EURO Excellence in Practice Award 2019 - Finalists

The six finalists of the 2019 EURO Excellence in Practice Award are:



Natalie Epstein, Industrial Engineering, Universidad de Chile
José Correa, Departmento de Ingenieria Industrial, Universidad de Chile
Rafael Epstein, Industrial Engineering, University of Chile
Juan Escobar, University of Chile
Ignacio Rios, Stanford University
Bastián Bahamondes,Universidad de Chile
Carlos Bonet, DRO, Columbia
Nicolás Aramayo, 11 plaza, Universidad de Chile
Martin Castillo, New York University
Andres Cristi, Universidad de Chile
Boris Epstein, Universidad de Chile


School Choice in Chile

Abstract

Centralized school admission mechanisms are an attractive way for improving social welfare and fairness in large educational systems. In this paper we report the design and implementation of the newly established school choice mechanism in Chile, where over 274,000 students applied to more than 6,400 schools.

The Chilean system presents unprecedented design challenges that make it unique. On the one hand, it is a simultaneous nationwide system, making it one of the largest school admission problems worldwide. On the other hand, the system runs at all school levels, from Pre-K to 12th grade, raising at least two issues of outmost importance; namely, the system needs to guarantee their current seat to students applying for a school change, and the system has to favor the assignment of siblings to the same school.

As in other systems around the world, we develop a model based on the celebrated Deferred Acceptance algorithm. The algorithm deals not only with the aforementioned issues, but also with further practical features such as soft-bounds and overlapping types. In this context we analyze new stability definitions, present the results of its implementation and conduct simulations showing the benefits of the innovations of the implemented system.

 

 

Rajeev Namboothiri, GE Global Research
Srinivas Bollapragada, General Electric Research
Babu Narayanan, General Electric Reseach
Guillaume Camard, GE Power
Ahmed Khattab, GE Power
 
Optimal Scheduling of Field Resources for Power Plant Outages


Abstract

GE Power installs and supports power generation equipment around the world. These power plants undergo periodic planned outages and forced unplanned outages to ensure safe and efficient operations. These outages require mobile field resources with specific skillset for a specific duration to perform maintenance tasks. A host of additional constraints restrict the eligibility and availability of a field resource to perform a maintenance task. GE Power Middle East Africa (MEA) centrally manages the deployment of field resources to power plant outages in more than 25 countries across MEA.

We developed a Field Resource Optimizer product to provide resource scheduling recommendations to minimize the total cost associated with field service operations while meeting all the real-world constraints associated with assigning field resources to outage tasks. The standard formulation of the field resource allocation problem results in a mixed integer linear program that is too large to be solved in a reasonable amount of time for practical use. Using constraint programming techniques and intelligently designing the cost function, we reduced the size of our math programming formulation.

The resulting math program, though much smaller in size, was still not practical for implementation. We therefore developed a novel solution procedure that iteratively solves a series of linear programs to achieve the optimal solution within a few minutes. GE Power has been using this tool in the MEA region since June 2016, resulting in operational cost savings of tens of millions of dollars.

 

Tugce Martagan, Eindhoven University of Technology
Yesim Koca, Industrial Engineering and Innovation Sciences, Eindhoven University of Technology
Ivo Adan, School of Industrial Engineering, Eindhoven University of Technology
Bram van Ravenstein, Operations Lead &ndash Associate Director, MSD Animal Health
Marc Baaijens, Global Operations, MSD Animal Health
Oscar Repping, Director Inactivated Vaccines, MSD Animal Health
 
 

Operational Research Improves Biomanufacturing Efficiency at Merk Sharp & Dohme

 Abstract

Biomanufacturing methods use live systems (e.g., bacteria, mammalian or insect cells) to manufacture the desired therapeutics. The use of live systems leads to several challenges, including batch-to-batch variability and uncertainty in the production yield and quality. In order to address these challenges, a multidisciplinary team of researchers from Merck Sharp and Dohme (MSD) Animal Health and Eindhoven University of Technology collaborated over three years and developed a portfolio of decision support tools. These tools consist of stochastic optimization and simulation models to reduce biomanufacturing costs and lead times. More specifically, the project builds a data-driven framework that can effectively integrate the business risks and process trade-offs along with the constraints posed by the biological and chemical features of the underlying processes. MSD Animal Health has successfully implemented and used these tools in daily practice, and achieved an average of 40% improvement in production yield (per batch) leading to an additional output in sales increase of €50 million per year. In addition, the project provided an improved use of historical production data and enabled a rigorous assessment of the operating policies and business risks.

