This paper describes the way a multi-item, multi-plant Lot-Sizing problem with transfer costs and capacities may be reformulated according to a multi-commodity flow

Lagrangean decomposition has been recognized as a promising approach for solving large-scale optimization problems. However, Lagrangean decomposition is critically dependent on the method of updating the Lagrangean multipliers used to decompose the original model. This paper presents a Lagrangean

decomposition algorithm to solve the MIP problem with a series of continuous problems. In section 4, we present results for small and large networks (experimental and randomly generated). 2 Formulation We consider an aggregated-flow based network, where traffic data (packets) arriving to a source for a

We present a scenario decomposition framework based on Lagrangean decomposition for the multi-product, multi-period, supply investment planning problem considering network design and discrete capacity expansion under demand uncertainty. We also consider a risk measure that allows us to reduce

in practice. Hence, we present two heuristic approaches based on Lagrangean Decomposition and Column Generation, which turned out to be well suited. From these methods we obtain lower bounds as well as feasible solutions.

of periods resulting in a large scale problem to solve. In order to reduce the problem to a manageable size, we propose a specialized heuristic algorithm that relies on the concept of Lagrangean decomposition. We present an iterative scheme where feasible solutions are postulated from the Lagrangean

Temporal and spatial Lagrangean decompositions in multi-site, multi-period production planning problems with sequence-dependent changeovers

include Lagrangean Decomposition, Lagrangean Relaxation, and Lagrangean/surrogate Relaxation, coupled with subgradient and modified subgradient optimization. Several schemes derived from the techniques are proposed and applied to the process network model. The results from the full-scale solution

vector of multipliers for the capacity constraints, the second for the assignment constraints and the other for the Lagrangean decomposition constraints. Relaxation multipliers are used with ecient constructive heuristics to ®nd good feasible solutions. The application of a Lagrangean/surrogate approach

the bidders demand a combination of dependent objects with a single bid. We show that not only can we use this auction mechanism to handle complex resource scheduling problems, but there exist strong links between combinatorial auction and Lagrangean-based decomposition. Exploring some of these properties, we