Abstract

To cope with fluctuating production demands in the volatile markets, a new-type seru production system is adopted due to its efficiency, flexibility, and responsiveness advantages. Seru loading problems are receiving tremendous attention, however, full downward substitution and uncertainties in product demand and yield are seldom considered. Accordingly, a combinatorial optimization seru loading model is constructed to address these concerns so as to maximize system profits, which, however, is notoriously challenging to solve with exact algorithms. Therefore, an improved genetic-simulated annealing algorithm (IGSA) is designed to obtain optimal loading results. To validate the effectiveness and efficacy of the proposed IGSA, algorithm comparisons with adaptive genetic algorithm (A-GA) and simulated annealing (SA) algorithm are conducted. Results show that the proposed model is effective for addressing the seru loading problem and IGSA is robust in solving the seru loading model.

摘要

为应对市场波动中波动的生产需求，采用新型血清生产系统，具有效率、灵活性和响应能力的优势。血清加载问题受到极大关注，但很少考虑完全向下替代以及产品需求和产量的不确定性。因此，组合优化血清构建加载模型来解决这些问题，以最大限度地提高系统利润，然而，用精确的算法来解决这个问题是出了名的挑战。因此，设计了一种改进的遗传模拟退火算法（IGSA）以获得最佳的加载结果。为了验证所提出的 IGSA 的有效性和有效性，与自适应遗传算法 (A-GA) 和模拟退火 (SA) 算法进行了算法比较。结果表明，所提出的模型对于解决血清加载问题是有效的，并且IGSA在解决血清加载模型方面具有鲁棒性。