A collaborative scheduling model for the supply-hub with multiple suppliers and multiple manufacturers

Area of Science:

• Operations Research
• Supply Chain Management
• Industrial Engineering

Background:

• Assembly systems involve complex coordination between multiple suppliers and manufacturers.
• Decentralized decision-making in supply chains can lead to inefficiencies.
• The role of a central coordinating entity, like a Supply-Hub, needs investigation.

Purpose of the Study:

• To develop and evaluate a collaborative scheduling model for assembly systems.
• To compare the performance of centralized (Supply-Hub) versus decentralized decision-making.
• To address the computational complexity of the collaborative scheduling problem.

Main Methods:

• Development of two scenarios: separate decision-making and collaborative scheduling via Supply-Hub.
• Formulation of the collaborative scheduling model as a NP-complete problem.
• Proposal and application of an auto-adapted differential evolution algorithm for optimization.

Main Results:

• Collaborative scheduling with the Supply-Hub significantly improves performance over separate decision-making.
• The proposed auto-adapted differential evolution algorithm demonstrates effective convergence and reliability.
• The model and algorithm are suitable for complex supply chain environments.

Conclusions:

• A collaborative scheduling model coordinated by a Supply-Hub offers superior performance in assembly systems.
• The developed differential evolution algorithm provides a robust solution for complex scheduling problems.
• Centralized coordination enhances efficiency and reliability in multi-supplier, multi-manufacturer supply chains.