• Svetlana I. Kolesnikova, St.Petersburg State University of Aerospace Instrumentation (St. Petersburg, Russia)
• Anastasiya A. Fomenkova, St.Petersburg State University of Aerospace Instrumentation (St. Petersburg, Russia)
• Viktor V. Polyakov, St.Petersburg State University of Aerospace Instrumentation (St. Petersburg, Russia)

A problem of control assignment of works with a large dynamically replenished (changed) volume of them is considered. Despite existing tools for automating large-scale software development (AGILE products, Waterfall, and others), the challenge of promptly and correctly intervening in their production process under changing external and internal conditions remains acute due to the resulting negative consequences (project delays, budget overruns, and unfinished projects due to unsatisfactory quality). The most pressing issue in large projects is the optimal assignment of work in accordance with the established current competencies of employees. However, several studies (such as surveys from Scrum Inc.) have noted that the relationship between the implementation of the most popular Agile products and increased software development efficiency is inconsistent. For this reason, developing improvements to existing tools is relevant for managing the production of large software projects, where interrelated resource constraints time, cost, and computational are particularly acute. The objective of this study is to present a model and its implementing algorithm for optimizing the work assignment process with a focus on big data (from the perspective of the developer), resulting in exponential gains in time and resources when comparing options in the form of alternatives (job, worker). A distinctive feature of the model is an optimal algorithm for ranking a large number of options, executed in real time, and resulting in a virtually unimprovable correct solution to the problem of sorting a dynamically replenished set of alternatives, which corresponds to the most important principle of “here and now” development, consisting of an immediate response to changing conditions and customer requirements. The gain is achieved through a non-mechanical combination of three methods: the classical paired comparison method, its modification, correct in the sense of K. Arrow's axiom on the independence of choice (preferences) from previously achieved rankings, and the selected algorithm for sorting the numerical sequence. Examples of numerical simulation are provided, confirming the declared characteristics of the algorithm for selecting the optimal alternative (job, worker) in a big data environment.

poorly formalizable object, decision-making algorithms, project management, dynamic work assignment model, big data, modification of the pairwise comparison method for big data, collective of evaluation algorithms, Markov chain

2026-06-05