High-Performance Computing for Scheduling Decision Support: A Parallel Depth-First Search Heuristic

TL;DR

This paper introduces a parallel depth-first search heuristic for complex scheduling problems, leveraging parallel computing to improve solution quality and reduce computation time significantly.

Contribution

It presents a novel parallel heuristic for NP-hard scheduling problems with setup times, exploiting parallel architectures for enhanced performance.

Findings

Heuristic computes near-optimal solutions for large instances.

Parallelization significantly reduces computation time.

Approach effectively utilizes parallel architectures for scheduling heuristics.

Abstract

Many academic disciplines - including information systems, computer science, and operations management - face scheduling problems as important decision making tasks. Since many scheduling problems are NP-hard in the strong sense, there is a need for developing solution heuristics. For scheduling problems with setup times on unrelated parallel machines, there is limited research on solution methods and to the best of our knowledge, parallel computer architectures have not yet been taken advantage of. We address this gap by proposing and implementing a new solution heuristic and by testing different parallelization strategies. In our computational experiments, we show that our heuristic calculates near-optimal solutions even for large instances and that computing time can be reduced substantially by our parallelization approach.