Integrated optimization of disruption management and scheduling for reducing carbon emission in manufacturing

Currently disruption management and Job-shop scheduling are optimized separately, which may ignore the influence of carbon emission on machine allocation, processing sequence in scheduling. In order to reduce carbon emission in manufacturing process, a multi-objective optimization model of disruption management and rescheduling strategy are proposed. Because there are many parameters to be optimized in the proposed method, an improve quantum bacterial optimization algorithm (IQBFO) based on prospect theory is designed to solve the proposed model. Four response indexes of 16 kinds of rescheduling scenarios are simulated and analyzed by using the IQBFO and comparing with the existing scheduling algorithms. The validity of proposed disruption management model and method on reducing carbon emission in manufacturing processes is verified by statistical analysis of the experimental results.