Optimizing Cellular Manufacturing Systems Through Multi-Objective Cobot Coordination and Tool Allocation

Abstract

Objectives: This study aims to enhance cellular manufacturing systems by optimizing cobot and tool assignments, maximizing flexibility, and minimizing production time, workload imbalances, energy consumption, error rates and rework. Methods: This study employs a sophisticated multi-objective optimization approach, integrating constraints into the cellular manufacturing system using advanced linear or integer programming techniques. The model is designed to dynamically adapt in real-time, allowing for flexibility in response to evolving production needs. We systematically evaluate cobot and tool assignments, balancing conflicting objectives within a comprehensive mathematical framework. The optimization process is fine-tuned to consider machine capacities, part type assignments, and tool compatibility, ensuring the practicality and realism of the proposed solutions. The overarching goal is to identify optimal configurations that minimize production time, workload imbalances, energy consumption, error rates and rework while maximizing system adaptability. Findings: The optimal cobot and tool assignments, determined through the multi-objective optimization model, yielded substantial improvements across critical metrics compared to a scenario without cobots. This data showcases a 26% reduction in production time, a 20% decrease in workload imbalance, a 20% improvement in flexibility, a 28% reduction in energy consumption, and a 26% decrease in error rates and rework when utilizing the proposed multi-objective optimization approach. These tangible improvements underscore the practical benefits of integrating cobots in cellular manufacturing systems.Top of Form Novelty: This study introduces a novel multi-objective optimization approach for cellular manufacturing, enhancing adaptability and efficiency through strategic cobot and tool assignments. Keywords: Cellular Manufacturing Systems, Cobots, Tool Assignment, Multi­Objective Optimization, Production Time, Workload Balancing, Energy Consumption, Error Rates, Flexibility