levels thus it is necessary to identify likely MTO episodes. This research proposes a novel outlier detection algorithm based on special density measures. We divide the time series' histogram into three clusters. One with frequent-low volume covers MTS items whilst a second accounts for high volumes which is dedicated to MTO items. The third cluster resides between the previous two with its elements being assigned to either the MTO or MTS class. The algorithm can be applied to a variety of time series such as stationary and non-stationary ones. We use empirical data from manufacturing to study the extent of inventory savings. The percentage of MTO items is reflected in the inventory savings which were shown to be an average of 18.1%.
Existing works in the supply chain complexity area have either focused on the overall behavior of multi-firm complex adaptive systems (CAS) or on listing specific tools and techniques that business units (BUs) can use to manage supply chain complexity, but without providing a thorough discussion about when and why they should be deployed. This research seeks to address this gap by developing a conceptually sound model, based on the literature, regarding how an individual BU should reduce versus absorb supply chain complexity. This research synthesizes the supply chain complexity and organizational design literature to present a conceptual model of how a BU should respond to supply chain complexity. We illustrate the model through a longitudinal case study analysis of a packaged foods manufacturer. Regardless of its type or origin, supply chain complexity can arise due to the strategic business requirements of the BU (strategic) or due to suboptimal business practices (dysfunctional complexity). Consistent with the proposed conceptual model, the illustrative case study showed that a firm must first distinguish between strategic and dysfunctional drivers prior to choosing an organizational response. Furthermore, it was found that efforts to address supply chain complexity can reveal other system weaknesses that lie dormant until the system is stressed. The case study provides empirical support for the literature-derived conceptual model. Nevertheless, any findings derived from a single, in-depth case study require further research to produce generalizable results. The conceptual model presented here provides a more granular view of supply chain complexity, and how an individual BU should respond, than what can be found in the existing literature. The model recognizes that an individual BU can simultaneously face both strategic and dysfunctional complexity drivers, each requiring a different organizational response. We are aware of no other research works that have synthesized the supply chain complexity and organizational design literature to present a conceptual model of how an individual business unit (BU) should respond to supply chain complexity. As such, this paper furthers our understanding of supply chain complexity effects and provides a basis for future research, as well as guidance for BUs facing complexity challenges.
Abstract The mTSP is solved using an exact method and two heuristics, that balances the number of nodes per route. The first heuristic uses a nearest node approach and the second assigns the closest salesman. A comparison of heuristics with test-instances being in the Euclidean plane showed that the closest node approach delivers better solutions and a faster runtime. On average, the closest node solutions are approximately one percent better than the other heuristic. Furthermore, it is found that increasing the number of salesman or customers results in a distance growth for uniformly distributed nodes in an Euclidean grid plane. The distance growth is almost proportional to the square root of number of customers (nodes). In this context we reviewed the expected distance of two uniformly distributed random (real and integer) points. The minimum distance of a node to n uniformly distributed random (real and integer) points was derived and expressed as functional relationship. This gives theoretical underpinnings for - previously - empirical distance to salesmen growth insights.
Since the bus deregulation (Transport Act 1985) the patronage for bus 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 strategy.
In this paper we study the optimality of production schedules in the food industry. Specifically we are interested whether stochastic economic lot scheduling based on aggregated forecasts outperforms other lot sizing approaches. Empirical data on the operation’s customer side such as product variety, demand and inventory is used. Hybrid demand profiles are split into make-to-order (MTO) and make-to-stock (MTS) time series. We find that the MTS demand aggregation stabilizes, minimizes change-overs, and optimizes manufacturing.
Scheduling multiple products with limited resources and varying demands remain a critical challenge formany industries. This work presents mixed integer programs(MIPs) that solve the Economic Lot SizingProblem (ELSP) and other Dynamic Lot-Sizing (DLS) models with multiple items. DLS systems are clas-sified, extended and formulated as MIPs. Especially, logical constraints are a key ingredient in succeedingin this endeavour. They were used to formulate the setup/changeover of items in the production line. Min-imising the holding, shortage and setup costs is the primaryobjective for ELSPs. This is achieved by findingan optimal production schedule taking into account the limited manufacturing capacity. Case studies for aproduction plants are used to demonstrate the functionality of the MIPs. Optimal DLS and ELSP solutionsare given for a set of test-instances. Insights into the runtime and solution quality are given.
