About the event

On January 30, 2025, the Workshop on Innovative Applications of Operations Research will take place at the Department of Production Engineering, UFSCar, São Carlos Campus. This event is organized by the Operations Research Group - UFSCar. The event will feature talks by researchers who are experts in the field, including Prof. Maria Battarra, Ph.D, (University of Bath, United Kingdom) Prof. Luciano C. A. da Costa, Ph.D, (Federal University of Paraíba, Paraíba) and Prof. Anand Subramanian, Ph.D, (Federal University of Paraíba, Paraíba).

The event showcases innovative applications of mathematical optimization to solve complex real-world problems, including preference modeling, disaster management, and industrial operations. Topics include scalable algorithms for handling large datasets, optimization strategies for resource coordination under operational constraints, and methods to enhance production efficiency while minimizing disruptions. These innovations demonstrate the impact of computational techniques on practical decision-making and performance improvement across diverse domains.

Programme overview

09:00 - 09:10	Opening session
09:10 - 10:20	The wildfire suppression problem with coordination of multiple types of resources Prof. Maria Battarra, Ph.D  University of Bath, United Kingdom     Abstract: The frequency and impact of wildfires have considerably increased in the past decade, due to extreme weather conditions and a growing population density. Wildfires are endangering forests, dwellings and the population. Due to global warming, their disastrous effects on both the environment and the economy call for plans of prevention, preparedness and response, even in countries in which wildfires were previously not a major concern. This study was motivated by informative discussions with members of the wildfire directorate in a province of Turkey, Natural Resources Wales, the UK Forestry Commission, and Forestry England. We introduce, model, and solve a wildfire suppression problem that involves the coordination of various types of fire-suppression resources, as we realised the literature models all types of firefighting resources (i.e., fire engines, helicopters and razing teams) as generic resources, ignoring their different operational constraints and effects in extinguishing or controlling the spread of a wildfire. Two integer programming formulations for the problem are presented, the performances of which are evaluated on a set of randomly generated problem instances. Realistic operational constraints about the different firefighting resources are embedded, and the fire spread is simulated depending on the wind conditions. Dwellings, infrastructures and areas of naturalistic beauty are prioritised for protection. The results indicate that the proposed formulations are able to obtain high quality upper and lower bounds, and solve instances of realistic size. Extensive numerical experiments are performed to analyse the effects of several operational constraints on the computational performance of the models. The importance of the right mix of resources employed in tackling a wildfire as well as their coordination are highlighted. A case study inspired by the Yatagan district of the Mugla province of Turkiye has been developed, and maps simulating the fire spread over time will be showcased. Biography: Maria Battarra is a Professor in Operations Research at the School of Management. She studied Industrial Engineering and her PhD is in Operations Research from the University of Bologna. Her research focuses on solving large scale optimisation problem arising both from industrial applications, like vehicle routing and scheduling, transportation and humanitarian logistics. Maria has been teaching in many departments (Industrial and Computer Engineering, Mathematics and Management), in many countries (Italy, Turkey and UK) and at all levels of education (from BSc to PhD and MBA level). She has been closely interacting with the industry and she enjoys finding the best possible solution methods which suits the needs of the relevant application. The methodologies she most commonly deploys are exact integer and mixed integer linear programming models, and metaheuristic algorithms.
10:20 - 10:50	Coffee break
10:50 - 12:00	Column generation applied to the estimation of non-parametric discrete-choice models Prof. Luciano C. A. da Costa, Ph.D  Universidade Federal da Paraíba - UFPB, Paraíba       Abstract: Discrete choice models (DCMs) provide probabilities for individuals choosing a certain alternative when faced with a set of limited options. DCMs can be parametric or non-parametric. Parametric models are easier to estimate but require assumptions about individuals' preferences, while non-parametric models rely solely on training data without any assumptions. Ranked-list methods are popular non-parametric models and capture individuals’ behavior by associating them with preference lists of options sorted in decreasing order of preference. Individuals are assumed always to choose the option best placed in their preference list when confronted with an alternative. Despite the generality and simplicity of ranked-list methods, a major drawback is the exponential increase in the number of potential lists. Column generation (CG) can be employed to address this issue, with the CG