Time-flexible min completion time variance in a single machine by quadratic programming

In the context of job scheduling, the time required to complete a task is related not only to the intrinsic difficulty of the task, but also to the operator's willingness to speed up (or slow down) its execution. In fact, service operators are sometimes authorized to flexibly calibrate job processing times when this is beneficial for the efficient design of services and production plans. In this paper, we show that some forms of time flexibility have major consequences on the operator's ability to efficiently solve the problem of scheduling non-preemptive jobs to minimize the variance of their completion times. In fact, although this remains a challenging combinatorial problem, authorizing forms of processing time flexibility allows for solving it up to optimality by convex quadratic programming approaches, with a view to tackling large-scale instances, where no exact algorithm can be applied. Our contribution allows establishing a form of least flexibilization of job processing times while guaranteeing the solvability of the resulting problem by convex quadratic programming approaches. To this end, we provide novel bounding conditions for the characterization of an optimal sequence that strengthen and integrate state-of-the-art dominance properties. Our numerical tests indicate that this new methodology is capable of approaching the solution of the original min completion time variance problem with a max relative difference of about 0 . 05% (on average), with respect to the time-flexible solution.& COPY; 2023 Elsevier B.V. All rights reserved.

Automated summary

In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.