## 1 Introduction
This is because the characteristics of the selected locations, constraints, and the type and number of needs in each area are not necessarily similar to each other, and these differences make uniform facilities have insufficient efficiency [16]. From these differences, we can, for example, discern the following: in an area with a high population density, there is a need for facilities with high capacity, or in an area where there are suitable and extensive access routes, the coverage radius of a hub can be greater than other hubs or the types of needs may vary in different areas and require hubs with different services [17]. Despite these conditions, to reach an optimal solution, facilities with different characteristics and capacities should be considered. This is known in the location literature as hub location, and considering this feature can lead to a more suitable solution for the problem [18]. In the field of hub location in various domains, including healthcare, uncertainty is one of the influential factors. For various facilities, many parameters such as the amount, type, and timing of demand creation are uncertain, and considering this uncertainty in the parameters can increase the practicality of the problem [19]. One of the assumptions that has been prevalent in location problems in the past is the assumption of facilities operating under different conditions [20]. However, this assumption is not valid in the real world, and facilities may be disrupted due to various reasons such as pollution and environmental issues, causing their activities to be halted [21]. The reasons for disruptions can be natural, such as disruptions due to the failure of wastewater and air catalysts, or human-induced, such as disruptions in the performance of air, water, and soil settlement due to operational errors [22]. Therefore, considering the probability of disruptions in pollutant production and providing a model that can continue its work under environmental disruption conditions is of paramount importance and high applicability [18]. In this article, we also attempt to provide a model that addresses hub location in a green state by considering environmental disruptions. Another aspect to consider in hub location is the limitations present in facility capacities and communication routes. Each facility has a limited capacity based on various factors, and it cannot provide services beyond this capacity. Therefore, considering limited capacities for facilities is necessary, and if these constraints are not considered, we deviate from real conditions, and the model will not be practical. On the other hand, many hub location problems have multiple objectives [23; 24; 25; 26; 27]. Furthermore, the proposed model offers significant applications in telecommunications and other industries where efficient hub location planning is critical. Telecommunication networks play a critical role in modern infrastructure, serving as the backbone for emergency services, high-speed internet, and other data-driven applications. The strategic planning of Base Transceiver Station (BTS) locations directly impacts the network's efficiency, coverage, and resilience. This paper delves into optimizing BTS site location within a green supply chain framework. It aims to address the demands of urbanization, environmental considerations, and the increasing complexity of telecommunication demands. The proposed model emphasizes not only cost-effectiveness but also sustainability and service accessibility [28; 29; 30; 31; 32]. Recent studies highlight the critical need for robust and adaptable hub location models in telecommunication networks to ensure high service levels under varying demand scenarios. The integration of advanced algorithms such as NSGA-II and MOPSO provides a dual-objective approach, balancing operational costs with enhanced service quality.