Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks

TL;DR

This paper introduces a novel climbing depth-bounded adjacent discrepancy search method for efficiently solving complex multiprocessor task scheduling problems in hybrid flow-shop environments, especially effective for large instances.

Contribution

It presents a new search algorithm tailored for NP-hard hybrid flow-shop scheduling with multiprocessor tasks, improving solution efficiency over existing methods.

Findings

Effective for large-size problems

Outperforms existing solutions in computational tests

Provides a new approach for complex scheduling problems

Abstract

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The problem even in its simplest form is NP-hard in the strong sense. The great deal of interest for this problem, besides its theoretical complexity, is animated by needs of various manufacturing and computing systems. We propose a new approach based on limited discrepancy search to solve the problem. Our method is tested with reference to a proposed lower bound as well as the best-known solutions in literature. Computational results show that the developed approach is efficient in particular for large-size problems.