JOB SHOP SCHEDULING WITH NON-BOTTLENECK MACHINES CONSIDERING THE MAINTENANCE ASPECTS

Abstract

Optimising the scheduling of current machines is related to machine scheduling. It is a common premise that machines are available whenever need arises. However, it is not true always as a machine may become nonfunctional at certain point of times. The problem of the job shop in a dynamic environment is attempted to be solved. Job shop maintenance issues are challenging optimisation issues. To reduce the likelihood of a machine breaking down, predictive maintenance is a viable option. The three objectives are to prioritise the volume of handled jobs, decrease the completion time, and consequently decrease the finishing times of the non-bottleneck machine in unrelated preventive maintenance scheduling (upms). To find roughly workable solutions, a hybrid tabu search (TS) method and mixed-integer programming (MILP) model are developed According to computational findings, the hybrid TS approach enables quick acquisition of the most processed jobs (average 8 s). The first two goals have been accomplished by the MILP model. All three objectives are satisfactorily attained by the hybrid TS algorithm. The hybrid TS algorithm's third phase also demonstrates its efficiency in increasing equipment utilisation.

Keywords: scheduling, breakdown, machine, random