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A global satisfaction degree method for fuzzy capacitated vehicle routing problems
Juan Carlos Figueroa-García1, Jhoan Sebastián Tenjo-García2, Carlos Franco3
1Universidad Distrital Francisco José de Caldas, Bogotá - Colombia.
This study introduces a novel fuzzy logic approach to solve uncertain vehicle routing problems. The method effectively handles data scarcity by using expert-derived fuzzy numbers to find optimal delivery routes.
Area of Science:
- Operations Research
- Fuzzy Logic
- Optimization
Background:
- Capacitated Vehicle Routing Problems (CVRP) often face uncertainty in delivery costs and demands.
- Deterministic methods fail when data is scarce or unreliable.
- Expert knowledge is a valuable alternative for estimating uncertain parameters.
Purpose of the Study:
- To develop a method for solving uncertain CVRP with limited data.
- To integrate expert-derived fuzzy numbers into an optimization framework.
- To achieve a global optimal solution for fuzzy CVRP.
Main Methods:
- Utilizing fuzzy numbers to represent uncertain costs and demands derived from expert information.
- Employing an iterative-integer programming method combined with a global satisfaction degree.
- Introducing auxiliary variables and cumulative membership functions for fuzzy set analysis.
- Iteratively finding equilibrium between fuzzy costs/demands using alpha and lambda parameters.
Main Results:
- The proposed algorithm successfully finds a global optimal solution for uncertain CVRP.
- The method demonstrates convergence regardless of initial parameter selection.
- Algorithm performance remains consistent across various problem sizes and instances.
Conclusions:
- Expert-derived fuzzy numbers provide a robust solution for data-scarce CVRP.
- The iterative fuzzy optimization approach is effective and reliable.
- This method enhances decision-making in logistics and supply chain management under uncertainty.
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