Modeling the Transformation of Configuration Management Processes in a Multi-Project Environment

Abstract

Human resource management during project implementation in a multi-project environment requires addressing the resource-constrained project scheduling problem. Agile methodologies allow for greater management flexibility, necessitating an agile transformation of human resource management processes. Changes occurring in human resource management lead to modifications in the initial project team and alterations in the state of the resource pool in a multi-project environment. To ensure controllable changes in the project team and address the task of allocating (reallocating) limited resources among project tasks in a multi-project environment with subsequent optimization based on a selected criterion, it is proposed to use configuration management of human resources. Depending on the chosen level of detail, project specifics, and the implementation environment, configuration elements can be an executor, project team, or intact team. Types of equivalence applied to the set of configuration elements have been classified. A model of the configuration management process for human resources has been considered. Using the proposed model will allow for formalizing the process of implementing human resource configuration management in a multi-project environment. Constructive enumeration of configuration elements in a multi-project environment has been examined. Identifying a typical representative of the configuration and considering the given equivalence, followed by selecting a resource allocation/reallocation option that meets the specified constraints, enhances team adaptability. An example of configuration management in addressing team composition management tasks has been discussed. The proposed approach can be applied in managing human resources for agile transformation projects of critical infrastructure, particularly in the healthcare sector, during the establishment of hospital clusters and supercluster medical institutions. This is because implementing such projects necessitates continuous monitoring of changes and requirements for resource provisioning.

1. Introduction

In the modern business ecosystem, project management has undergone significant transformations driven by technological advancements and the global push toward sustainability [1]. Once linear and compartmentalized, projects have evolved into intricate webs of interdependencies, necessitating a more sophisticated approach to resource allocation and management [2]. While presenting numerous opportunities, this evolution also brings forth challenges that traditional methodologies need to be equipped to address [3]. The actuality of the present research emerges from this very context and aims to provide a fresh perspective on configuration management techniques tailored to the complexities of the 21st-century project landscape.

Among the tasks facing managers, based on the labor market analysis by the company “Deloitte” for 2022–2023, one can highlight ensuring the continuity of work processes, ensuring technology and team efficiency, flexible workload management, reviewing the quantitative and qualitative composition of human resources, and revising the organizational structure [4,5].

To ensure the sustainable development of companies, according to the study “2023 Deloitte Human Capital Trends: New Fundamentals for a Boundaryless World” [6], it is necessary to consider risks that significantly affect the development of the global workforce: political instability, migration, education and the viability of the workforce, economic inequality, state intervention at the level of labor legislation, cyber risks related to personal information, aging, and a reduction in the workforce. The risks associated with political instability and labor resource migration come first in Ukraine. Ensuring the viability of companies requires agile transformation of human resource management processes to promptly respond to emerging resource needs, which is especially relevant for critical infrastructure and healthcare institutions.

The increase in information, especially about project resource management, leads to the growth of the project’s digital footprint (active, passive trace, digital shadow) [7]. The application of the concentric model of the project’s digital footprint proposed by Bushuyev, S., Bushuyeva, V., and Zasukha, I., based on the use of genomic project management methodology in a multi-project environment, requires the development and implementation of a resource configuration management system.

Glaudia Califano and David Spinks [8] focus on adapting agile approaches, considering organizations’ specifics, and applying appropriate adaptation mechanisms. It should be noted that the proposed recommendations for implementing agile adaptation are effective for small projects, but when applied at the industry level (medical industry, critical infrastructure, education, etc.) without using formalized models and developing elements of configuration and coordination management, project implementation efficiency decreases.

The use of frameworks such as SAFe [9], Lean [10], and DevOps tools contributes to the stable functioning of the organization. However, it requires adaptation when deploying agile transformation projects of project management processes under defined constraints in a volatile and aggressive environment in the public sector (healthcare institutions, critical infrastructure enterprises, educational institutions).

