Heuristic approaches for solving a bilevel optimistic scheduling problem on parallel machines

TL;DR

This paper introduces novel heuristic algorithms, including a Recovering Beam Search and a Multi-Start Local Search, for solving a bilevel optimistic scheduling problem on parallel machines, with automated parameter tuning.

Contribution

It develops a bilevel-specific branching scheme and hybrid heuristics, including Bayesian optimization for parameter selection, for efficient scheduling solutions.

Findings

Effective heuristics for large instances up to 500 jobs and 10 machines.

The proposed methods outperform traditional approaches in computational experiments.

Automated parameter tuning improves heuristic performance and reduces computational effort.

Abstract

This work addresses the uniform parallel machine scheduling problem within an optimistic bilevel optimization framework. The leader seeks to minimize the weighted number of tardy jobs, while the follower aims to minimize the total completion time across a set of uniform machines. The hierarchical decision-making process of the bilevel problem makes designing effective heuristics challenging. To tackle this, we exploit a property of the follower that enables the construction of optimal schedules. From this property, we derive an effective branching scheme that simultaneously accounts for both leader and follower decisions. This branching scheme allows us to design a Recovering Beam Search (RBS), which represents a significant contribution from a bilevel perspective. Then we propose a Multi-Start Local Search (MSLS) algorithm based on an innovative scheme that couples the RBS and a Local Search (LS). To the best of our knowledge, while hybridizing beam search with local search is known in other contexts, our approach of leveraging a bilevel-specific branching scheme to efficiently explore the search space is novel. Moreover, we propose an automated method for determining heuristic parameters via Bayesian optimization. This reduces computational resource requirements and yields better parameters than the usual empirical approach. Finally, computational experiments are presented for instances with up to 500 jobs and 10 machines.