DataCafé

As we look ahead to a new year, and reflect on the last, we consider how data science can be used to optimise the future. But to what degree can we trust past experiences and observations, essentially relying on historical data to predict the future? And with what level of accuracy?
In this episode of the DataCafé we ask: how can we optimise our predictions of future scenarios to maximise the benefit we can obtain from them while minimising the risk of unknowns?
Data Science is made up of many diverse technical disciplines that can help to answer these questions. Two among them are mathematical optimisation and machine learning. We explore how these two fascinating areas interact and how they can both help to turbo charge the other's cutting edge in the future.
We speak with Dimitrios Letsios from King's College London about his work in optimisation and what he sees as exciting new developments in the field by working together with the field of machine learning.
With interview guest Dr. Dimitrios Letsios, lecturer (assistant professor) in the Department of Informatics at King's College London and a member of the Algorithms and Data Analysis Group.

Further reading

• Dimirios Letsios' publication list (https://bit.ly/35vHirH via King's College London)
• Paper on taking into account uncertainty in an optimisation model: Approximating Bounded Job Start Scheduling with Application in Royal Mail Deliveries under Uncertainty (https://bit.ly/3pLHICV via King's College London)
• Paper on lexicographic optimisation: Exact Lexicographic Scheduling and Approximate Rescheduling (https://bit.ly/3rS8Xxk via arXiv)
• Paper on combination of AI and Optimisation: Argumentation for Explainable Scheduling (https://bit.ly/3oobgGF via AAAI Conference on Artificial Intelligence)

Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.

Recording date: 23 October 2020
Interview date: 21 February 2020

Intro music by Music 4 Video Library (Patreon supporter)

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How can we spot a change in a jet engine vibration that might mean it’s about to fail catastrophically? How can a service forecast adapt to unexpected changes brought about by a pandemic? How might we spot an increase in rate of change of pollution in the atmosphere? The answer to all these questions is changepoints, or rather changepoint detection.

Common to all these systems is a set of ordered data, usually a time series of observations or measurements that may be noisy but have some underlying pattern. As the world changes, so those changes might lead to dramatic changes in the measurements and a disruption of the usual pattern. Unless these forecasts or failure-detection systems are updated quickly to take account of a change in measurement data, they will likely produce erroneous or unpredictable results.

Changepoints have many important applications in areas such as:

• Climatology
• Genetic sequencing
• Finance
• Medical imaging
• Forecasting in industry

We speak to statistician Dr. Rebecca Killick from Lancaster University about her work in changepoint detection and how it is a critical part of the statistical toolkit for analysing time series and other ordered data sets. In particular:

• In forecasting where most methods tend to work on the basis of extrapolating trends, it is essential to know if a changepoint has occurred so that a refreshed model calculation can be started.
• If there is a change in the underlying dynamics of a system that causes a complex change in the observed output then this can often be detected with a changepoint. This might be indicative of a mechanical failure or impending change in operation or an unobserved event buried deep in a difficult-to-measure environment, like a nuclear reactor.

With interview guest Dr. Rebecca Killick, Associate Professor of Statistics at Lancaster University.

Further reading

• Rebecca Killick’s publications (via Lancaster University)
• Changepoints Overview Paper: changepoint: An R package for changepoint analysis (pdf via Journal of Statistical Software)
• R Package: changepoint: Methods for Changepoint Detection (R package via CRAN library)
• PELT algorithm paper: Optimal detection of changepoints with a linear computational cost (pdf via arXiv)
• Paper: Distinguishing Trends and Shifts from Memory in Climate Data (paper via American Meteorological Society)
• R Package: EnvCpt: Detection of Structural Changes in Climate and Environment Time Series (R package via CRAN library)

Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.

Recording date: 10 June 2020
Interview date: 5 June 2020

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Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.

How do big grocery retailers maintain product availability for their customers day after day while minimising food wastage and storage costs? The answer is Inventory Optimisation, the science of maintaining sufficient stock levels of a set of products so that customers see an appropriate level of availability when they walk into your store.

