An improved discrete backtracking searching algorithm for fuzzy multiproduct multistage scheduling problem

In this study, we address the fuzzy multiproduct multistage scheduling problem (FMMSP). The FMMSP with the objective of minimizing makespan is close to practical manufacturing circumstances in terms of the uncertainty in processing time. Backtracking searching algorithm (BSA) is a new proposed meta-algorithm for continuous optimization problems. To solve the FMMSP, an improved discrete BSA, called discrete BSA with local search (DBSA-LS), is developed. The information of the global best solution is incorporated into the mutation process to improve the convergence speed of discrete BSA. A local search related to the FMMSP enhances the exploitation ability of the improved discrete BSA. Left shift operation is applied to improve the solution quality when a hybrid encoding string is decoded into the scheduling scheme. The influence of the key parameter on the performance of the proposed algorithm is tested using different size instances. Sixteen different scales of instances are used to evaluate the performance of the proposed DBSA-LS. Several comparisons are conducted between DBSA-LS and three other algorithms. The results show the effectiveness of the proposed DBSA-LS.
Source: Neurocomputing - Category: Neuroscience Source Type: research