Abstract: Production planning and inventory management as an important component of production management, play an important role in production and operation of enterprises. Based on the oil demand forecast and taking the economic benefits of enterprise-scale into account, multi-stage production inventory optimization model which is based on the changes of processing costs is established to minimize the production costs. The model demonstrate not only the impact of unit processing cost on the production plan, but also consider the finished goods inventory optimization, therefore, meet the business requirements of minimizing inventory costs. The adaptive weight particle swarm algorithm and simulated annealing-based particle swarm algorithm are used to solve the model, The experimental results show that simulated annealing based on the performance of PSO is better than the adaptive weight of the particle swarm algorithm, which can jump out of the local optimum and converge to the global optimum faster.

Key words: Production inventory , Changes unit processing costs ,  Particle swarm optimization , Simulated annealing

摘要：     　生产计划与库存管理作为生产管理重要的组成部分，对企业的生产运营起着重要的作用。以成品油需求量预测值为基础，在考虑企业规模经济效益的情况下，以最小化企业生产库存成本为目标，建立了基于变动加工成本的多阶段生产库存优化模型。该模型不仅体现了生产装置的单位加工成本对生产计划的影响，而且还考虑了产成品库存优化问题。因此，可满足企业对生产成本最小化的要求。最后，分别采用自适应权重的粒子群算法和基于模拟退火的粒子群算法对模型进行了优化求解。实验结果表明：基于模拟退火的粒子群算法的性能优于自适应权重的粒子群算法，这是因为前者跳出局部最优的速度较快，能有效地收敛于全局最优。

关键词: 　生产库存 , 　单位变动加工成本 , 　粒子群算法 , 　模拟退火算法