Multi-Objective Decision Making in Supply Chain Management Under Uncertainty

Dear Colleagues,

In order to study supply chain management at a global level, effectively managing the development of logistics and transport information technologies is necessary. The supply chain includes price fluctuations, climate change risks, geopolitical changes, or pandemic consequences, and the resulting data and information and the coordination of materials/stocks are most often associated with the transportation service of materials (products). It should be emphasized that innovative technologies implemented in supply chain management have a more effective impact on service provision if the information transmitted by technological development is compatible and interacts with cooperating companies. Since logistics companies are involved in supply chain management, innovations in supply chain technologies can be classified as random optimal management problems. In this case, the problems arising in supply chain management can be analyzed using optimal control and dynamic programming methods.

The journal topic “Multi-Objective Decision Making in Supply Chain Management Under Uncertainty” can be explored as a relevant interdisciplinary field that combines operations management, decision theory, logistics, and data analysis. Researchers might examine how various uncertainty factors—such as geopolitical changes, pandemics, fluctuations in raw material prices, or climate risks—impact decision making in supply chains. Articles may focus on the application of multi-objective optimization methods to address conflicting goals, such as cost reduction, delivery time minimization, sustainability, and service quality. Additionally, studies may present modeling approaches, scenario analysis, or artificial intelligence-based solutions that enable more effective supply chain management in a dynamic environment. This topic opens up opportunities for research on the practical application of decision support systems and the analysis of best practices in both local and global contexts.

We invite submissions that examine the application of data-driven modeling in various contexts, including but not limited to:

• Analysis of the impact of uncertainty factors on supply chain management.
• Analysis of changes in raw material price fluctuations.
• Analysis of the application of multi-objective optimization methods in the supply chain management process.
• Analysis of the impact of artificial intelligence-based solutions in supply chain management.
• Analysis of potential modeling methods for supply chain management.
• Analysis of decision support systems.

Dr. Kristina Vaiciute
Dr. Aušra Katinienė
Guest Editors

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