Presented by Dr. Anne Liret, Research Manager (British Telecommunications)

In the face of the climate emergency, several measures are being taken to move to more sustainable sources of energy. The transport sector has increasingly adopted Electric Vehicles (EVs) in order to cut down on greenhouse gas emissions. This has led to the necessity of accounting for these technologies in the area of field services delivery. In particular BT Research have been developing technical solutions to support the introduction of EV into their large fleet operations. This led to new models to plan for new EV chargers deployment minimising the energy risk, and then to schedule jobs and charge activities of electric vehicles in the feasible operational manner for field engineers while minimising the productivity impact.

By optimising the infrastructure, the required investment can be determined as well as minimising the disruption to the operations caused by the rollout of the new VE fleet and infrastructure can be minimised. As usual in operational research applied to decision-making support, the measuring of the impact on real-case trials is key to make sure changes adoption is going smoothly.

1 APRIL 2022 WEBINAR RECORDING

Presented by Alex Fleischer, Optimization Expert (IBM)

When trying to solve puzzles, practitioners train themselves on OR techniques (and other techniques). Puzzles are good ways for large and small companies to have OR practitioners from academia and consulting take a look at their specific issues (ROADEF / Euro challenges). Puzzles can support challenging students to show motivation and skills.

Why puzzles are very interesting with regards to equivalent computational challenges? We need to map real world concepts to mathematical concepts and that’s useful! They also tell a story, so they’re very easy to share and explain.

Kaggle challenges are part of the buzz around data science. During the presentation, I’ll mention other public challenges with examples: The IBM Ponder this challenge, the Decision Management challenge, and, not to forget, the “Comité International des Jeux Mathématiques”, and I will request the audience’s feedback with regard to deciding if challenges are good training paths for OR consulting and, also, if real-world business OR helps practitioners get better at challenge

4 MARCH 2022 WEBINAR RECORDING

Presented by Ugur Arikan (Operations Research Scientist, Deutsche Post DHL) and Jonas Witt (Operations Research Scientist, Deutsche Post DHL)

Traditional last-mile delivery planning purely based on optimization methods often lacks important real-life aspects and thus does not satisfy relevant operational requirements. Only experienced couriers have tacit knowledge about the delivery area and its customers, forcing them to deviate from planned routes. They know where to find good parking spots, when to best approach certain business customers, and which neighbors to approach at what times when the actual recipient is not at home. This tacit knowledge is almost impossible to collect and maintain, let alone to incorporate in optimization algorithms.

Thus, we at Deutsche Post DHL developed an algorithm following a different approach: We aim at implicitly learning about this tacit knowledge from historical tours and combine this with optimization algorithms to plan routes that an experienced courier would choose. In this talk we will present details of our algorithm, which incorporates machine learning, statistics, and optimization in a novel way. Furthermore, we show how it impacted last-mile delivery planning at Deutsche Post after its rollout across Germany.

Presented by Moulay Driss El Alaoui Faris, Energy Decision Scientist (Air Liquide)

Embedding OR functionalities in operational tools to address a large class of problems requires designing the software architecture to provide the required modularity.

In this talk we will present comparative design of component primitives (Nodes Arcs Boxes) in Python and Julia. A particular exploration question is how to use Julia for the design of object oriented software.

14 JANUARY 2022 WEBINAR RECORDING

Presented by Çağrı Doğuş Iyican (Trendyol Express) and Yahya Ertuğrul Geçkil (Trendyol Express)

Trendyol Express (TEX) is the sub-branch in Trendyol that leads daily cargo operations. As in other cargo cases, it needs depots for storing, sorting and transporting to meet customer demand and manage seller supplies. In this context, TEX has two kinds of depots for its operations which are sorting centers and cross-docks (x-docks). Sorting centers are responsible for taking packages from sellers and delivering these packages to x-docks whereas x-docks are responsible for delivering packages to customers. In a growing market, first mile and last mile demands in e-commerce forces planners to design an efficient network in order to optimize delivery times. This project aims to find the optimal locations for sorting centers and x-docks under several operational constraints.

Increasing e-commerce demand is hard to satisfy because of delivery bottlenecks. Cargo capacity growth ratio is far below e-commerce sales growth. Also, delivery costs have a major effect on expenses for e-commerce companies. Furthermore, delivery management is crucial for customer satisfaction levels. However, as well as customers, the number of sellers taking place in marketplaces is also increasing dramatically. Since it is not possible to find out best practice for tens of thousands of sellers manually, it is quite possible that resources are wasted, customers can not reach their orders in desired time and sellers deal with unnecessary costs. Besides, managers waste weeks only to find out a feasible plan. A mathematical model is developed for solving the dispatching problem for marketplace sales beside backlog amount. The model aims to distribute backlog and sales by minimizing the total delivery time.

Professor Gilberto Montibeller (Loughborough University, UK; University Southern California, USA),

Professor L. Alberto Franco (Loughborough University, UK; University del Pacifico, Peru)

Emerging health threats, such as the COVID-19 pandemic, create extensive health, economic and social problems. A key challenge for health experts and policy makers is deciding how to balance and reduce the risk of these threats.

In this applied research project, we developed an advanced framework to support the prioritisation of emerging health threats. The project won the prestigious EURO Excellence in Practice Award conferred by the Association of European Operational Research Societies, during the 31st EURO Conference on Operational Research held in Athens this year.

