Abstract

The article addresses the current problem of selecting the optimal loading and unloading mechanism for logistics ground robotic complexes operating in direct combat engagement conditions. The full-scale invasion of the Russian Federation into Ukraine has led to a fundamental revision of approaches to logistics support organization, where traditional methods of delivering material and technical means result in critical losses of personnel and equipment. The aim of the study is to develop a scientifically grounded approach to evaluating and selecting the optimal model of loading and unloading mechanism for integration into logistics ground robotic complexes.

A scenario-adaptive methodology for multi-criteria evaluation of alternatives based on the Simple Additive Weighting method is proposed. A system of nine efficiency indicators has been developed, covering productivity, reliability, maintainability, concealment, level of automation, survivability, load capacity, efficiency coefficient, and mechanism mass. The key feature of the approach is a hybrid decision-making model that combines technical excellence assessment through an integral indicator with economic feasibility verification using a relative cost coefficient.
The recommended range of this coefficient k=0.2-0.3 has been substantiated. The methodology provides a mechanism for justified deviation from formal assessment results in the presence of significant operational-tactical factors.
The methodology’s operability is demonstrated through a model example comparing four types of mechanisms.

Integration of the developed approach with simulation modeling and practical testing under real operational conditions is identified as a promising direction for further research.

Keywords: logistics ground robotic complexes; loading and unloading mechanism; multi-criteria evaluation; Simple Additive Weighting method; concealment; survivability; economic feasibility; military logistics.

FULL TEXT (in Ukrainian)

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The article was submitted 26.11.2025.
© Korkin O., Bryzhenyuk Yu., 2025
Creative Commons Attribution 4.0 International License (CC BY 4.0)