Mathematical models and algorithms to support the evolution of operational practices

支持操作实践演变的数学模型和算法

• 批准号: RGPIN-2021-03327
• 负责人: Gruson, Matthieu
• 金额: $ 1.89万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742497
• 关键词: Mathematical; models; algorithms; support; evolution

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.

运营管理是一个在过去几十年中经历了许多演变的领域。例如，业务现在更加绿色。科学界，特别是运筹学（OR）专家，已经成功地利用他们的技能来模拟这些演变，并达到公司的目标，无论是在制造业还是服务业。例如，我们现在建立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）运营决策的集成，（ii）工业4.0，以及（iii）对服务开发的关注。运营决策的整合在于同时做出相关决策，如生产和分销决策，而不是顺序决策。众所周知，这会带来许多好处，例如降低运营成本和提高组织绩效等。工业4.0是虚拟世界和物理世界之间的另一种整合。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相结合，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数学模型和算法，植根于OR，以有效地代表来自运营集成，工业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等领域的影响。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。在这项研究计划中所做的工作将有利于科学界和从业人员。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相一致。我们也将有助于新一代的经典或问题。从实践的角度来看，该研究计划将导致创新运营实践的命题。这些创新做法将在进行大量数值试验后得到验证，并将有助于业务做法的发展。最后，这项研究计划将有利于学生通过培养高素质的人才。