Mathematical models and algorithms to support the evolution of operational practices

运营管理是一个在过去几十年里经历了多次演变的领域。例如，运营现在更环保了。科学界，特别是运筹学(OR)专家，已经成功地利用他们的技能对这些演变进行建模，并实现了公司的目标，无论是制造业还是服务业。例如，我们现在建造的车辆路线可以最大限度地减少温室气体排放。最近，运营管理领域出现了几个演变：(I)运营决策的整合，(Ii)行业4.0，以及(Iii)对服务开发的关注。运营决策的整合在于一次做出相关决策，如生产和分销决策，而不是顺序决策。众所周知，这带来了许多好处，如降低业务成本和改善组织业绩等。Industry 4.0是虚拟世界和现实世界之间的另一种整合。除其他外，新技术的发展使这种整合成为可能，并促进了产品的可追溯性和更好地利用资源等。最后，服务开发的重点在于将运营研究和收入管理结合起来，为客户提供新的可能性。我的发现研究计划的长期目标是开发植根于运筹学的数学模型和算法，以有效地表示来自运营集成、行业4.0和服务开发的运营实践的演变。在短期内，我们将重点关注这些演变在供应链管理、生产计划和调度以及车辆路线方面的影响。首先，我们将对这些新的运营实践进行建模。然后，将设计有效的求解算法来解决由此产生的问题。最后，我们将分析运营实践演变的影响，并提出创新的运营实践。这项研究计划所做的工作将对科学界和实践者都是有益的。从科学的角度来看，所开发的算法将能够解决大型实例，从而与日益增长的海量数据的存在相一致。我们也将为新一代经典OR问题做出贡献。从实践的角度来看，该研究计划将导致创新运营实践的提出。这些创新做法将在进行大量数值实验后得到验证，并将有助于业务做法的演变。最后，通过培养高素质的人才，这一研究计划将有利于学生。

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.