Robust schedules for tardiness optimization in job shop with interval uncertainty

This paper addresses a variant of the job shop scheduling problem with total tardiness minimization where task durations and due dates are uncertain. This uncertainty is modelled with intervals. Different ranking methods for intervals are considered and embedded into a genetic algorithm. A new robustness measure is proposed to compare the different ranking methods and assess their capacity to predict 'expected delays' of jobs. Experimental results show that dealing with uncertainty during the optimization process yields more robust solutions. A sensitivity analysis also shows that the robustness of the solutions given by the solving method increases when the uncertainty grows.

Automated summary

This paper discusses a variant of the job shop scheduling problem that deals with uncertainty in task durations and due dates using interval modeling. Different ranking methods for intervals are explored and integrated into a genetic algorithm. A new measure of robustness is proposed to evaluate the ability of ranking methods to predict expected delays. Experimental results demonstrate that considering uncertainty during optimization leads to more robust solutions. Sensitivity analysis also indicates that the robustness of solutions improves as uncertainty increases.