Flow shop scheduling with general position weighted learning effects to minimise total weighted completion time

This article considers the flow shop problem of minimising the total weighted completion time in which the processing times of jobs are variable according to general position weighted learning effects. Two simple heuristics are proposed, and their worst-case error bounds are analysed. In addition, some complex heuristics (including simulated annealing algorithms) and a branch-and-bound algorithm are proposed as solutions to this problem. Finally, computational experiments are performed to examine the effectiveness and efficiency of the proposed algorithms.

Automated summary

This article discusses the flow shop problem of minimizing the total weighted completion time when the processing times of jobs vary based on position weighted learning effects. It introduces simple heuristics and complex heuristics, including simulated annealing algorithms, as well as a branch-and-bound algorithm to address this problem. Computational experiments are conducted to evaluate the effectiveness and efficiency of the proposed algorithms.