A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems

TL;DR

This paper introduces a hybrid bio-inspired algorithm combining bacterial foraging and ant colony optimization to improve solutions for complex job shop scheduling problems, demonstrating superior performance over traditional methods.

Contribution

The paper proposes a novel Hybrid Bacterial Foraging Optimization algorithm that enhances job shop scheduling solutions compared to existing bacterial foraging methods.

Findings

Hybrid algorithm outperforms standard bacterial foraging in test problems.

Proposed method yields better scheduling solutions for real-world scenarios.

Hybrid approach is more effective for large and complex scheduling problems.

Abstract

Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop Scheduling Problem is categorized under popular scheduling problems. In this research work, Bacterial Foraging Optimization was hybridized with Ant Colony Optimization and a new technique Hybrid Bacterial Foraging Optimization for solving Job Shop Scheduling Problem was proposed. The optimal solutions obtained by proposed Hybrid Bacterial Foraging Optimization algorithms are much better when compared with the solutions obtained by Bacterial Foraging Optimization algorithm for well-known test problems of different sizes. From the implementation of this research work, it could be observed that the proposed Hybrid Bacterial Foraging Optimization was effective than Bacterial Foraging Optimization algorithm in solving Job Shop Scheduling Problems. Hybrid Bacterial Foraging Optimization is used to implement real world Job Shop Scheduling Problems.