A Two-Sided Matching Model Based on Multi-Criteria Decision-Making for Resource Allocation in the Automotive Industry

In the highly competitive automotive industry, efficient resource allocation is pivotal for optimizing logistics operations and ensuring market competitiveness. This study introduces a sophisticated two-sided matching (TSM) model integrated with multi-criteria decision-making (MCDM) techniques to optimally assign third-party logistics (3PL) contractors to sales partners across various provinces in Iran. By employing the Best–Worst Method (BWM) and TOPSIS, we systematically identify, weight, and rank criteria derived from an extensive literature review and expert consultations via questionnaires. For contractors, “On-time delivery” and “Number of fleets” emerge as the most critical criteria, while “Access to rail transport” and “Ability to ship load on return journey” are paramount for sales partners. The proposed TSM model, incorporating an ε-constrained many-to-many matching framework, ensures fairness by balancing mutual preferences and operational constraints. A comparative analysis across nine ε values (ranging from 2650 to 5900) demonstrates that ε = 5500 yields the optimal balance, achieving the highest fairness index (MF₁ = 0.593) with minimal rank variance, while maintaining operational efficiency. Higher ε values enhance fairness but slightly compromise efficiency, as evidenced by increasing objective function values. The model successfully meets provincial demands, with top-ranked provinces being Isfahan, Khorasan Razavi, and East Azerbaijan, and leading contractors. Compared to traditional one-sided MCDM approaches, this dual-preference, fairness-aware model offers superior scalability and applicability for complex logistics networks, providing actionable insights for strategic decision-making in the automotive sector.