Introduction and objectives: This study aims to design a sustainable logistics network for automotive after-sales services based on the novel concept of the Physical Internet. It introduces a bi-level optimization model that simultaneously addresses economic, social, and environmental dimensions. At the first level, the strategic decisions of Iran’s Emdad Khodro Company such as determining the optimal order quantity for spare parts, selecting distribution hubs, and allocating resources to third-party logistics providers (3PLs) are considered. The second level focuses on the operational performance of 3PLs, including transport route optimization, emissions reduction, and improved customer response times. By integrating these two levels, the study presents a unified framework for managing the after-sales service logistics network.

With the rapid growth of the automotive industry and increasing customer expectations, efficient after-sales services have become a major challenge in this sector. Additionally, environmental concerns arising from logistics activities and escalating operational costs underscore the need for a reevaluation of traditional logistics models. This study leverages the concept of the Physical Internet, which is founded on resource-sharing principles and standardized processes, aiming to fill the existing gap in research related to sustainable automotive service logistics



Methodology: The proposed model is developed using a combination of exact and metaheuristic algorithms, including the epsilon constraint method and NSGA-II. It is implemented and evaluated in a real world case study involving Iran’s roadside assistance network, which comprises over 860 rescue personnel across the country. The dataset includes information on spare parts orders, the geographical locations of customers, distribution hubs, and the technical specifications of logistics vehicles. To solve the model, multi-objective approaches are employed to simultaneously optimize costs, carbon emissions, and response times. Additionally, sensitivity analysis of key parameters such as demand and fuel rates is conducted to assess the model’s flexibility.





Findings: The computational results indicate that the proposed model reduces total logistics costs by 17% compared to traditional methods. This cost reduction is attributed to optimized transport routes, fewer empty trips, and more efficient utilization of shared hubs. From an environmental perspective, CO₂ emissions in the network decrease by 22%, demonstrating the model’s positive impact on sustainability. Furthermore, the use of Physical Internet hubs as distribution centers improves roadside assistance personnel’s access to customers by an average of 12%, leading to enhanced customer satisfaction and a stronger brand image. On a social level, the proposed model increases the time productivity index of roadside assistance personnel by 15%, signifying a reduction in idle time and a fairer workload distribution.



Conclusion: The present study develops a bi-level sustainable logistics service network model based on the Physical Internet concept, offering a novel framework for integrating strategic and operational decisions in service supply chains. The main achievement of this research is the ability to synchronize economic, social, and environmental objectives within a flexible and shareable platform, enabling organizations to utilize dispersed operational capacities in a coordinated manner. Furthermore, the proposed model demonstrates that applying Physical Internet architecture to outsourced logistics services is not merely a cost-reduction tool but rather a structural approach to enhancing network resilience, agility, and sustainability. From this perspective, the study provides a practical decision-support model for large service organizations to redesign their logistics networks under uncertainty.