%0 Journal Article
%T A Four-Type Decision-Variable MINLP Model for a Supply Chain Network Design
%A M. M. Monteiro
%A J. E. Leal
%A F. M. P. Raupp
%J Mathematical Problems in Engineering
%D 2010
%I Hindawi Publishing Corporation
%R 10.1155/2010/450612
%X We propose a mixed integer nonlinear programming model for the design of a one-period planning horizon supply chain with integrated and flexible decisions on location of plants and of warehouses, on levels of production and of inventory, and on transportation models, considering stochastic demand and the ABC classification for finished goods, which is an NP-hard industrial engineering optimization problem. Furthermore, computational implementation of the proposed model is presented through the direct application of the outer approximation algorithm on some randomly generated supply chain data. 1. Introduction It is known that industrial organizations can obtain significant savings through the optimal design of their supply chain networks. Indeed, the optimal design can contribute to refine logistics objects as well as logistics strategies, improve on the architecture logistics network, and above all, support decision making. However, decision makers have troublesome task when dealing with integrated planning of logistics networks. Since this industrial engineering optimization problem is in general difficult and more specifically NP-hard even for networks with small sizes, trying one by one potential plans is very time consuming, and therefore impractical. In fact the optimization of an integrated logistics network design is still a challenge, specially if many items, many layers, many logistics components, many different types of decision variables and stochastic demands are being considered. With respect to the number of different types of decision variables, just a few existing studies have addressed the logistics network design problem considering three or more layers and deterministic demands with four different types using mixed integer linear programming models (MILP) [1, 2]. According to the recent review made in [3], the works of [4, 5] can fit the design optimization of a one-period planning horizon logistics network with stochastic demand with three or more layers, but they involve only decisions on location using MILP models. Uncertainty of customer demands has also been considered in [6] in order to determine, for example, the optimal network design, transportation and inventory levels of a single-item multiechelon supply chain. In [7], the same authors formulated a bi-criterion MINLP for the optimal design of responsive process supply chains with inventories, considering economic and responsiveness objectives. Besides the cited references relevant for this work, there exist many works in the literature that address the optimization of
%U http://www.hindawi.com/journals/mpe/2010/450612/