Scheduling a single machine with compressible jobs to minimize maximum lateness

TL;DR

This paper introduces a model for scheduling jobs on a single machine where job processing times can be compressed at a cost, aiming to minimize maximum lateness within resource constraints, and provides an optimal solution approach.

Contribution

It extends traditional scheduling models by incorporating compressible processing times and develops an algorithm for optimal scheduling under resource limits.

Findings

Compression of specific jobs reduces maximum lateness.

Optimal solutions can be found given sufficient additional resources.

Feasible solutions can be adapted when resources are limited.

Abstract

The problem of scheduling non-simultaneously released jobs with due dates on a single machine with the objective to minimize the maximum job lateness is known to be strongly NP-hard. Here we consider an extended model in which the compression of the job processing times is allowed. The compression is accomplished at the cost of involving additional emerging resources, whose use, however, yields some cost. With a given upper limit $U$ on the total allowable cost, one wishes to minimize the maximum job lateness. It is clear that, by using the available resources, some jobs may complete earlier and the objective function value may respectively be decreased. As we show here, for minimizing the maximum job lateness, by shortening the processing time of some specially determined jobs, the objective value can be decreased. Although the generalized problem is harder than the generic non-compressible version, given a ``sufficient amount'' of additional resources, we can solve the problem optimally. We determine the compression rate for some specific jobs and develop an algorithm that obtains an optimal solution. Such an approach can be beneficial in practice since the manufacturer can be provided with an information about the required amount of additional resources in order to solve the problem optimally. In case the amount of the available additional resources is less than used in the above solution, i.e., it is not feasible, it is transformed to a tight minimal feasible solution.