Reducing tardy batches by seru production: Model, exact solution, cooperative coevolution solution, and insights

Abstract

Seru production, as an innovative production mode, has the advantages of quick response, good flexibility, and high efficiency. We investigate how to reduce tardy batches using seru production. The model of seru production, which minimizes the number of tardy batches, is formulated and decomposed into seru formation and seru scheduling. To obtain the optimal solution, we formulate a linear model of seru scheduling to maximize the number of on-time batches for a given seru formation. In the case of small-scale instances, the optimal solution for seru production is obtained by solving the linear model exactly for all seru formations. However, this method is extremely time-consuming. Therefore, for medium-scale instances, we develop an exact method based on surrogate-relaxation and branch-and-bound. Surrogate relaxation is developed to rapidly eliminate unpromising seru formations, where an upper bound of a given seru formation is quickly produced. If the upper bound is worse than the current lower bound of seru production, the seru formation is unpromising and eliminated in advance. Otherwise, it remains, and its feasible solution is obtained by surrogate relaxation to update the current lower bound. By the proposed method, 97.29% of unpromising seru formations are eliminated on average and 76.21% of computational time is saved. Subsequently, for each remaining seru formation, we develop a branch-and-bound algorithm to obtain an optimal solution. In the branch-and-bound method, the lower bound is used not only to prune branches but also to eliminate the remaining seru formations. For large-scale instances, we propose a cooperative coevolution algorithm combined with simulated annealing that can generate nearly optimal solutions for large-scale problems in seconds. Extensive experiments demonstrate the superiority of the proposed algorithms, and tardy batches are reduced by 62.06% on average. Based on the experimental results, several managerial insights are provided to reduce tardy batches.