SKEMA Centre for Analytics and Management Science
Researchers advance the state-of-the-art to help managers improve the performance of their firms using analytics, data science, mathematical modelling, operations research, project management, and computational science.
Researchers advance the state-of-the-art to help managers improve the performance of their firms using analytics, data science, mathematical modelling, operations research, project management, and computational science. Researchers' mission is also to educate students and to assist faculty members in understanding and applying advanced techniques in management of organizations related to functional areas of business, such as accounting, finance, marketing, law, and operations.
Research themes
Blockchain usage in supply chains to improve security, traceability, trust, and coordination between chain partners and the consumers.
Green supply chains : reducing the carbon footprint of logistic networks, circular and reverse chains.
Hospital and emergency room scheduling: improving the efficient usage of costly resources under stochastic demand.
Scheduling in projects of scarce or costly assets under availability constraints.
Optimization under constraints and various goals in stochastic environments.
Explaining and using Machine Learning to increase managerial control.
Mathematical Economics and Quantitative Finance with particular attention to economic growth modelling, sustainable growth, and portfolio management.
Mathematical Imaging with a strong focus on feature extraction, image classification, image denoising.
Léonie’s PhD focuses on project scheduling. Her first chapter is dedicated to the project scheduling with one resource problem. She is using exact methods more specifically integer linear programming. She holds a MSc in Optimization and Operations Research from University of Toulouse III, Paul Sabatier and is an Engineer in Big Data and Distributed Systems from INSA Toulouse.
Diego is a PhD candidate in the joint program between SKEMA Business School and KU Leuven. He is also an electrical engineer from Universidad Técnica Federico Santa María, Valparaíso, Chile, with an MSc. in Electrical Engineering from the same university. His current research areas involve optimization under uncertainty, combinatorial optimization, and bilevel optimization. Besides, he worked as a consultant engineer in Chile, developing several studies related to the technical-economical planning and operation of electric power systems.
Gabriela Pinto is a PhD Student in the joint program by SKEMA Business School and FEB – KU Leuven, working under the supervision of professors Aida Jebali and Erik Demeulemeester in the field of Operations Research applied to Health Services. Her research focuses on Operating Room Planning and Scheduling.
Gabriela holds a Master of Science degree in Operations Research from Columbia University and a Bachelors and Professional degree in Industrial Engineering degree from Universidad Adolfo Ibáñez, Chile.
Prior to joining SKEMA, she worked as a Research Engineer for the project “Operations Research in Energy and Climate Change” (ORECC) at Universidad Adolfo Ibáñez and funded by the Chilean National Agency for Research and Development (ANID). Her earlier experience includes consulting and research, mainly applying Operations Research and Data Analytics tools to problems related to the energy and transportation industries. Gabriela also has teaching experience, both as lecturer and teaching assistant for Operations Research at Universidad Adolfo Ibáñez.
Her interests include mathematical modeling, design and analysis of algorithms and applications of Operations Research.
-
Explainable Artificial Intelligence (XAI) is one of the most effervescent areas of research in Artificial Intelligence (AI) and Machine Learning (ML), which is due to the fact that in general the internal mechanisms of AI and ML systems are difficult to understand and their outputs are difficult to explain. Stakeholders expect some sort of explanation when decisions or results from an AI system affect them. Furthermore, legislation is being introduced, demanding for more Ethical AI systems. A five-year research project, funded in 2023 by NSERC-GD (Canada), proposes different approaches to explaining how AI arrives at a conclusion, namely applications of concepts and techniques related to causality or the introduction and analysis of score-based explanations that value the various attributes in a study to reflect their relevance. Causality is beginning to play an important role in machine learning, as the correlations implicit in the data may not be sufficient to build accurate, generalizable, and robust AI models.
-
Blockchain technology (BCT) has emerged as an enabling technology that can provide traceability, provenance and transparency in business operations, across complex global supply chain ecosystems, where leanness, agility, and speed are crucial, in addition to achieving social sustainability. It is considered one of the most disruptive technologies representing decentralised environment for transactions, self-executing digital contracts (smart contracts) and intelligent asset management over the Internet, providing a single-view to the entities (users) involved in the transaction. Therefore, the key characteristics of BCT will significantly impact the organisational governance, supply-chain relationships, operations strategy, digital transformation pathway and existing supply-chain business models. BCT when integrated with other technologies such as the Internet of Things, big data analytics, and artificial intelligence, will help to increase the efficiency of supply chain through agile data-driven decision-making based on high quality data (stored in Blockchain) and further facilitating supply chain transparency that will also afford product traceability, authenticity and legitimacy, and enhance sub-supplier transparency that will alleviate social sustainability problem in multi-tier supply networks.
