Algorithmic framework for strategic robotics procurement in academia: An integrated optimization approach using GBWM and VIKOR

Omer Bafail; Adnan Miski; Omer Bafail; Adnan Miski

doi:10.3934/jimo.2026073

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 4: 1981-2013. doi: 10.3934/jimo.2026073

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Research article

Algorithmic framework for strategic robotics procurement in academia: An integrated optimization approach using GBWM and VIKOR

Omer Bafail ^{1
,
,},
Adnan Miski ²

1.
Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2.
Department of Industrial Engineering, Faculty of Engineering-Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Received: 30 November 2025 Revised: 03 February 2026 Accepted: 26 February 2026 Published: 23 March 2026
90B50, 90C29

This study addresses a critical challenge in strategic decision-making and resource allocation: The unsystematic procurement of single-arm robotic systems for university curricula. This study introduces a dual-stage computational framework designed to transform qualitative pedagogical requirements into a rigorous mathematical optimization model. The proposed method innovatively synthesizes the group best–worst method (GBWM) with the Visekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) algorithm to establish a stabilized decision-making architecture. The novelty of this approach lies in its ability to resolve the inherent "pedagogical economic paradox" by balancing high-precision technical performance with the stringent budgetary and safety constraints of academic environments, which remain unaddressed in standard industrial models. By leveraging the linear optimization principles of the GBWM, the framework minimizes the maximum absolute deviation in expert consensus, effectively reducing the "cognitive noise" prevalent in subjective procurement. The integration of VIKOR further enhances the model by using a compromise-ranking logic that penalizes individual criterion regret, ensuring that no single academic priority is sacrificed for the overall performance. The results quantified through this methodology reveal that cost (48.4%) and repeatability (22%) are the primary determinants, a prioritization that diverges significantly from industrial benchmarks. This study provides a replicable algorithmic roadmap that empowers academic institutions to optimize resource management, demonstrating the strategic importance of applied mathematics in bridging the gap between engineering management and educational utility.
- educational robotics,
- GBWM,
- VIKOR,
- compromise solution,
- applied mathematics,
- algorithmic design and analysis,
- optimization,
- MCDM
Citation: Omer Bafail, Adnan Miski. Algorithmic framework for strategic robotics procurement in academia: An integrated optimization approach using GBWM and VIKOR[J]. Journal of Industrial and Management Optimization, 2026, 22(4): 1981-2013. doi: 10.3934/jimo.2026073

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Abstract

This study addresses a critical challenge in strategic decision-making and resource allocation: The unsystematic procurement of single-arm robotic systems for university curricula. This study introduces a dual-stage computational framework designed to transform qualitative pedagogical requirements into a rigorous mathematical optimization model. The proposed method innovatively synthesizes the group best–worst method (GBWM) with the Visekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) algorithm to establish a stabilized decision-making architecture. The novelty of this approach lies in its ability to resolve the inherent "pedagogical economic paradox" by balancing high-precision technical performance with the stringent budgetary and safety constraints of academic environments, which remain unaddressed in standard industrial models. By leveraging the linear optimization principles of the GBWM, the framework minimizes the maximum absolute deviation in expert consensus, effectively reducing the "cognitive noise" prevalent in subjective procurement. The integration of VIKOR further enhances the model by using a compromise-ranking logic that penalizes individual criterion regret, ensuring that no single academic priority is sacrificed for the overall performance. The results quantified through this methodology reveal that cost (48.4%) and repeatability (22%) are the primary determinants, a prioritization that diverges significantly from industrial benchmarks. This study provides a replicable algorithmic roadmap that empowers academic institutions to optimize resource management, demonstrating the strategic importance of applied mathematics in bridging the gap between engineering management and educational utility.

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