subproblem modeled as a generalized linear ordering problem (GLOP). In this talk, we present a dynamic programming algorithm to solve GLOPs. The proposed method is generic and capable of handling different settings without requiring drastic changes to its implementation. The proposed algorithm outperforms a previously proposed Branch-and-Cut algorithm. Our algorithm efficiently generates preference lists when incorporated into maximum likelihood and minimum L1 estimators. The algorithm performs well when facing instances with numerous observations, which is crucial as non-parametric choice models heavily rely on data volume for accurate estimations. This is joint work with Claudio Contardo (Concordia University), Gerardo Berbeglia (Melbourne Business School, The University of Melbourne), and Jean-François Cordeau (HEC Montréal). Biography: Luciano Costa is an Assistent Professor at the Universidade Federal da Paraíba (UFPB), Brazil. He holds a B.Sc. in Mechanical Production from UFPB (2013) and an M.Sc. in Production Engineering from the same institution. In 2020, he earned his Ph.D. in Applied Mathematics from Polytechnique Montréal, Canada. His research expertise includes Combinatorial Optimization, Exact Algorithms based on Cut and Column Generation techniques, Metaheuristics, Multiobjective Optimization, Discrete Choice Modeling, Vehicle Routing and its variants, Production Machine Scheduling, Lot Sizing Problems, Personnel Assignment, Humanitarian Logistics, Optimization in the Management of Academic Resources, and Resource Optimization in Health Care Services.
Lunch break
14:00 - 15:10	The maximum length car sequencing problem Prof. Anand Subramanian, Ph.D  Universidade Federal da Paraíba - UFPB       Abstract: This study introduces a new variant of the car sequencing problem to support the operations of a Brazilian automotive assembly plant. We propose an integer linear programming (ILP) formulation to schedule the maximum number of cars without violating the spacing constraints associated with car options, such as air-conditioning or sunroof. In the studied plant, these violations lead to a complete stop of the assembly line, which is the least desirable outcome for the company. We also present valid combinatorial lower and upper bounds, as well as binary and iterative search algorithms to solve the problem when good primal bounds are not readily available. In addition, we develop an effective iterated local search algorithm to quickly obtain high-quality solutions, then used to warm start the exact methods. Computational results demonstrate that relatively low gaps are achieved for benchmark instances within a time limit of ten minutes, and we conduct an instance space analysis to identify the features that make the problem more difficult to solve. Moreover, the instances reflecting the company's needs are solved to optimality in less than a second. Finally, simulations with real-world demands, divided into shifts, are performed for a period of four months. In this case, we use the proposed ILP model in all shifts except the last one of each month, for which we employ an alternative ILP model to sequence the unscheduled cars, adjusting the pace of the assembly line. Biography: Anand Subramanian is an Associate Professor at Universidade Federal da Paraíba (UFPB), Brazil. He received his B.Sc. degree in Mechanical Production Engineering from UFPB in 2006 and his M.Sc. degree in Production Engineering in 2008 from the same institution. He obtained his doctorate in Computing in 2012 from the Universidade Federal Fluminense (UFF), Brazil. His doctoral thesis received the Honorable Mention Award from the Brazilian Ministry of Education, and it was selected among the top 6 thesis (presented in 2012) by the Brazilian Computing Society. His research interests are mainly related to developing heuristic, exact and hybrid algorithms for combinatorial optimization problems. He is the head of of the Logistics and Optimization Group (LOG) at UFPB and is an author of 65 articles published in prestigious international journals. In 2016 he received the highly cited research award from Elsevier for the paper "A hybrid algorithm for a class of vehicle routing problmes" published in Computers & Operations Research (C&OR). In addition, he supervised the works that won the prize for the best undergraduate paper at the Brazilian OR Conference in 2015 and 2021. He also supervised on of the finalist works for the INFORMS Undergraduate OR Prize in 2024. Moreover, he has been a member of the Editorial Board of C&OR since 2019, and coordinates or has coordinated 11 research projects with public or private funding. Anand is the organizer and host of the "Subject to" podcast.
15:10 - 16:00	Ongoing researches by the Operations Research Group - UFSCar
16:00 - 16:30	Coffee break
16:30 - 18:00	Ongoing researches by the Operations Research Group - UFSCar

Venue

The event will be held in person, with all activities taking place at the CCET AUDITORIUM (Center of Exact Sciences and Technology) at UFSCar.

Registration

Participation is open to all interested. (Link to forms).

Organizing committee

• Prof. Maria Battarra, Ph.D (University of Bath, United Kingdom)
  
• Prof. Pedro Munari, Ph.D (UFSCar, São Carlos)
  
• Alex Paranahyba de Abreu (UFSCar, São Carlos)
  

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