EURO Excellence in Practice Award 2021 - Finalists

The six finalists of the 2021 EURO Excellence in Practice Award are:



Navonil Mustafee, Business School, University of Exeter
John Powell, Business School, University of Exeter


NHSquicker: Shaping Demand for Urgent Care through Real-Time Data and Digital Nudges


Abstract

Our work aims to investigate if indirect suggestions (nudges) can support patients in need of urgent care to make more informed decisions about available healthcare choices. Our nudge is digital in nature and is delivered through the NHSquicker app. The app informs patients of alternative facilities for care that are located at a catchment-level (e.g., hospitals with A & E departments, urgent care centres [UCC] and minor injury units [MIU] that are part of the urgent care network). By combining the current wait time with travel time, the app directs users towards facilities where they may be seen quicker, so that patients can choose the appropriate type of treatment facility for their condition. Thus, beneficially, only those with more serious needs present at the A & E, thereby reducing overall demand on the A & E facilities by redirecting less serious cases to the more appropriate facilities of MIUs/UCCs. A & E waiting times are thereby reduced. It also helps to shape demand across the urgent care network by encouraging patients to choose an appropriate destination and optimal time of visit. NHSquicker is currently live in all of Devon and Cornwall and covers parts of Somerset and Bristol. It receives real-time data from 27 centres for urgent care, including nine emergency departments.

In this talk, we present the context and motivation for this work, the process of co-development of the solution with several NHS Trusts in the South West of England (as part of the Health and Care IMPACT Network), the data standard and the IT architecture, the initial rollout and deployment and the evidence of impact.



Andreas Klinkert, Institute of Data Analysis and Process Design (IDP), Zurich University of Applied Sciences (ZHAW)
Peter Fusek, Institute of Data Analysis and Process Design, Zurich University of Applied Sciences
Bruno Riesen, Vice President Business Support, Swissport International Ltd.
Roman Berner, Optimization Specialist, Swissport International Ltd.


Airport Staff Scheduling at Swissport International: 14 Years of Collaboration in Business Optimization


Abstract

Swissport International Ltd. is the largest ground handling company worldwide, providing services for 82 million passengers and 4.1 million tons of cargo a year, with a workforce of 45,000 personnel at 269 airports. Swissport employs at its main airports up to 2500 people with hundreds of different work skills and shift duties, and a multitude of contract types. Monthly staff planning is highly complex and expensive, and usually requires extensive manual work by specially trained planners.

In 2007, Swissport launched a strategic R&D collaboration with the Zurich University of Applied Sciences (ZHAW), Institute of Data Analysis and Process Design (IDP), with the aim of developing innovative software for solving its challenging staff rostering problems. Evaluation of the commercially available tools showed that no software was able to satisfactorily solve the complex large-scale planning problems at Swissport.

Staff scheduling and rostering involves a number of hierarchical subproblems including demand modeling, task generation, shift design, days-off scheduling, shift assignment and real-time dispatching. When solving highly constrained large-scale workforce planning problems it is usually not computationally practical to deal simultaneously with all these tasks. Real-world software solutions typically decompose the overall planning task into heuristically designed subproblems which then are tackled by a variety of suitable exact and heuristic methods.

This presentation focuses mainly on the central rostering phase, which is the most complex, expensive and sensitive planning task at Swissport. The enormous effort involved can be illustrated by the example of Zurich Airport, where the initial rostering process with 20 planners took around 400 working days per month.

During a long-term strategic collaboration, a high-performance software for automated staff scheduling has been developed, which is able to efficiently solve Swissport's complex rostering problems. The methodology comprises a broad range of optimization techniques including preprocessing, decomposition, projection, and relaxation approaches, mixed-integer programming models, infeasibility analysis, and various heuristic procedures. Developing 'good' MIP formulations to reduce solver computation times was one of the most challenging parts and required substantial insights from combinatorial optimization, polyhedral combinatorics, and graph theory. The project was several times close to failing due to intractable MIP models and could only be continued thanks to mathematical breakthroughs leading to powerful new MIP formulations.

The developed software is fully implemented and in operational use at all major airports in Switzerland, including more than 55 internal customers, and its roll-out is continuously being expanded to other stations. Bottom line benefits include faster and more robust planning processes, improved roster quality and fairness, and significant financial savings.



