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Lopes, M. J., Cortinhal, M. J. & Melo, M. T. (2016). Design of Multi-Echelon Supply Chain Networks under Outsourcing Opportunities. 7th International Conference on Computational Logistics.

M. J. Lopes et al.,  "Design of Multi-Echelon Supply Chain Networks under Outsourcing Opportunities", in 7th Int. Conf. on Computational Logistics, Lisboa, 2016

@misc{lopes2016_1732205205751,
	author = "Lopes, M. J. and Cortinhal, M. J. and Melo, M. T.",
	title = "Design of Multi-Echelon Supply Chain Networks under Outsourcing Opportunities",
	year = "2016",
	howpublished = "Outro",
	url = "http://iccl2016.widescope.pt/"
}

TY  - CPAPER
TI  - Design of Multi-Echelon Supply Chain Networks under Outsourcing Opportunities
T2  - 7th International Conference on Computational Logistics
AU  - Lopes, M. J.
AU  - Cortinhal, M. J.
AU  - Melo, M. T.
PY  - 2016
CY  - Lisboa
UR  - http://iccl2016.widescope.pt/
AB  - We address the problem of designing a multi-echelon supply chain network comprising suppliers, production plants, warehouses and customer zones (see Fig. 1). Strategic decisions include opening new plants and warehouses at candidate sites and selecting their capacities from a finite set of available capacity levels. 
In addition, the operation of the supply chain network is also subject to decisions involving supplier selection, procurement of raw materials as well as production and distribution of end products. Shipments between facilities and to customer zones can be performed using different transportation modes. In Fig. 1, rail and road freight transport are depicted as examples of possible options. Each option is associated with a minimum shipment quantity, a maximum transportation capacity and a variable cost.
In the network displayed in Fig. 1, multiple types of end products are manufactured at plants by processing specific raw materials according to given bills of materials (BOMs). Different classes of raw materials are considered. One of them represents minor components that are required to manufacture all types of end products. At each potential plant location, some production resources may only be available to manufacture specific products (e.g. a machine dedicated to a given item). Global capacity limits are imposed at plants and warehouse locations. A new plant and a new warehouse can only be operating provided that a minimum capacity utilization level is achieved. 
Furthermore, a strategic choice between in-house manufacturing, outsourcing or a mixed approach is also to be made. An upper bound on the quantity of an end product that can be purchased from an external source is imposed. Outsourced products are consolidated at warehouses. Although product outsourcing incurs higher costs than in-house manufacturing, this option may be attractive when the cost of establishing a new facility to process given end items is higher than the cost of purchasing them.
A further distinctive feature of our problem is that each customer zone must be served by a single facility (either a plant or a warehouse). Many companies strongly prefer single-sourcing deliveries as they make the management of the supply chain considerably simpler. Direct shipments from a plant to a customer zone are only permitted if at least a given quantity is distributed to the customer zone. Such a delivery scheme reduces transportation costs for large quantities. In addition, each raw material must be purchased from a single supplier by an operating plant. However, different raw materials may be procured from multiple suppliers by the same plant. This feature overcomes the disadvantages of single-supplier dependency.
Fixed costs for location and capacity choices for plants and warehouses are considered. In addition, variable costs are associated with procurement, production, transportation and outsourcing.
We propose a mixed-integer linear programming model to determine the least cost network configuration that satisfies all demand requirements. By integrating various strategic and tactical features of practical relevance into a single model, our formulation generalizes several existing supply chain network design models. Moreover, the new model captures different types of network structures and tailored distribution strategies. Additional inequalities are derived in an attempt to strengthen the linear relaxation bound and to improve the performance of the model.
To gain insight into how challenging the problem at hand is to solve, a computational study is performed with randomly generated instances and using a general-purpose solver. Useful insights are derived from analysing the impact of different business strategies on various segments of the supply chain network. 

ER  -