Dynamic routing occurs when customers are not known in advance, e.g. for real-time routing. Two heuristics are proposed that solve the balanced dynamic multiple travelling salesmen problem (BD-mTSP). These heuristics represent operational (tactical) tools for dynamic (online, real-time) routing. Several types and scopes of dynamics are proposed. Particular attention is given to sequential dynamics. The balanced dynamic closest vehicle heuristic (BD-CVH) and the balanced dynamic assignment vehicle heuristic (BD-AVH) are applied to this type of dynamics. The algorithms are applied to a wide range of test instances. Taxi services and palette transfers in warehouses demonstrate how to use the BD-mTSP algorithms in real-world scenarios. Continuous approximation models for the BD-mTSP’s are derived and serve as strategic tools for dynamic routing. The models express route lengths using vehicles, customers, and dynamic scopes without the need of running an algorithm. A machine learning approach was used to obtain regression models. The mean absolute percentage error of two of these models is below 3%.
In this paper we study the profitability of car manufacturers in relation to industry-wide profitability targets such as industry averages. Specifically we are interested in whether firms adjust their profitability in the direction of these targets, whether it is possible to detect any such change, and, if so, what the precise nature is of these changes. This paper introduces several novel methods to assess the trajectory of profitability over time. In doing so we make two contributions to the current body of knowledge regarding the dynamics of profitability. First, we develop a method to identify multiple profitability targets. We define these targets in addition to the commonly used industry average target. Second, we develop new methods to express movements in the profitability space from t to t + j, and define a notion of agreement between one movement and another. We use empirical data from the car industry to study the extent to which actual movements are in alignment with these targets. Here we calculate the three targets that we have previously identified, and contrast them with the actual profitability movements using our new agreement measure. We find that firms tend to move more towards to the new targets we have identified than to the common industry average. © 2012 Elsevier B.V. All rights reserved.
As a first contribution the mTSP is solved using an exact method and two heuristics, where the number of nodes per route is balanced. The first heuristic uses a nearest node approach and the second assigns the closest vehicle (salesman). A comparison of 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.
Industrial practices and experiences highlight that demand is dynamic and non-stationary. Research however has historically taken the perspective that stochastic demand is stationary therefore limiting its impact for practitioners. Manufacturers require schedules for multiple products that decide the quantity to be produced over a required time span. This work investigated the challenges for production in the framework of a single manufacturing line with multiple products and varying demand. The nature of varying demand of numerous products lends itself naturally to an agile manufacturing approach. We propose a new algorithm that iteratively refines production windows and adds products. This algorithm controls parallel genetic algorithms (pGA) that find production schedules whilst minimizing costs. The configuration of such a pGA was essential in influencing the quality of results. In particular providing initial solutions was an important factor. Two novel methods are proposed that generate initial solutions by transforming a production schedule into one with refined production windows. The first method is called factorial generation and the second one fractional generation method. A case study compares the two methods and shows that the factorial method outperforms the fractional one in terms of costs.
Lean and Swift-Even-Flow (SEF) operations are compared in the context of sorting facilities. Lean approaches tend to attack parts of their processes for improvement and waste reduction, sometimes overlooking the impact this will have on their overall pipeline. A SEF approach on the other hand is driven by a desire to reduce variations by enabling the practitioner to visualise himself as the material that flows through the system thus unearthing all the problems that occur in the process as a whole. This study integrates Discrete Event Simulations (DES) into the lean and SEF framework. A real world case study with high levels of variations is used to gain insights and to derive relevant simulation models. The models were used to find the optimal configuration of machines and labour such that the operational costs are minimised. It was found that DES and SEF have a common basis. Lean processes as well as SEF processes both converge to similar solutions. However, SEF arrives faster at a near optimum solution. DES is a valuable tool to model, support and implement the lean and SEF approach. The SEF approach is superior to lean processes in the initial phases of a business process optimisation. The primary novelty of this study is the usage of DES to compare the lean and SEF approach. This study presents a systematic approach of how DES and optimisation can be applied to lean and SEF operations.