-
Mobility and logistics systems are vital for societies, and their performance is of primordial importance to the economy. Nevertheless, transportation networks in major cities are frequently congested and disrupted, and the forecasted growth of the worldwide urban population poses a natural threat to the welfare of mobility and logistics systems. With the emergence of on-demand services, user demands for travel and goods are evolving rapidly with substantial economic consequences for society. This is reinforced by environmental challenges: the prospect of climate change poses greater pressure on the need to develop more sustainable, low-emission and resilient mobility and logistics ecosystems . To address these challenges, this project takes a stochastic optimization perspective wherein perturbations and disruptions in mobility and logistics systems are modeled as sources of uncertainties. The goal of this project is to conceive, design and develop novel methodologies for sustainable on-demand service planning and operations.
-
A pandemic can wreak havoc in supply chains, as witnessed in the COVID-19 context. As workers get infected, production level drops and demand from customers goes unfulfilled. Combining in a novel way an epidemic model with optimal control theory, various models provide a plant manager with the optimal level of prophylactic effort she needs to deploy over a planning horizon to protect the workforce from a pandemic in its early stage and so maintain production levels.
-
The research aims to delineate changing priorities in the competencies currently recognized by the project management bodies of knowledge and to explore emerging project manager role challenges that require new or adapted competencies fueled by the demands of modern day social, technological and ecological changes in society. We take stock of the roles and competencies required of project professionals in the light of new and recent trends:
- The need to work under greater uncertainties than ever before.
- The need to be able to adapt at speed with agility and responsiveness.
- The need to innovate new working methods, products and services with a sustainable and ethical mindset.
- A growing need to learn effectively.
- A willingness to engage in transformation everywhere aided by digital and AI technologies.
- A growing need to be able to self-manage and maintain wellbeing.
Our research questions are framed in the above “post-COVID" contexts and take advantage of existing work done at the authors' institution in applying Theory U concepts to early career project managers in a higher education setting. We adapt this theoretical framework to the context of early career project managers in a higher education setting through 4 principal steps: intention setting, observation phase, presencing phase, crystallizing vision and enacting.
-
The members of the Centre will develop new business models for logistic service providers delivering inside city centres. They will produce demand prediction models using both historical and real-time data. The purpose is also to develop an electronic auction system to assign parcel deliveries in real time to available delivery vehicles while minimizing travel and maximizing service. New delivery methods will be tested and deployed in 6 European cities including Hub&Spoke delivery, Hyperlocal on-demand delivery, Collaborative delivery, Digital-as-a-service delivery, and Containerisation delivery. Demonstration activities will generate new products and services that increase the efficiency of the operations of last mile deliveries which on the one hand create new local jobs and on the other will decrease costs and increase profits for the industrial players of the URBANE urban logistics value chain.
-
Today, the main challenge faced by healthcare systems worldwide is the management of chronic conditions. Indeed many diseases could be treated, but they still impact survivors' quality of life and health, requiring continual care even after recovery; moreover, additional chronic health and well-being issues could be generated by treatment. For example, breast cancer survivors have to deal with important challenges daily. Fear of recurrence (FOR), namely “fear, worry, or concern that cancer will come back or progress" is an important feature of cancer survivorship and literature shows that it relates to important factors in post-treatment overall health and quality of life. This project aims at modelling FOR based on fine-grained, multicomponent tracking of the experience along with individual characteristics and well-being/quality of life in breast cancer survivors. We aim at building an integrated system that results in a mobile application used by breast cancer survivors and by professionals to assess patient reported outcome.
-
This project aims to develop a multi-criteria framework for the co-evolution of feature subsets with neural topologies to design efficacious yet sparse and interpretable neural networks. To the best of our knowledge, such co-evolution has not been explored so far, especially, from the perspective of interpretability. Further, the part of this research focuses on the development of new search algorithms which are crucial to solving the multi-criteria co-evolution problem, which is known to be an NP-Hard problem. In particular, this research focuses on a day-ahead movement prediction of a stock index as a benchmark problem classification problem for neural architecture design.
-
This study aims to analyse optimal coalescing control strategies of (economically) different nations, subpopulations or groups.
This particular aspect has not been developed in the literature, especially not in the frame of network dynamics and neither in game theoretic formulation. The project started from a probabilistic formalism developed in decision dynamics built on Markov continous-time and quantum-like evolution systems, and needed to be extended to a graph ontology. Recent results include:- The realisation of an effective network design for population inhomogeneity. It was applied in a SIS model with variation of the individual social contact networks and the individual infectious-recovery rates.
- The extension of an heterogeneous network to a SIR epidemic on a social-contact networks. Control scenarios were also introduced (confinement and vaccination and their combination).
- The extension of the epidemic model to SEIRD and to take into account the cost of deployment of the control strategies constrained by economic, social, and technological factors.
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
-
Read publication
Next events
Contact us
Our team is at your disposal for any further information you may require.
Faculty and Research Team
direction.faculte@skema.edu
Copied