Improved genetic-simulated annealing algorithm for <i>seru</i> loading problem with downward substitution under stochastic environment
Zhe Zhang, Lili Wang, Xiaoling Song, Huijun Huang, Yong Yin
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.