A HYBRID EVOLUTIONARY ALGORITHM FOR INTEGRATED PRODUCTION PLANNING AND SCHEDULING PROBLEMS

Integrated production planning and scheduling (IPPS) refers to a manufacturing management process by which raw materials and production capacity are optimally allocated to meet demand. However, most researches presented the different IPPS models with considering the different assumptions under the different manufacturing environment, and proposed the special optimization algorithms. Because the structure difference of the mathematical models, the effectiveness of the proposed algorithm is also different, most IPPS models are difficult to be applied to the practical manufacturing systems. In this paper, we propose a network modeling way to formulate the IPPS problem into a unified model. In addition, most scheduling models belong to the class of NP-complete problems even when simplifications in comparison to practical problems are introduced. The IPPS transform the original deterministic model to parametric formulations, which makes the problem more complicated. For solving this unified IPPS model, we propose a hybrid evolutionary algorithm (hEA) with combining genetic algorithm (GA) and particle swarm optimization (PSO). Finally, the experiments verify the effectiveness of proposed algorithm, by comparing with different evolutionary approaches for the several test problems.