Decision making is often based on Bayesian networks. The building blocks for Bayesian networks are its conditional probability tables (CPTs). These tables are obtained by parameter estimation methods, or they are elicited from subject matter experts (SME). Some of these knowledge representations are insufficient approximations. Using knowledge fusion of cause and effect observations lead to better predictive decisions. We propose three new methods to generate CPTs, which even work when only soft evidence is provided. The first two are novel ways of mapping conditional expectations to the probability space. The third is a column extraction method, which obtains CPTs from nonlinear functions such as the multinomial logistic regression. Case studies on military effects and burnt forest desertification have demonstrated that so derived CPTs have highly reliable predictive power, including superiority over the CPTs obtained from SMEs. In this context, new quality measures for determining the goodness of a CPT and for comparing CPTs with each other have been introduced. The predictive power and enhanced reliability of decision making based on the novel CPT generation methods presented in this paper have been confirmed and validated within the context of the case studies.
Drone delivery services (DDS) are an upcoming reality. Companies such as Amazon, DHL and Google are investing in developments in this area. German’s parcel delivery company DHL has begun applying them commercially. This emphasises the importance to have insights in the economic benefits of drone delivery services. This study compares drone delivery services with traditional delivery services. It looks at the cost effectiveness of integrating drones into a delivery services’ operations. The study identifies and categorise factors that control the delivery operations. It develops a mathematical model that compares 3D flight paths of a fleet of drones with a fleet of 2D van routes. The developed method can be seen as an extension of the Vehicle Routing Problem (VRP). Efficiency savings of drone services of real world rural postcode sectors are analysed. The study limits itself to the use of small unmanned aerial vehicles (UAVs) with a low payload, which are GPS controlled and autonomous. The case study shows time, distance and cost savings when using drones rather than delivery vans. The model reveals efficiency factors to operate DDS. The study shows the economic necessity for delivering low weight goods via DDS. The primary methodological novelty of this study is a model that integrates factors relevant to drones into the VRP.
Background: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous group of lung conditions that are challenging to diagnose and treat. As the presence of comorbidities often exacerbates this scenario, the characterization of patients with COPD and cardiovascular comorbidities may allow early intervention and improve disease management and care. Methods: We analysed a 4-year observational cohort of 6883 UK patients who were ultimately diagnosed with COPD and at least one cardiovascular comorbidity. The cohort was extracted from the UK Royal College of General Practitioners and Surveillance Centre database. The COPD phenotypes were identified prior to diagnosis and their reproducibility was assessed following COPD diagnosis. We then developed four classifiers for predicting cardiovascular comorbidities. Results: Three subtypes of the COPD cardiovascular phenotype were identified prior to diagnosis. Phenotype A was characterised by a higher prevalence of severe COPD, emphysema, hypertension. Phenotype B was char-acterised by a larger male majority, a lower prevalence of hypertension, the highest prevalence of the other cardiovascular comorbidities, and diabetes. Finally, phenotype C was characterised by universal hypertension, a higher prevalence of mild COPD and the low prevalence of COPD exacerbations. These phenotypes were reproduced after diagnosis with 92% accuracy. The random forest model was highly accurate for predicting hypertension while ruling out less prevalent comorbidities. Conclusions: This study identified three subtypes of the COPD cardiovascular phenotype that may generalize to other populations. Among the four models tested, the random forest classifier was the most accurate at predicting cardiovascular comorbidities in COPD patients with the cardiovascular phenotype.
The catchment area along a bus route is key in predicting bus journeys. In particular, the aggregated number of households within the catchment area are used in the prediction model. The model uses other factors, such as head-way, day-of-week and others. The focus of this study was to classify types of catchment areas and analyse the impact of varying their sizes on the quality of predicting the number of bus passengers. Machine Learning techniques: Random Forest, Neural Networks and C5.0 Decision Trees, were compared regarding solution quality of predictions. The study discusses the sensitivity of catchment area size variations. Bus routes in the county Surrey in the United Kingdom were used to test the quality of the methods. The findings show that the quality of predicting bus journeys depends on the size of the catchment area.
You can play against "Edi" a chess program using Matlab. It uses a greedy heuristic to find the "best" move.