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Fuzzy-set qualitative comparative analysis applied to the design of a network flow of automated guided vehicles for improving business productivity

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  • Llopis-Albert, Carlos
  • Rubio, Francisco
  • Valero, Francisco

Abstract

Designing efficient warehouse management systems is essential to improve business performance. The use of autonomous guided vehicles (AGVs) in logistic processes and material handling systems (MHS) improves productivity and reduces costs. However, determining the appropriateness and financial feasibility of acquiring a fleet of AGVs, together with the definition of their path layout, routing schemes, operation tasks, and network flow, becomes a complex problem when designing flexible manufacturing systems (FMS). This study aids the design of a fleet of AGVs by means of a fuzzy-set qualitative comparative analysis (fsQCA), which makes it possible to measure the level of satisfaction of managerial decision makers. It enables us to identify a combination of factors that lead to stakeholders' satisfaction while dealing with uncertain environments due to the heterogeneous nature of decision makers and factors. Our methodology has been applied to multi-criteria decision-making analysis, resulting in greater transparency, fairness, social equity, and consensus among stakeholders.

Suggested Citation

  • Llopis-Albert, Carlos & Rubio, Francisco & Valero, Francisco, 2019. "Fuzzy-set qualitative comparative analysis applied to the design of a network flow of automated guided vehicles for improving business productivity," Journal of Business Research, Elsevier, vol. 101(C), pages 737-742.
    • Handle: RePEc:eee:jbrese:v:101:y:2019:i:c:p:737-742
    DOI: 10.1016/j.jbusres.2018.12.076
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    Citations

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    Cited by:

    1. Rubio, Francisco & Llopis-Albert, Carlos & Valero, Francisco, 2021. "Multi-objective optimization of costs and energy efficiency associated with autonomous industrial processes for sustainable growth," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    2. Igor Taran & Asem Karsybayeva & Vitalii Naumov & Kenzhegul Murzabekova & Marzhan Chazhabayeva, 2023. "Fuzzy-Logic Approach to Estimating the Fleet Efficiency of a Road Transport Company: A Case Study of Agricultural Products Deliveries in Kazakhstan," Sustainability, MDPI, vol. 15(5), pages 1-14, February.
    3. Snežana Tadić & Mladen Krstić & Svetlana Dabić-Miletić & Mladen Božić, 2023. "Smart Material Handling Solutions for City Logistics Systems," Sustainability, MDPI, vol. 15(8), pages 1-26, April.
    4. Llopis-Albert, Carlos & Palacios-Marqués, Daniel & Simón-Moya, Virginia, 2021. "Fuzzy set qualitative comparative analysis (fsQCA) applied to the adaptation of the automobile industry to meet the emission standards of climate change policies via the deployment of electric vehicle," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    5. Llopis-Albert, Carlos & Rubio, Francisco & Valero, Francisco, 2021. "Impact of digital transformation on the automotive industry," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    6. Baihui Jin & Wei Li, 2023. "External Factors Impacting Residents’ Participation in Waste Sorting Using NCA and fsQCA Methods on Pilot Cities in China," IJERPH, MDPI, vol. 20(5), pages 1-21, February.
    7. Lihle N. Tikwayo & Tebello N. D. Mathaba, 2023. "Applications of Industry 4.0 Technologies in Warehouse Management: A Systematic Literature Review," Logistics, MDPI, vol. 7(2), pages 1-19, April.
    8. McLeay, Fraser & Olya, Hossein & Liu, Hongfei & Jayawardhena, Chanaka & Dennis, Charles, 2022. "A multi-analytical approach to studying customers motivations to use innovative totally autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 174(C).

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