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The purpose of this paper is to determine and evaluate resource allocation algorithms for mixed‐structure operation systems with unknown parameters characterized by experts using the formalism of C‐uncertain variables.

Aggregation and decomposition of mathematical models of operations, performed using analytical‐numerical optimization methods, lead to serial and parallel structures for which allocation algorithms are known. Evaluation of allocations carried out by means of a computer simulation.

Resource allocation problems for mixed‐structure operation systems may be solved by applying aggregation, decomposition and allocation algorithms determined for simple structures. Allocation algorithms based on C‐uncertain variables outperform these based on basic uncertain variables.

Application of the presented algorithms is limited to some mixed structures, however, the methodology used appears general enough to allow developing algorithms for other mixed structures.

The algorithms developed may be embedded into a knowledge‐based system supporting management‐level decisions concerning optimal distribution of limited financial resources.

Originally determined rules for aggregation and decomposition, as well as resulting allocation algorithms. The presented methodology seems promising for developing a general resource allocation algorithm – valid for any mixed structure of an operation system.

The purpose of this paper is to present results concerning resource distribution problems for a class of production systems composed of production operations described by relations with uncertain parameters – for newly considered serial‐parallel and parallel‐serial structures.

The resource distribution problems are formulated. For the typical mathematical model of a single production operation, the resource distribution algorithms are derived analytically or, when that is impossible, numerical procedures for finding solutions to distribution problems are suggested.

The uncertain variables have been proved as a convenient tool for solving specific optimization problems in production management based on relational knowledge representation with unknown parameters.

The paper presents analytical resource distribution and numerical algorithms for determining the optimal resource distribution, which may be applied in knowledge‐based management‐level decision systems for serial‐parallel and parallel‐serial structures of production systems.