The textile industry is particularly vulnerable to problems in subcontractor selection, where choices are critical and must be made regarding quality, price, and delivery time. In this paper, we used a hybrid model of fuzzy Logic and ant colony optimization (ACO) to address the problem of subcontractor selection. Fuzzy Logic was used to make the ratings of the subcontractors less precise due to the uncertainty in the evaluation process and the subjective factors such as quality, price, and delay. This method enables decision-makers to develop a more sophisticated decision-making framework by translating qualitative judgments into quantitative ratings. At the same time, the ant colony system uses a swarm intelligence approach to search for possible subcontractors. It improves the selection process based on the pheromone implementation and the adaptive path planning. The integration of these two methodologies allows for a more effective decision support system for subcontractor evaluation and, consequently, improves the company’s overall operations. This article presents a real-life case study of a textile company that demonstrates the feasibility of this hybrid model and the better results obtained in subcontractor selection compared to human decisions. The results show that the system designed in this paper reduces costs and improves suppliers’ quality and delivery times, thereby enhancing the textile firm’s competitiveness. The analysis of human expertise versus the hybrid Ant Colony Optimization and fuzzy logic system reveals enhanced performance across delays, defects, and costs. The comparison between Algorithm Optimization and Human Decision-Making reveals an 8.25% total improvement. The algorithm outperforms human decision-making by reducing delays by 9.87%, costs by 17.97%, and defects by 2.55%. This paper provides a basis for future research on improving supply chain management decisions using intelligent systems.
Lahdhiri et al. (Wed,) studied this question.