Block, S. [11] notes that the “Digital transformation places a variety of demands on organizations and requires a rethinking of organizational structures”. An example of the need for organizational transformation and the introduction of flexible management is the infrastructure stage of healthcare reform in Ukraine (creation of supercluster hospitals, hospital districts, and hospital clusters according to the Resolution of the Cabinet of Ministers of Ukraine dated 28 February 2023 No. 174) [12]. The global emphasis on sustainable practices further magnifies the urgency of this research. As businesses grapple with the dual objectives of operational efficiency and sustainable contributions, there is a palpable need for innovative strategies that can harmoniously blend these goals [13]. Traditional resource allocation techniques, while foundational, often need more flexibility and foresight for today’s dynamic and sustainability-driven projects [14]. This research seeks to address this lacuna, offering a novel approach to configuration management that is adept at handling the multifarious challenges of modern projects and aligns seamlessly with the broader sustainability objectives. In essence, this research stands at the confluence of efficiency and sustainability and aims to redefine the paradigms of contemporary project management.

Changes occurring during the implementation of a project portfolio lead to alterations in project resource provisioning. Special attention must be paid to issues of configuration information, both for the product and the project, to ensure transformational mechanisms when managing the human resources of project-oriented companies [15,16]. Barbosa, A.P.F.P.L., et al. also highlight the appropriateness of scaling applied practices depending on the current project configuration [17]. When managing project human resource provisioning, it is essential to consider project requirements, constraints imposed by project specifics, the current state of the resource pool, and the required resource profile of the project [18]. Managing resources in safety-oriented systems introduces additional constraints related to safety requirements: qualification level, information access level, reliability, resilience, etc. Specialized Human Resource Information Systems (HRIS) support resource-related decision making and reduce the influence of subjective factors in such projects [19]. Post-COVID-19 labor market trends and military actions demonstrate the need for functional resource redundancy with the potential for subsequent flexible adaptation of project teams, automation, and digitization of resource management processes [20,21]. The review by Robert Pellerin et al. [22] of hybrid methods based on metaheuristic strategies identified practical approaches to solving the resource-constrained project scheduling problem (RCPSP). Xie, L.-L. et al. [23] proposed a comprehensive approach to solving the RCPSP based on the application of DM technology and the genetic algorithm (GA). However, the authors note the efficiency of method application depends on project specifics and the dimensionality of the problem being solved. The importance of the self-adaptation strategy is also highlighted. Lu, Z. and Chen, C. [24] justify developing resource flexibility to enhance problem-solving efficiency using a hybrid multi-objective optimization algorithm.

It should be noted that the discussed methods do not consider principles of adaptability and functional redundancy when forming teams in a multi-project environment. The limiting factor in applying known approaches is the need to account for prohibitions on combinations, fixed combinations, etc. Issues of redistributing functions among team members with the potential involvement of additional resources from the company’s resource pool lead to the need to monitor changes for further optimization of the organizational structure. Berntzen, M. et al. [25], Madampe, K., and Grundy, J. [26] emphasize the relevance of applying taxonomy to describe human resource management processes.

Thus, the analysis of scientific publications has shown that ensuring company operation in a post-COVID-19 environment is relevant to transforming resource management processes.

This article aims to develop a conceptual foundation for transforming human resource management processes in the context of war and the COVID-19 pandemic based on the application of configuration resource management in a multi-project environment.

In this study, we explore the utilization of configuration management within a multi-project milieu to address the resource-constrained project scheduling problem. We propose a novel classification schema for configuration elements grounded in various forms of equivalence, thereby enabling the application of formal transformations across a spectrum of human resource configurations in a multi-project context. We delve into the configuration enumeration of representative archetypes of configuration elements and derive estimates for the number of configuration elements. Furthermore, we elucidate an illustrative case of employing configuration management to tackle the challenge of composition management. This contribution aims to enhance the existing body of knowledge by providing a comprehensive framework for managing human resources in a multi-project environment, ultimately aiding project managers and practitioners in optimizing resource allocation and project scheduling.

