A fuzzy interval optimization approach for p-hub median problem under uncertain information

Area of Science:

• Operations Research
• Logistics and Supply Chain Management
• Optimization Theory

Background:

• Stochastic and robust optimization methods yield suboptimal solutions for the uncertain p-hub median problem due to parameter discretization.
• Existing approaches struggle to maintain information integrity in complex, uncertain logistics network design.

Purpose of the Study:

• To propose a novel triangular fuzzy number model for the Non-Strict Uncapacitated Multi-Allocation p-hub Median Problem.
• To develop an enhanced optimization approach combining fuzzy logic with metaheuristics for improved efficiency and accuracy.

Main Methods:

• Developed a triangular fuzzy number model to represent uncertainty in the p-hub median problem.
• Integrated a triangular fuzzy number evaluation index with the Genetic-Tabu Search algorithm for optimization.
• Calculated the fitness of fuzzy hub schemes using membership function relationships during algorithm iterations.

Main Results:

• The proposed Genetic-Tabu Search algorithm reduced average computation time by 49.05% compared to Genetic Algorithm and 40.93% compared to Tabu Search.
• The novel approach decreased total costs in uncertain environments by 1.47% (vs. random), 2.80% (vs. robust), and 8.85% (vs. real-number optimization).

Conclusions:

• The triangular fuzzy number model and integrated Genetic-Tabu Search algorithm effectively address the uncertain p-hub median problem.
• This method enhances optimization speed and solution quality, outperforming traditional optimization techniques in logistics network design.