Comparative Analysis of Robotic and Automatic Warehousing Systems with Fuzzy-Based Multi-Criteria Decision Making

Key findings from this study

• The study found that automated systems outperform manual alternatives in productivity, safety, and annual operating costs.
• The authors report that manpower-based warehousing retains advantages in operational flexibility and reduced initial investment requirements.
• The researchers demonstrate that multi-method fuzzy MCDM integration with sensitivity analysis enhances the robustness of warehouse automation decision outcomes.

Overview

This study applies an integrated fuzzy multi-criteria decision-making (MCDM) framework to evaluate automated versus manpower-based warehousing systems. Four established fuzzy MCDM methods—Fuzzy EDAS, Fuzzy TOPSIS, Fuzzy AHP, and Fuzzy VIKOR—operate within a single evaluation structure to enable comprehensive comparison across competing alternatives.

Methods and approach

The framework evaluates six core criteria: productivity, safety, flexibility, initial investment, annual expense, and error rate. Fuzzy EDAS and Fuzzy VIKOR underwent targeted sensitivity analysis to test ranking robustness across alternative weighting scenarios. Multi-method integration strengthens result credibility by triangulating findings across distinct algorithmic approaches.

Results

Automated warehousing systems demonstrated superior performance in productivity, safety, and long-term operating expenses relative to manpower-based alternatives. Manpower-based systems retained advantages in flexibility and lower initial capital requirements. Sensitivity analysis confirmed that Fuzzy EDAS and Fuzzy VIKOR rankings remained stable under alternative weighting scenarios, supporting the robustness of the comparative conclusions across both system categories.

Implications

Warehouse managers evaluating automation investments gain a structured decision framework that accounts for competing performance and financial objectives under uncertainty. The integration of multiple fuzzy MCDM methods mitigates method-specific biases and strengthens confidence in automation recommendations. Organizations must weigh long-term operational cost reductions against substantial upfront capital allocation and flexibility constraints when transitioning to automated systems.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.