Using shared resources has created better opportunities in the field of sustainable logistics and procurement. The Multi-Depot Traveling Purchaser Problem under Shared Resources (MDTPPSR) is a new variant of the Traveling Purchaser Problem (TPP) in sustainable inbound logistics. In this problem, each depot can purchase its products using the shared resources of other depots, and vehicles do not have to return to their starting depots. The routing of this problem is a Multi-Trip, Open Vehicle Routing Problem. A tailored integer programming model is formulated to minimize the total purchasers’ costs. Considering the complexity of the model, we have presented a decomposition-based algorithm that breaks down the problem into two phases. In the first phase, tactical decisions regarding supplier selection and the type of collaboration are made. In the second phase, the sequence of visiting is determined. To amend the decisions made in these phases, two heuristic algorithms based on the removing and insertion of operators are also proposed. The experimental results show that not only can purchasing under shared resources reduce the total cost by up to 29.11%, but it also decreases the number of dispatched vehicles in most instances.

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.

Ci,j d1 d2 d3 s1 s2 s3 s4 s5 s6 s7 d1 0.00 157 78 49 81 M p4 M M M 20 M 27 M p5 189 192 116 12 47 71 165 p6 M M M 4 M M 9 p7 34 M M 130 M M M p8 147 M 130 91 110 60 149 p9 38 138 37 74 126 157 17 p10 186 156 98 88 90 62 M p11 Required products d1 p1:p4 d2 p5:p10 d3 p11:p15 PS 2 Q 6In each cell M means that that product is not available by that supplier The required products of each depotThe number of parking spaces of each depotThe capacity of vehicle

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