model for a FFS problem, which is based on an energy-efficient mechanism, is described to solve multi-objective optimization. Since FFS is well known as a NPhard problem, an improved genetic-simulated annealing algorithm is adopted to make a significant trade-off between the makespan and the total energy consumption for implementing a feasible scheduling. Finally, a case study of production scheduling problem for metalworking workshop in a plant is simulated. The experimental results show the relationship between the makespan and the energy consumption is apparently conflicting. Moreover, an energy saving decision is performed in a feasible scheduling. Using the decision method, there can be a significant potential to minimize energy consumption while complying with the conflicting relationship.
Dai et al. REF reported an energy efficient model for the flexible flow shop scheduling and employed a genetic-simulated annealing algorithm to make a significant tradeoff between the makespan and the total energy consumption.
Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm
Robotics and Computer-integrated Manufacturing
Engineering