The rapid growth of the transportation industry and the advance of the e-commerce have opened opportunities for boom delivery services companies. Although, intense competition between shipping companies has obliged many to amend their shipment networks. In this setting, we aim to provide high-quality shipping services for a third-party logistics company in Iran. Currently, in the selected company, one hub is in the west part of Tehran that gives services to all customers. There are some difficulties in shipping services to the eastern part of Tehran city. Therefore, in this paper, we find the location of another hub center to streamline the shipping process. Moreover, to promote our service levels, some Distribution Centers (DCs) are going to be set up. Due to the lack of shipment data like pick up and delivery data, we tried to find the locations of the hub and the DCs based on Tehran population's data. We clustered 122 regions of Tehran based on population, economic index, accessibility to the internet, and the number of business units. Since some remote regions (which are not in the same vicinity) belong to the same cluster, we defined a relative distance criterion to avoid making remote regions in the same cluster. In each cluster, a fixed or movable DC can be set up to service the regions of its cluster. Moreover, to find the best candidate locations for the hub, each zone of Tehran was assessed based on some criteria like land cost, accessibility to the highways, and distance to its nearest bus terminal. Based on these criteria, some zones have been dominated by others, and the remains were considered as candidate locations in a hub location model. By considering the DCs as spokes in a hub and spoke model, the optimal location for the eastern hub establishment was determined.

As the growth of e-commerce continues to accelerate, there is a need for new and innovative strategies in last-mile delivery to meet the changing demands of customers. The main objective of this study is to address this need by optimizing the last-mile delivery problem with service options (LMDPSOs) through a novel two-phase approach that considers various delivery options such as home delivery, self-pickup, and delivery at different prices. This original approach enables simultaneous optimization of the selection of the most appropriate pickup and delivery points and determination of the most efficient vehicle routing. The LMDPSOs reduces overall costs, minimizes environmental impact, and considers customer satisfaction levels by determining the most appropriate trips according to the available service options. This research employs a two-phase methodology for decision making. The first phase determines the value of the proposed locations through a novel multi-criteria decision-making (MCDM) approach that incorporates sustainability criteria. In the second phase, a tailored mathematical model is proposed for vehicle routing with service options. The model is coded in the CPLEXsoftware version 12.6 in various dimensions. We evaluate the potential and advantages of diverse delivery choices, illustrating that aggregating orders at pickup and delivery points can reduce delivery costs and minimize environmental impact. Additionally, this paper directs managers in selecting the most appropriate delivery method for last-mile delivery, considering environmental, social, and economic factors.