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This paper proposes a novel approach for the optimization of generalized supply chain network design (G-SCND) problems applied to the upstream sector of the oil and gas industry. Generally, a supply chain network is represented by complex graphs including flows of goods, materials and information linking the different nodes making part of it. These nodes account for suppliers, warehouses, distribution centers, cross-docking facilities and/or demand points. Nowadays, the globalization along with the shortening of the product life-cycle and the necessity of high standards of responsiveness challenge the supply chain to be agile and flexible to face a changing environment.[1] The optimal design of materials supply chain networks have become a strategic field in the recent years due the implication of the logistic expenses in the global costs of organizations. With some differences according to the industry, logistic costs average 7% to 9% of the sales of a company, reaching up to 30% for certain chemistry industries. In global terms, the IMF roughly estimates that the logistic costs are about 12% of the global GDP.[2] As a result, the optimization of the supply chain network design may have an enormous impact on the overall operation costs. The search for efficient, flexible and robust network designs gives rise to a very challenging and interesting problem. Researchers have addressed this topic with different tools, including heuristics, optimization and simulation models, though several issues still remain open.[3] The design of a SCN is a strategic and long-term problem to be addressed by one or a group of organizations (extended supply chain network concept). It involves decisions on the location and type of facilities to be built, which products to storage, the inventory policies to adopt and how the goods will be transported to reach the clients. The aim is to fulfill customer demands, minimizing the net present value of operational costs and capital investment. Furthermore, the network should be designed considering the responsiveness to accomplish a determinate service level with customers. In the particular case of the upstream sector, the exploration and exploitation of oil and gas reserves requires the operators to provide a large number of materials towards the surface facilities in order to carry out tasks related with locations building, energy supplying, infrastructure set-up, drillings, workovers and well completions. Moreover, the growth of non-conventional resources development deepens the need of efficient supply chains due the enormous quantities of materials, proppants and water required comparing with the conventional operations. The principal weakness identified in the literature consist in the inexistence of general and flexible models addressing the facility localization and supply chain network design problems, being the vast majority focused in Fixed-Supply Chain Network Design (F-SCND) approaches. These, mainly pre-determine the number of echelons in the SCN jointly with the type of facility to be installed in each one (warehouse, distribution center or a factory). Furthermore, 80% of the literature propose networks with one or two echelons and an important number of works consider the management of a single product. Little relevance and attention have had for researchers the interns flows and the possibility of direct supply from not-end echelons to the clients. F-SCND approaches establish in advance rigid schemas that lead to non-optimal solutions.[4] To face these weaknesses, Generalized Supply Chain Network Design (G-SCND) models have been proposed to represent more flexible networks including the location of various types of facilities in several nodes, multiproducts and the demand fulfillment from any node, among others features. The power of G-SCND approaches is the ability of impact different operational logics and trade-offs over the structural SCND capturing the intertwined nature of decisions and leading to more efficient results.[5] This paper presents a novel G-SCND approach applied to the upstream sector in the oil and gas industry considering all its characteristics in order to obtain a complete conceptual model. The clients in the model corresponds to the oil and gas reserve areas, which demand different types of materials required by the wells for its development and operation. The proposed model allows the differentiation of investment from operational materials demands. The first one comprises all the materials needs to develop a well and depends directly on the companies' operational projections for reserves. The rest of the materials demands reflects the maintenance needs of the current wells structure, which incorporates through the time the new drilled wells. Additionally, the proposed model do not limit the number of echelons or layers but allows, through a novel formulation, infinite transportations between facilities in order to reach the clients. It is known that strong economies of scale are present in a supply chain network, from investments in new infrastructure to operational costs. Exists a non-linear relationship between the size of an installation and the corresponding required investment, such that each additional unit size added to a facility is more economic than the previous. The identical phenomena occurs with the costs, where the increment in the volume of operations diminish the unitary handling and transportation costs. To capture this economies of scale, three types of locations (large - medium - small) are proposed in the model, each one possible to be located in a set of potential points. The main difference between them is in terms of the minimum and maximum flows limits established globally and for every product at each type of facility. Fixed costs, unitary handling costs and transportation cost are also impacted by the type of facilities involved. Finally, the concept of opportunity cost is introduced in order to represent the responsiveness of the SCN through the measurement of the time required to serve the wells with its materials. In the oil and gas industry, the equipment used for drilling, fracturing and workover tasks is, in most cases, rented and represent a big fixed cost for companies. Maximize the utilization of these equipment is crucial and depends on the minimization of the travelling times and the rapid assistance with materials. Also, when a problem occurs in a well, the production is stopped representing a loss for the company. Thus, minimizing the time to repair wells is important in order to reactivate the production as soon as possible. A mixed integer linear programming (MILP) formulation is developed to represent mathematically the conceptual model proposed. The objective is to establish a structure of facilities which minimize the net present value (NPV) over the planning horizon considering all the capital investments and operational costs such as (1) transportation costs, (2) acquisition costs, (3) financial stock costs, (4) handling costs, and (5) opportunity costs. The model takes primary, intern and secondary logistic decisions, which are, respectively, the materials acquisition from suppliers, the intern transportations between facilities and the final demand fulfillment. A set of theoretical study cases with different demand patterns, geographical distributions and product families? properties is developed to test the flexibility of the proposed approach. Various SCN designs are obtained with different types of facilities installed and transportation flows between them. This results shows a logical reflect to the variations in the data structure in order to take advantage of the present trade-off. The division between investment and operational materials demands allows to perform a sensitivity analysis of different projections for the development of the reserves, which is important in order to guarantee the robustness of the SCN. Summarizing, a novel G-SCND approach is proposed for the upstream sector in oil and gas industries. The main concepts introduced are the elimination of pre-determined echelons structures and the capture of the strong economies of scale through the modeling of three types of facilities possible to be installed in each potential node. The formulation comprises all the components present in the operation of supply chains generating a complete SCN representation. 1-Shah, N., Process industry supply chains: Advances and challenges. Computers and Chemical Engineering, 29 (2005) 1225?12352- International Monetary Fund, World Economic and Financial Survey. 3- Tsiakis P., Design of Multi-echelon Supply Chain Networks under Demand Uncertainty. Ind. Eng. Chem. Res. 40 (2001), 3585-3604.4- Melo M.T. et al, Facility location and supply chain management ? A review European Journal of Operational Research 196 (2009) 401?412.5-Kalaitzidou M.A., Optimal Design of Multiechelon Supply Chain Networks with Generalized Production and Warehousing Nodes, Industrial & Engineering Chemical Research, 53, (2014), 13125−13138.