Mathematical models and algorithms to support the evolution of operational practices

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Operations management is a field that witnessed numerous evolutions in the last decades. For instance, operations are now greener. The scientific community, and in particular the operations research (OR) experts, have successfully used their skills to model those evolutions and reach the objectives targeted by companies, whether in the manufacturing or service industry. For example, we now build vehicle routes that minimize the greenhouse gas emissions. Recently, several evolutions appeared in the operations management field: (i) the integration of operational decisions, (ii) Industry 4.0, and (iii) the focus on service development. The integration of operational decisions consists in making related decisions at once, such as the production and distribution decisions, instead of sequentially. This is known to bring numerous benefits, such as a decrease of operational costs and a better organizational performance, among others. Industry 4.0 is another kind of integration, between the virtual and physical worlds. This integration was made possible by the development of new technologies, among others, and facilitates the traceability of products, and a better use of resources, among others. Finally, the focus on service development consists here in combining operations research and revenue management to offer customers new possibilities. The long-term objective of my discovery research program is to develop mathematical models and algorithms, rooted in OR, to efficiently represent the evolution of operations practices coming from operational integration, Industry 4.0, and service development. In the short-term, we will focus on the implications of those evolutions in the areas of supply chain management, production planning and scheduling, and vehicle routing. First, we will model those new operations practices. Then, efficient solution algorithms will be designed to solve the resulting problems. Finally, we will analyze the impacts of the evolution of operations practices, and will propose innovative operational practices. The work done in this research program will be beneficial for both the scientific community and practitioners. From a scientific viewpoint, the developed algorithms will be able to solve large size instances, thus being consistent with the growing presence of massive data. We will also contribute to the new generation of classical OR problems. From a practical viewpoint, the research program will lead to the proposition of innovative operations practices. Those innovative practices will be validated after performing numerous numerical experiments and will contribute in return to the evolution of operations practices. Finally, this research program will be beneficial for students through the training of highly qualified personnel.