Single machine flow-time scheduling with a single breakdown
Acta Informatica
Computers & Mathematics with Applications
Single-machine scheduling with general learning functions
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Scheduling a maintenance activity to minimize total weighted completion-time
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Single machine scheduling with decreasing linear deterioration under precedence constraints
Computers & Mathematics with Applications
Complexity and algorithms for two-stage flexible flowshop scheduling with availability constraints
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Single machine multiple common due dates scheduling with learning effects
Computers & Mathematics with Applications
Some single-machine scheduling problems with general effects of learning and deterioration
Computers & Mathematics with Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Approximability of two-machine no-wait flowshop scheduling with availability constraints
Operations Research Letters
Hi-index | 0.09 |
There are different reasons, such as a preventive maintenance, for the lack of machines in the planning horizon in real industrial environments. This paper focuses on the multi-objective flexible job-shop scheduling problem with parallel machines and maintenance cost. A new mathematical modeling was developed for the problem. Two meta-heuristic algorithms, a hybrid genetic algorithm and a simulated annealing algorithm, were applied after modeling the problem. Then, solutions of these meta-heuristic methods were compared with solutions obtained by using the software LINGO for small-scale, medium-scale, and large-scale problems in terms of time and optimality. The results showed that the applied hybrid genetic and simulated annealing algorithms were much more effective than the solutions obtained using LINGO. Finally, solutions using the simulated annealing approach were compared with solutions of the hybrid genetic algorithm.