EURO Excellence in Practice Award 2016 - Finalists

The six finalists of the 2016 EURO Excellence in Practice Award are:



Kerem Akartunali, Management Science, University of Strathclyde
Diclehan Tezcaner Ozturk, Dept. of Industrial Engineering, TED University
Evangelos Boulougouris, Dept. of Naval Architecture, Ocean & Marine Engineering,
University of Strathclyde
Sandy Day, Dept. of Naval Architecture, Ocean & Marine Engineering, University of Strathclyde

A Novel Framework of Simulation and Optimisation for Offshore Wind Farm Installation Logistics at SSE and SPR

Abstract

The development of an offshore wind farm involves a relatively complex sequence of activities in order to install electrical cabling, offshore electrical systems, turbine foundations, masts and turbine generators, typically spread through multiple years with budgets often significantly exceeding £100 million for each project. Complexities arise from various aspects including range of vessel types, their capabilities and availabilities, significant uncertainties apparent in weather and costs, and operational limitations and requirements. Motivated by an almost non-existent literature in the area and the urgent need expressed by our industrial partners Scottish Power Renewables and Scottish Southern Energy to improve current installation logistics operations and realize substantial cost savings, we started our project with an extensive period of data collection and data analysis, followed by an iterative process of model building in close collaboration with engineers and project managers. We have designed and developed several stand-alone and integrated simulation and optimisation modules in order to address the needs of our partners, where novel approaches exploiting Monte Carlo simulation, rolling horizon scheduling, and robust optimization were required to solve challenging massive-size industrial problems. All models were implemented in a multi-stage validation and verification process that resulted in custom-built software ready to use by the end users and decision makers.

 

Christian Artigues, LAAS, CNRS
Emmanuel Hebrard, Groupe MOGISA, CNRS-LAAS
Pierre Lopez, ROC, LAAS-CNRS
Gilles Simonin, Computer sciences, Insight Centre for Data Analytics UCC
 
Scheduling Scientific Experiments for Comet Exploration on the Rosetta/Philae Mission


Abstract

On November 12th 2014, the robot-lab Philae was released from the spacecraft Rosetta and landed on the ground of the comet 67P/Churyumov-Gerasimenko. Philae is fitted with ten instruments to conduct the experiments elaborated by as many research teams across Europe. These experiments, should they be imaging, sampling or other types of signal analysis, correspond to sequences of activities constrained by two extremely scarce resources: the energy supplied by a single battery, and the storage memory of its CPU.
The CNES, the French space agency that was in charge of designing the plans executed by Philae, acquired to that purpose from an industrial subcontractor a toolkit called MOST (Mission Operation Scheduling Tool). This toolkit modeled the problem of scheduling Philae’s experiments as a complex Resource-constrained project scheduling problem using a commercial software embedding dedicated scheduling, constraint programming and operations research techniques. Limitations of this first version were identified as the solution procedure was way too slow for large scale scenarios.
We thus present our contributions to solving the problem of scheduling Philae's activities. In particular, we focus on the design of polynomial-time complexity algorithms for efficiently reasoning about data transfers within Philae and between Philae and Rosetta. These algorithms made it possible to solve in a few seconds long term sequences of activities that otherwise required hours with the previous approach, or in some case could not be solved. Moreover, they also give a more accurate prediction of the memory usage, thus giving better guarantees against data loss.
Moreover, as Philae bounced right after touch down due to a malfunction of its harpoons, recourse schedules had to be rebuilt in a nearly real-time basis, which was made possible by the reactivity of the new algorithms. Despite this unexpected event, most of the experiments could be carried out and allowed to obtain significant scientific discoveries.

 

Jens Brunner, University of Augsburg
Andreas Fügener, Universität Augsburg
Armin Podtschaske, Klinik für Anästhesiologie, Klinikum Rechts der Isar (MRI)

Duty and Workstation Rostering Considering Preferences and Fairness: A Case Study at a Department of Anaesthesiology

 Abstract

This research addresses a personnel scheduling problem at hospitals. We present two mixed integer linear programming models – a duty-roster and a workstation-roster model. The duty-roster model determines the assignment of physicians to 24h- and late-duties whereas the workstations-roster model assigns physicians to actual workstations as operating rooms. The former serves as an input for the latter. In both models we maximize the number of assignments subject to labor regulations and internal department specific scheduling rules. Furthermore, we consider experience levels and qualifications in our models. To promote for job satisfaction we take into account fairness aspects as well as individual physician preferences. We implemented both models at a large German university hospital for the department of anesthesiology with approximately 150 physicians. We could demonstrate the superior quality compared to manual scheduling previously in use at our cooperation hospital with regards to granted requests, shortage of coverage, and fair distribution of weekend duties.

