Modeling the leader-follower supply chain network under uncertainty and solving by the HGALO algorithm

Area of Science:

• Operations Research
• Supply Chain Management
• Optimization

Background:

• Supply chain networks (SCNs) face significant uncertainty, impacting profitability and strategic decisions.
• Optimizing SCNs requires balancing multiple objectives across different network echelons.
• Existing models often struggle to address the complexities of bi-level decision-making and uncertain parameters.

Purpose of the Study:

• To develop a competitive supply chain network (SCN) model that maximizes total network profits amidst uncertainty.
• To strategically optimize the location of suppliers, manufacturers, distribution centers, and retailers within the SCN.
• To analyze optimal flow allocation and product pricing strategies in a bi-level SCN framework.

Main Methods:

• A bi-level optimization model was converted to a single-level model using Karush-Kuhn-Tucker (KKT) conditions.
• Fuzzy programming techniques were employed to manage and control uncertain parameters within the SCN.
• A novel hybrid genetic and ant-lion optimization algorithm (HGALO) was developed and applied to solve the model.

Main Results:

• Increasing uncertainty rates were found to increase demand but decrease the optimal base flow value (OBFV) and average product selling price.
• The fuzzy programming approach effectively handled parameter uncertainty, providing insights into its impact on SCN performance.
• The HGALO algorithm demonstrated superior efficiency and performance compared to other algorithms, especially for larger-scale SCN problems.

Conclusions:

• The proposed SCN model provides a robust framework for strategic decision-making under uncertainty.
• The HGALO algorithm offers an effective computational tool for solving complex, large-scale SCN optimization problems.
• Findings highlight the critical interplay between uncertainty, demand, pricing, and network structure in SCN profitability.