Mathematical Modeling and Optimization Techniques for Supply Chain Management

Dear Colleagues,

Mathematical modeling, particularly, optimization techniques, plays a crucial role in supply chain management by improving the efficiency and effectiveness of various processes involved in supply chains. Supply chain optimization involves finding the most effective and efficient ways to manage the flow of goods, services, and information from suppliers to customers.

One of the key benefits of optimization in supply chain management is the ability to reduce costs and improve profitability. This can be achieved by identifying and eliminating inefficiencies in the supply chain, such as excess inventory, transportation costs, and production delays. Optimization can also help to improve customer satisfaction by ensuring the timely delivery of products and services.

Optimization and mathematical modeling techniques can be used to solve a variety of supply chain management problems. The focus of this Special Issue is on the application of mathematical modeling, optimization techniques and artificial intelligence (AI) algorithms for addressing supply chain management problems. Some common applications of mathematical modeling in supply chain management include but not limited to:

• Supplier selection: AI and optimization techniques, such as mixed integer linear programming (Hu et al. 2016), stochastic programming (Hosseini et al. 2019), simulation (Wu and Olson 2008), Bayesian networks (Hosseini and Barker 2016; Hosseini and Ivanov 2020) and ensemble machine learning algorithms (Hosseini and Al Khaled 2019) can be used to identify the best suppliers based on criteria such as cost, lead times, quality, inventory level, risk level, financial status, service level, etc.;
• Supply chain risk analysis: AI algorithms can be used to model supply chain disruption and predict the restoration and recovery level of disrupted supply chain entities (Hosseini et al. 2020; Moosavi and Hosseini 2021; Hosseini and Ivanov 2021; Hosseini and Ivanov 2022). Optimization techniques can be utilized to find the optimal budget and human resources to avoid disruptions on supply chain operations;
• Production planning: optimization techniques can be used to optimize production planning by determining the most efficient production schedule, production line allocation and resource allocation to meet demand while minimizing production costs.

In summary, the aim of this Special Issue is to explore the application of AI and optimization techniques that enable supply chain managers to make data-driven decisions to optimize their operations and achieve better outcomes, such as reduced costs, improved efficiency, and increased customer satisfaction.

Dr. Seyedmohsen M. Hosseini
Guest Editor

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• supply chain management
• optimization
• operations research
• artificial intelligence
• mathematical modeling