sequencing mixed model assembly line problem to minimize line stoppages cost by a modified simulated annealing algorithm based on cloud theory

this research presents a new application of the cloud theory-based simulated annealing algorithm to solve mixed model assembly line sequencing problems where line stoppage cost is expected to be optimized. this objective is highly significant in mixed model assembly line sequencing problems based on just-in-time production system. moreover, this type of problem is np-hard and solving this problem through some classical approaches such as total enumeration or exact mathematical procedures such as dynamic programming is computationally prohibitive. therefore, we proposed the cloud theory-based simulated annealing algorithm (csa) to address it. previous researches indicates that evolutionary algorithms especially simulated annealing (sa) is an appropriate method to solve this problem; so we compared csa with sa in this study to validate the proposed csa algorithm. experimentation shows that the csa approach outperforms the sa approach in both cpu time and objective function especially in large size problems.