Abstract:

To solve the stack selection problem in the integrated management of inventory and production for the iron steel enterprises,we construct a joint optimization model to balance the load of each stack and to maximize the slab comprehensive matching degree at the same time based on the Ashaped constraints,dispersive constraints et al.To help the solution jump out of the local optimum during the evolution when using the particle swarm optimization (PSO) algorithm, we introduce an elitist learning strategy,which can improve the solutions when the group converges.Finally,simulation results demonstrate the validity and feasibility of the proposed algorithm.

Key words: stack selection problem;particle swarm optimization;elitist learning strategy;dispersive constraints;exponent of convergence