Leonard Heilig, Institute of Information Systems, University of Hamburg
Eduardo Lalla-Ruiz, Faculty of Behavioural, Management and Social Sciences, University of Twente
Stefan Voss, Wirtschaftsinformatik/Information Systems, University of Hamburg


Intelligent Truck Drayage Dispatching and Appointment Booking: A real-world application in the Port of Hamburg (Germany)


Abstract

To be confirmed



Alessio Trivella, Institute for Transport Planning and Systems, ETH Zurich
Mikele Gajda, Department of Operations, UNIL | Université de Lausanne
Paolo Giannotti, Supernova Hub
Renata Mansini, Department of Information Engineering, University of Brescia
David Pisinger, Management Engineering, DTU


Sustainable and efficient logistics: How optimization transformed Italmondo’s cargo loading operations


Abstract

Logistics companies around the world face increasing pressure, exacerbated by the Covid-19 pandemic, to handle larger volumes of freight even faster. At the same time, growing environmental concerns push these companies to meet climate targets by reducing a typically high carbon footprint. Therefore, pursuing strategies to operate more efficiently and sustainably is a must in modern logistics.

In this project, we have successfully developed Operations Research methods at Italmondo Spa, a multinational logistics and transportation company that loads and ships hundreds of trucks every day. We have tackled the key operational process of loading a truck fleet, previously performed manually, by solving a complex variant of the container loading problem, which: (i) is multi-objective, (ii) jointly complies with several practical constraints, some of which only sparsely studied in the literature, and (iii) is subject to strict industry standards requiring large-scale heterogeneous instances to be solved in seconds. Given these challenges, we developed a multi-phase tailored randomized constructive heuristic. In a computational study involving real-life instances, our algorithm is benchmarked against dual bounds, the company’s internal solutions, and commercial software, providing loading solutions of significantly higher quality in a few seconds while handling more practical constraints.

Besides improving efficiency of loading operations and increasing transport safety, the project allows potential annual savings estimated at around one million Euros and one thousand tons of CO2. It also paved the way for further scientific research and contributed to establish an “analytics-based culture” at the company, that now plans to initiate new optimization projects and academic collaborations.



Gilberto Montibeller, Management Science and Operations Group, Loughborough University
L. Alberto Franco, School of Business and Economics, Loughborough University


Improving Global Risk Management of Emerging Health Threats with Facilitated Decision Analysis


Abstract

Emerging health threats, such as the Coronavirus global 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. This research project on facilitated decision analysis for emerging health threats underpinned the development and implementation of innovative decision models and enhanced decision processes in two global organisations. The use of these operational research models and processes by health experts and policymakers achieved the following impacts: (i) enhanced the quality of health experts’ recommendations to the UK Department for Environment, Food and Rural Affairs leadership in the prioritisation of animal and human emerging health threats; and, (ii) informed new international standards for the Food Standards joint programmes of the Food and Agriculture Organization of the United Nations and the World Health Organization.



Sven Müller, Chair of Operations Management, Otto-von-Guericke-University Magdeburg
Knut Haase, Institut f. Verkehrswirtschaft, Lehrstuhl BWL, insb. Verkehr, Universität Hamburg
Mathias Kasper, TU Dresden
Matthes Koch, Institut für Verkehrswirtschaft, Universität Hamburg


A Pilgrim Scheduling Approach to Increase Safety During the Hajj


Abstract

The Hajj — the great pilgrimage to Mecca, Saudi Arabia — is one of the five pillars of Islam. Up to four million pilgrims perform the Hajj rituals every year. This makes it one of the largest pedestrian problems in the world. Ramy al-Jamarat — the symbolic stoning of the devil — is known to be a particularly crowded ritual. Up until 2006, it was repeatedly overshadowed by severe crowd disasters. To avoid such disasters, Saudi authorities initiated a comprehensive crowd management program. A novel contribution to these efforts was the development of an optimized schedule for the pilgrims performing the stoning ritual. A pilgrim schedule prescribes specific routes and time slots for all registered pilgrim groups. Together, the assigned routes strictly enforce one-way flows toward and from the ritual site. In this paper, we introduce a model and a solution approach to the Pilgrim Scheduling Problem. Our multistage procedure first spatially smooths the utilization of infrastructure capacity to avoid dangerous pedestrian densities in the network. In the next optimization step, it minimizes overall dissatisfaction with the scheduled time slots. We solve the Pilgrim Scheduling Problem by a fix-and-optimize heuristic, and subsequently simulate the results to identify necessary modifications of the scheduling constraints. Our numerical study shows that the approach solves instances with more than 2.3 million variables in less than 10 minutes on average. At the same time, the gap between optimal solution and upper bound never exceeds 0.28%. The scheduling approach was an integral part of the Hajj planning process in 2007–2014 and 2016–2017. No crowd disaster occurred in these years. Our approach was not applied in 2015, when a severe crowd crush happened close to the ritual site. We briefly discuss possible causes and consequences of this accident.



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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 International License and the GNU Free Documentation License (unversioned, with no invariant sections, front-cover texts, or back-cover texts).

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