1 research outputs found
ΠΡΡ ΠΎΠ΄ Π·Π°Π³ΡΡΠΆΠ΅Π½Π½ΡΡ Π²Π΅ΡΠ΅ΡΡΠ² in vitro ΠΈΠ· ΡΠΈΡΡΠ΅ΠΌΡ ΡΠΏΠΎΡΡΠ΄ΠΎΡΠ΅Π½Π½ΡΡ Π±ΠΈΠΎΠ΄Π΅Π³ΡΠ°Π΄ΠΈΡΡΠ΅ΠΌΡΡ ΠΎΡΠ΄Π΅Π»ΡΠ½ΠΎΡΡΠΎΡΡΠΈΡ ΠΌΠΈΠΊΡΠΎΠΊΠ°ΠΌΠ΅Ρ
We address in this paper the optimization of a multi-site, multi-period, and multi-product planning problem with sequence-dependent changeovers, which is modeled as a mixed-integer linear programming (MILP) problem. Industrial instances of this problem require the planning of a number of production and distribution sites over a time span of several months. Temporal and spatial Lagrangean decomposition schemes can be useful for solving these types of large-scale production planning problems. In this paper we present a theoretical result on the relative size of the duality gap of the two decomposition alternatives. We also propose a methodology for exploiting the economic interpretation of the Lagrange multipliers to speed the convergence of numerical algorithms for solving the temporal and spatial Lagrangean duals. The proposed methods are applied to the multi-site multi-period planning problem in order to illustrate their computational effectiveness.</p