Improved genetic algorithm for the permutation flowshop scheduling problem
Computers and Operations Research
A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem
Expert Systems with Applications: An International Journal
Computers and Operations Research
Expert Systems with Applications: An International Journal
Computers and Operations Research
A note on the learning effect in multi-agent optimization
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Two-machine flowshop scheduling with truncated learning to minimize the total completion time
Computers and Industrial Engineering
Hi-index | 12.05 |
The learning effect in scheduling has received considerable attention recently. However, most researchers consider a single criterion with the assumption that jobs are all ready to be processed. The research of bi-criterion problems with learning effect is relatively limited. This paper studies a single-machine learning effect scheduling problem with release times where the objective is to minimize the sum of makespan and total completion time. First, we develop a branch-and-bound algorithm incorporating with several dominance properties and a lower bound to derive the optimal solution. Secondly, we propose a genetic algorithm to obtain near-optimal solutions. Finally, a computational experiment is conducted to evaluate the performance of the branch-and-bound and the genetic algorithms.