The trade-off
It’s hard because it often costs money to maintain a large inventory of products, because of space that is given over to bulky stock as the cost of buying a large amount of potentially expensive items without getting a return on that investment until sale. A long lead time from stocking to use or sale means no value is extracted from those items and that can have cashflow implications for a company while obviously minimising risk of so-called stockout.

Wastage
A significant further complication comes from food and grocery retail where the items being stocked are themselves perishable with varying expiry dates. Further significant costs are incurred if the product expires while still being stocked. This leads to huge food waste problems around the world which in turn have a significant carbon and environmental impact on the direct supply chain for the retailer.

With interview guest Dr. Anna Lena-Sachs, Lecturer in Predictive Analytics at the Department of Management Sciences, Lancaster University.
https://www.lancaster.ac.uk/lums/people/anna-lena-sachs
Further Reading

• Anna-Lena Sach's list of papers (via University of Lancaster)
• Book: Retail Analytics: Integrated Forecasting and Inventory Management for Perishable Products in Retailing (contents via Springer)
• Paper: The data-driven newsvendor with censored demand observations (summary via ScienceDirect)
• Paper: A stochastic model for joint spare parts inventory and planned maintenance optimisation (summary via ScienceDirect)

Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.

Recording date: 29 Jan. 2020
Interview date: 24 Jan. 2020

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Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.

How can we generate efficient routes for a large fleets of vehicles that have to make many thousands of deliveries a day while taking into account breaks, shift patterns and traffic conditions? Now let's make those vehicles electric and we need to take into account vehicle battery charge level, recharging station locations and anticipated energy efficiency. It's a challenging problem!
Vehicle Routing Problem (VRP) is the optimisation problem that describes all manifested delivery operations. It provides an optimal way of sorting deliveries onto multiple vehicles and providing each vehicle with an optimal sequence for delivery. The problem is NP-hard and suffers from a combinatorial explosion of solutions as the number of vehicles and deliveries increases.
We speak to Merve Keskin of the Warwick Business School about extending VRP as an area of optimisation to electric vehicles with a number of interesting developments:

• Keeping track of battery level en route and inserting possible stops at nearby compatible recharging points
• Allowing delay for possible queues at recharge points
• Potential for inflight negotiation on charge point booking so as to minimise contention for resource and wait times on long journeys.

With interview guest Dr. Merve Keskin, Warwick Business School and KTP fellow.
https://www.wbs.ac.uk/about/person/merve-keskin/
Further reading

• Merve Keskin's publication list (via Google Scholar)
• Paper: A matheuristic method for the electric vehicle routing problem with time windows and fast chargers (summary via ScienceDirect)
• Paper: Electric vehicle routing problem with time-dependent waiting times at recharging stations (pdf via researchgate)
• Paper: Partial recharge strategies for the electric vehicle routing problem with time windows (summary via ScienceDirect)
• Overview: A concise guide to existing and emerging vehicle routing problem variants (pdf via arXiv)

Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.
Recording date: 28 Feb. 2020
Interview date: 12 Feb. 2020

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Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.

The grey, green and yellow squares taking over social media in the last few weeks is an example of the fascinating field of study known as Game Theory. In this bite episode of DataCafé we talk casually about Wordle - the internet phenomenon currently challenging players to guess a new five letter word each day.
Six guesses inform players what letters they have gotten right and if they are in the right place. It’s a lovely example of the different ways people approach game strategy through their choice of guesses and ways to use the information presented within the game.
Wordles

• Wordle - the original
• Absurdle - it's Wordle but it fights you!
• Nerdle - Maths Wordle
• Quordle - when one Wordle is not enough!
• Foclach - Irish Wordle

Analysis

• Statistical analysis of hard-mode Wordle with Matlab by Matt Tearle (youtube)
• The science behind Wordle by Ido Frizler (medium.com)

Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.

Recording date: 15 Feb 2022

Intro music by Music 4 Video Library (Patreon supporter)

Send us a text

Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.