The research project achieved the following impacts in two global organisations:

(i) enhanced the quality of health experts’ policy recommendations at the UK Department for Environment, Food and Rural Affairs, and improved health risk control regulations;

(ii) informed the development of new international standards for the Food Standards Joint Programme of the Food and Agriculture Organization of the United Nations and the World Health Organization.

The contributions to Operational Research arising from the ongoing research programme are built upon three themes, which are focused on improving decision capability for the prioritisation of emerging health threats. These are tool development, process enhancement and competence building:

• Tool development encompassed the creation of rigorous decision analytic models to support decision making in the prioritisation of emerging health threats, as well as the design of risk management support tools that enable policy makers to use these models.

• Process enhancement focused on the redesign of decision processes to address the challenges posed by the prioritisation of emergent health threats, to enable the embedding of risk management support tools within organisational routines.

• Finally, competence building supported the development of health experts’ decision and risk analysis skills, together with the effective deployment of value-focused decision making and sound health risk management practices.

Health security is an important area of application for Operational Research and in this talk we will share our experiences in conducting these challenge and successful applied research projects.

5 NOVEMBER 2021 WEBINAR

Presented by Professor Goos Kant (Professor of Logistic Optimization, Tilburg University; Managing Partner, ORTEC)

Thanks to all great developments in mathematics and computing power, we are able to solve more complex problems within a reasonable amount of time. Machine learning and other AI-techniques can boost the power of OR. But are we also able to explain the outcome to the industry? Optimal is not always logical, which leads to challenges both in modeling and in implementation. In this presentation I like to share my thoughts, based on my professorship “Logistic Optimization” at Tilburg University and my long experience (in parallel) as “Optimization Evangelist” at ORTEC. ORTEC is a global and leading partner in data-driven decision support.

8 OCTOBER 2021 WEBINAR

Presented by Professor Carlo Mannino (Professor at the Department of Mathematics, University of Oslo; Senior Research Scientist, OSLO)

Trains running through railway networks are monitored and guided in real time by train dispatchers. They control train movements by taking various relevant decisions, such as where trains should pass and meet each other, when trains should arrive or depart from stations, which routes they must take, etc. These decisions become crucial when one or more trains are delayed, in order to reduce knock-on effects and quickly regain punctuality.

In the past decades a significant research effort has been devoted to developing optimization models and methods for (automatic) real-time train rescheduling (dispatching). However, such academic effort did not translate into a widespread use of such techniques in the practice of train dispatching. To the best of my knowledge, only a few fully or partially autonomous dispatching systems are or have been rolled out over the years to support dispatchers. The mathematics and data technology required for supporting the practice, at least to a certain extent, have been approaching maturity for at least 10-15 years, and are possibly fully mature now. So, what has impeded a more widespread development and deployment of such systems? In the talk, I will try to formulate an answer to this question and also discuss some recent developments and “gamechangers” which show that the situation is rapidly evolving.

I will start by describing the basic mathematical models and methods for representing and solving train (re-)scheduling problems. I will then describe a new system to dispatch trains in the greater Oslo area, an important portion of the Norwegian railway network which includes the large Oslo central station and all the adjacent lines. Although not in operation yet, the system has the highest level of readiness (TLR 9) and is currently undergoing on-field testing by professional dispatchers at Oslo Control Center.

After this I will quickly review the few, real-life dispatching systems which (to the best of my knowledge) are or have been in operation for supporting dispatchers in practice.

I will end the talk by analyzing which were the main hurdles for the penetration of optimization methods into the practice of dispatching and why things are changing now. This discussion may be of inspiration for other sectors with similar obstacles (such as, for instance, air-traffic control).

3 SEPTEMBER 2021 WEBINAR

Presented by Professor Christina Pagel (Director of the Clinical Operational Research Unit, University College London)

In May 2020, I joined the group Independent SAGE. At the time, I thought I was signing up to one or two public meetings on YouTube, but instead our profile grew and we discovered there was a large public appetite for more information about Covid and its spread in the UK. From the summer onwards, I have been giving regular updates on the latest Covid situation in the UK during Independent SAGE weekly briefings and have been invited regularly on the media fielding questions about various aspects of the Covid pandemic. In this talk, I will reflect on how my experience of working in Operational Research applied to health care has shaped how I have understood and communicated the Covid pandemic over the past year. This ranges from thinking through problem definition and unintended consequences, combining knowledge across different disciplines to deciding on where the information lies in the data and how best to present it.

9 JULY 2021 WEBINAR RECORDING

Presented by Professor Joaquim Gromicho (Professor of Business Analytics, University of Amsterdam; Science and Education Officer, ORTEC)

I will tell you some stories that introduce mathematical optimization in python. Each story describes a problem and the solution will be demonstrated using standalone Jupyter notebooks which can be made available on request. The domains addressed include:

Optimization problems with analytical solution

Nonlinear continuous optimization problems

Mixed integer linear optimization problems

Coping with data uncertainty using robust models

Scalable mixed integer convex optimization expressed using second order cones

The illustrations will use python packages that are solver agnostic, meaning that you can easily replace the solver when needed. This can be the case when you start with a free solver but discover that you need a commercial one: replacing the solver will require no recoding of the model.

4 June 2021 WEBINAR RECORDING

4 June 2021 webinar presentation relevant information and links

Speaker prof. dr. Joaquim Gromicho is science and education officer at www.ortec.com and professor of Business Analytics at www.uva.nl, see https://abs.uva.nl/content/news/2021/01/improving-the-world-through-analytics.html