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June 19, 2026Open Access

A Comparative Study of Hybrid Metaheuristics for the Flexible Job Shop Scheduling Problem: From SA-GA Operator Analysis to Multi-Algorithm Evaluation

Key Points

This study aims to compare various hybrid metaheuristic algorithms for solving the Flexible Job Shop Scheduling Problem effectively.
Developed a two-phase comparative study with hybridization of Simulated Annealing and Genetic Algorithm operators.
Evaluated additional algorithms including Ant Colony Optimization, Memetic Algorithms, Harmony Search, and Variable Neighborhood Search.
Applied hybrid combinations across eight instances of the Brandimarte benchmark dataset.
Identified hybrid pairings that consistently minimized makespan across diverse problem instances.
Demonstrated parameter sensitivity and operator design significantly impacted solution quality.
Provided insights for selecting algorithms based on complexity and scale of scheduling problems.

Abstract

This paper presents a two-phase comparative study of hybrid metaheuristic algorithms for solving the Flexible Job Shop Scheduling Problem (FJSSP). In Phase 1, we analyze the hybridization of Simulated Annealing (SA) with Genetic Algorithm (GA) operators — examining parameter sensitivity and the effect of selection, crossover, and mutation operator design on solution quality. In Phase 2, we broaden the comparison to include Ant Colony Optimization (ACO), Memetic Algorithms (MA), Harmony Search (HS), and Variable Neighborhood Search (VNS), evaluating hybrid combinations across eight instances of the Brandimarte benchmark dataset (MK01–MK08). Results identify which hybrid pairings consistently achieve competitive makespan minimization across problem instances of varying scale and complexity, and provide practical insights into algorithm selection for production scheduling applications.

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