 

 

Thorsten Koch, Optimization, Zuze Institue Berlin
Benjamin Hiller, Optimization, Zuse Institute Berlin
Marc Pfetsch, Discrete Optimization, Technische Universität Darmstadt
Lars Schewe, Mathematics, FAU Erlangen-Nürnberg, Discrete Optimization

Evaluating Gas Network Capacities

 Abstract

In 2009 Open Grid Europe (OGE, at that time E.ON Gastransport) Germanys largest Transmission System Operator (TSO) initiated the ForNe project (in German “Forschungskooperation Netzwerkoptimierung”) due to the new challenging problems resulting from the liberalization of the gas market. Over decades, gas transport had been more or less steady state with long-term delivery contracts and weather forecast predictable demands. Now, demand and supply change on a daily basis and are triggered by market prices, not by security of supply. Nevertheless, a TSO like OGE has to guarantee the latter, having only little influence on the trading process. These facts result in enormously challenging problems simultaneously including uncertainties, dynamical aspects, and feasibility questions. Mathematically, these problems lead to stochastic mixed-integer non-linear non-convex optimization problems including PDE and ODE constraints.
The research part of the project run until 2015 and and it was necessary to bring together expertise in mixed-integer programming, non-linear programming, mixed-integer non-linear programming, stochastics, simulation, gas physics, network planning, law and regulations. The project involved about 40 people from OGE, five universities, and two research institutes. The whole team developed new mathematical models and methods, provided new theoretical insights into this kind of problems that lead to a completely new methodology for validating booked capacities, a task at the core of OGE's business operations. This cutting edge research was turned into a software system, bringing it directly into the workplace of the company. This software is now maintained and further developed by two university spin-offs working closely together with OGE's IT-department.
To the best of our knowledge, the ForNe project was for the first time able to solve real-world mixed-integer non-linear non-convex optimization problems with tens of thousands of binary and continuous variables. This is a break-through in computational mixed-integer non-linear programming and will have influence on many other areas where OR and engineering aspects come together. The results of this research project are documented in the book “Evaluating Gas Network Capacities” published in 2015 in the SIAM-MOS Series on Optimization, in nine PhD theses and several publications. 

 

Jannik Matuschke, TUM School of Management, Technische Universität München
Tobias Harks, Institut für Mathematik, Universität Augsburg
Felix G. König, TomTom International B.V.
Alexander Richter, Institut für Mathematik, Technische Universität Berlin
Jens Schulz, Institut für Mathematik, Technische Universität Berlin

An Integrated Approach to Tactical Transportation Planning

 Abstract

Logistics costs constitute a major cost driver in today's economy and efficient planning of transportation processes is an important necessity for companies of all sizes and industries. We introduce a new model for tactical planning of freight transportation, capturing all important aspects of this logistical task: the routes of commodities within the network, the corresponding tariff choices as well as delivery frequencies and inventory levels. These different decisions are integrated into a unified capacitated network design formulation using a cyclic network expansion and a set of graph-based gadgets for realistically modelling the different classes of transportation tariffs occurring in practice. We complement our model by providing various algorithmic methods for solving the resulting optimization problem, most notably a local search procedure based on flow decomposition and an aggregated mixed integer programming formulation.
These results are the outcome of a joint research project of TU Berlin and 4flow AG, a leading provider of supply chain consulting, software, and fourth-party logistics services. Throughout development, the model and the algorithms have been constantly evaluated on a broad set of instances obtained from recent and ongoing customer projects of 4flow AG. In a case study, a 14% decrease in logistics cost was achieved compared to previous optimization approaches. Our algorithmic toolkit has now been integrated into the standard software for logistics planning 4flow vista.

 

Karin Thörnblad, Logistics Development, GKN Aerospace Engine Systems Sweden

Using Mathematical Optimization for Scheduling Heat Treatment Production

 Abstract

During my studies for PhD, I developed an iterative scheduling procedure for the scheduling of a real flexible job shop, the so-called multitask cell at GKN Aerospace Engine Systems in Sweden. A time-indexed mathematical optimization formulation of the problem is repeatedly solved with increasing accuracy using smaller and smaller length of the time steps. To my knowledge it was the first time-indexed model formulated for a flexible job shop, and the first mathematical optimization model to include side constraints regarding preventive maintenance, fixture availability, and unmanned night shifts. After my dissertation, I was given the opportunity to further develop the scheduling model in order to adjust it to the planning situation of the heat treatment department at GKN Aerospace Engine Systems. The difficulties that were overcome during implementation and a first analysis of the impact of the first quarter of usage of the scheduling procedure in comparison with the corresponding quarter the previous year are presented. The main result from the analysis is that the utilization rate of the heat treatment department increased significantly between the two evaluated quarters.

 

 



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