Title
A unified view of parallel machine scheduling with interdependent processing rates
Document Type
Article
Publication Title
Journal of Scheduling
Abstract
In this paper, we are concerned with the problem of scheduling n jobs on m machines. The job processing rate is interdependent and the jobs are non-preemptive. During the last several decades, a number of related models for parallel machine scheduling with interdependent processing rates (PMS-IPR) have appeared in the scheduling literature. Some of these models have been studied independently from one another. The purpose of this paper is to present two general PMS-IPR models that capture the essence of many of these existing PMS-IPR models. Several new complexity results are presented. We discuss improvements on some existing models. Furthermore, for an extension of the two related PMS-IPR models where they include many resource constraint models with controllable processing times, we propose an efficient dynamic programming procedure that solves the problem to optimality.
First Page
499
Last Page
515
DOI
10.1007/s10951-019-00605-x
Publication Date
10-1-2019
Recommended Citation
Alidaee, Bahram; Wang, Haibo; Kethley, R. Bryan; and Landram, Frank, "A unified view of parallel machine scheduling with interdependent processing rates" (2019). Business Faculty Publications. 24.
https://rio.tamiu.edu/arssb_facpubs/24