 

 

Christian Weckenborg, Institute of Automotive Management and Industrial Production, Technische Universität Braunschweig
Karsten Kieckhäfer, Chair of Business, esp. Resouce Management, TU Bergakademie Freiberg
Thomas Spengler, Institute of Automotive Management and Industrial Production, Technische Universität Braunschweig
Patricia Bernstein, Post office box 011/1608, Volkswagen Group

The Volkswagen Pre-Production Center Applies OR to Optimize Capacity Scheduling 

 Abstract

Maximizing the utilization of resources is a frequently pursued optimization measure in industrial manufacturing. The planning task of capacity scheduling contributes to this objective by deciding on resource allocation and scheduling of orders. Since the Volkswagen Pre-Production Center (VPC), responsible for the entire prototype assembly of the Volkswagen brand, had been suffering from fluctuations of manufacturing personnel utilization at generally lower utilization levels we were asked to evaluate possible OR/MS solutions for their capacity scheduling problem. To this end, we developed a prototype for capacity scheduling based on binary integer programming. After the prototype had revealed high optimization potential, we developed a fully-fledged decision support system (DSS) for daily operation of capacity scheduling. It turned out that the schedules generated by the DSS were substantially better than the solutions generated by the current manual procedure, both in terms of resource utilization and planning effort. After successful test implementation and roll-out, the VPC estimates the annual cost savings to lie in the six-digit euro range. Meanwhile, we continue spreading OR/MS methods in neighboring departments of the VPC.

 

Janis Sebastian Neufeld, Industrial Management, TU Dresden
Kirsten Hoffmann, Lehrstuhl für BWL,insb. Industrielles Management, Technische Universtät Dresden
Martin Scheffer, Chair of Business Management, especially Industrial Management, TU Dresden
Udo Buscher, Industrial Management, TU Dresden

An Efficient Hybrid Column Generation Approach for Practical Railway Crew Scheduling with Attendance Rates

 Abstract

The crew scheduling problem with attendance rates is a highly relevant problem for regional passenger rail transport in Germany. Its major characteristic is that only a certain percentage of trains have to be covered by crew members or conductors, which increases complexity significantly. Even though this problem is commonly found in regional transport networks, still it has been discussed rarely in literature and no automated planning support exists in practice. We propose a novel hybrid column generation approach for a real-world problem in railway passenger transport. To the best of our knowledge for the first time several realistic requirements are integrated that are necessary for a successful application of generated schedules in practice. While an extended mixed integer programming model is used to solve the master problem, a genetic algorithm is applied for the pricing problem. Several improvement strategies are applied to speed up the solution process. These are analyzed in detail and exemplified. The effectiveness of the proposed improved algorithm is proven by a comprehensive computational study using real-world instances. The developed approach is successfully used in practice at a large German railway company since 2017. 

 

Yves Lucet, Computer Science, University of British Columbia
Warren Hare, University of British Columbia


Towards Optimal Alignment Design for Road Construction – Using Optimization to Design Safe Roads at Minimal Cost

Abstract

Efficient transportation networks are critical to any economy. Optimizing a road design is a prime application for operational research since saving a few percent may add up to millions even on a single road.

Optimization of road design splits the problem into several nested stages. First, promising corridors are selected before a satellite view of the road is traced in each corridor (horizontal alignment problem). Next, cusp and crest are altered to obtain a road design that is safe to drive (vertical alignment problem), and finally materials are optimally moved to their best location (earthwork problem). In this talk, we overview modern techniques and recent advancements in optimization for road design.

In particular, we present an integrated model that captures both the vertical alignment and earthwork problem, and computes a globally optimal solution. The cost savings are validated on a representative set of 40-60 roads, with average improvements of 20-30%. The model achieves the required precision by considering side-slopes, multiple materials and various equipment, while keeping the computation time in an acceptable range. The result saves civil engineers significant design times and produce a solution that no human expertise can surpass.

 



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