An empirical study on solving an integrated production and distribution problem with a hybrid strategy

Area of Science:

• Operations Research
• Supply Chain Management
• Industrial Engineering

Background:

• Effective supply chain coordination is crucial for performance.
• Integrating production and distribution is a key challenge.
• Existing methods may not fully address complex, multi-stage supply chains.

Purpose of the Study:

• To propose a novel two-stage hybrid solution methodology for supply chain coordination.
• To optimize the integrated production and distribution scheduling.
• To enhance overall supply chain performance through improved coordination.

Main Methods:

• A two-stage hybrid solution methodology was developed.
• The first stage involved fuzzy multi-objective scheduling optimization using a modified non-dominated sorting genetic algorithm II (NSGA-II) for production.
• The second stage utilized a modified genetic annealing algorithm (GAA) for distribution scheduling optimization.

Main Results:

• The proposed methodology successfully integrated production and distribution scheduling.
• Simulation results demonstrated the feasibility of the two-stage approach.
• The efficiency of the hybrid solution methodology was verified.

Conclusions:

• The novel two-stage hybrid solution effectively addresses supply chain coordination challenges.
• The integration of NSGA-II and GAA provides a robust optimization framework.
• The methodology offers a practical and efficient approach for improving supply chain performance.