Hybrid collaborative evolutionary algorithm for integrated production planning

The integrated optimization of lot-sizing and scheduling with sequence-dependent setup and limited buffer space in a flow-shop was discussed. A nonlinear mix integer programming model was established, and a hybrid collaborative evolutionary algorithm was designed to solve this model. The objective function of the model was to minimize the sum of inventory cost, shortage cost, and overtime cost. Balanced inventory constraint and demand constraint were taken into consideration in constraint function. The proposed algorithm applied the parallel hybrid architecture of collaborative evolutionary algorithm and genetic algorithm, in which a kind of migration operator was designed to dynamically associate the coevolved subpopulations and the independently evolved common population. A neighborhood-based evolutionary strategy was also employed to improve the performance of the algorithm. Numerical simulation experiments of three different-scaled problems were conducted to demonstrate the effectiveness of the proposed algorithm.