Work

Enhanced Decision Support for Multi-Objective Factory Layout Optimization: Integrating Human Well-Being and System Performance Analysis

Year: 2024

Type: article

Abstract: This paper presents a decision support approach to enable decision-makers to identify no-preference solutions in multi-objective optimization for factory layout planning. Using a set of trade-off solutions for a battery production assembly station, a decision support method is introduced to select three solutions that balance all conflicting objectives, namely, the solution closest to the ideal point, the solution furthest from the nadir point, and the one that is best performing along the ideal nadir vector. To further support decision-making, additional analyses of system performance and worker well-being metrics are integrated. This approach emphasizes balancing operational efficiency with human-centric design, aligning with human factors and ergonomics (HFE) principles and Industry 4.0–5.0. The findings demonstrate that objective decision support based on Pareto front analysis can effectively guide stakeholders in selecting optimal solutions that enhance both system performance and worker well-being. Future work could explore applying this framework with alternative multi-objective optimization algorithms.

Source: Applied Sciences

DOI: 10.3390/app142210736

Authors: Andreas Lind, Veeresh Elango, Sunith Bandaru, Lars Hanson, Dan Högberg

Institutions: Scania (Sweden), University of Skövde, Volvo (Sweden)

Language: English

Topic: Advanced Manufacturing and Logistics Optimization

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,806