Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm

Abstract

[EN] The traditional production scheduling problem considers performance indicators such as processing time, cost and quality as optimization objectives in manufacturing systems; however, it does not take energy consumption and environmental impacts into account completely. Therefore, this paper proposes an energy-efficient model for flexible flow-shop scheduling (FFS). First, a mathematical 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 the 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 conflicting apparently. 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