The foundation of the work is based on the formulated hypotheses:

• The application of configuration management for the project team enhances the efficiency of human resource management in a multi-project environment;
• Since the resource-constrained project scheduling problem is an NP-hard problem, there is a need for aggregation of configuration elements to solve the problem, followed by decomposition of the obtained solutions;
• Constructive enumeration of configuration elements allows for the identification of typical representatives considering the type of equivalence;
• The formation of the project team is carried out considering the specified constraints based on the constructive enumeration of configuration elements.

2. Background

In today’s rapidly evolving business landscape, projects have become more intricate, demanding a nuanced approach to resource allocation and management. The complexity of modern projects and the dynamic nature of team compositions and stakeholder expectations underscores the pressing need for advanced configuration management techniques [27]. Beyond the immediate complexities, there is an emergent emphasis on sustainability, ensuring that projects meet their immediate objectives and contribute positively to long-term environmental, social, and economic goals [28]. The actuality of this research lies in its potential to address these complexities, offering a structured methodology to navigate the multifaceted realm of human resource allocation within projects while also integrating sustainable practices.

Traditional methods of resource allocation, foundational as they are, often fall short of addressing the unique challenges posed by contemporary projects. These challenges range from fluctuating stakeholder requirements to integrating cross-functional teams, each bringing its own expertise and expectations [29]. Moreover, sustainability adds another layer of complexity, necessitating resource allocation that minimizes environmental impact, promotes social equity, and ensures economic viability [30]. In such a context, the inability to adapt and reconfigure team compositions and responsibilities can lead to inefficiencies, cost overruns, and missed project milestones [31].

Hence, the emphasis on constructive enumeration of organizational structure elements and identifying typical representatives based on equivalence types is not merely academic but has profound practical and sustainable implications.

As organizations increasingly adopt a multi-project approach, the interdependencies between projects further complicate resource allocation [32]. In such environments, the flexibility to redistribute tasks and responsibilities becomes paramount. This research’s focus on configurations with the highest distribution variants offers a promising solution, ensuring that teams remain agile, adaptable, and sustainable in changing project dynamics. The global shift toward digital transformation and sustainability further accentuates the need for configuration management integrated with software solutions, presenting a timely and relevant avenue for exploration [33]. This integration promises to revolutionize the way organizations approach project management, ensuring that projects are efficient and resonate with sustainability principles.

Configuration management, while pivotal in project management, must also align with the broader sustainability goals of organizations [34]. This alignment ensures that human resource allocation within projects optimizes efficiency and contributes to long-term sustainable outcomes. By integrating sustainability into the core of configuration management, organizations can ensure that their projects are future-proof and resilient and contribute positively to global sustainability objectives [35].

In essence, the actuality of this research is anchored in its potential to bridge the gap between traditional resource allocation methodologies and the evolving demands of modern, sustainable projects. This research paves the way for more efficient, adaptable, and environmentally conscious project management solutions by offering a comprehensive framework for configuration management that integrates sustainability.

3. Materials and Methods

The reasons for changes in human resource provisioning can be external to the project and internal and can have a reactive or proactive nature. Each change can be described using a set of parameters that determine the configuration of the project team:

• Project name;
• Nature of the change;
• Initiator;
• Character of the change depending on the initiator (within the team/within the organization/external change);
• Character of the change depending on the cause (reactive/proactive);
• Impact on processes;
• Criticality;
• Timelines, etc.

If an executor possesses critical competencies at the company portfolio level, they can be considered a configuration element in a multi-project environment. In the proactive management of human resource provisioning during the team formation stage, it is necessary to anticipate the team’s adaptability through functional redundancy. When forming the company’s resource pool, it is essential to consider the possibility of functional redundancy for project team personnel and administrative staff (specifically PMO). This approach will help mitigate the threat of losing critical knowledge due to migration processes.

Figure 1 presents the model of the configuration management process for project resources.

The process of managing project human resource configurations encompasses the identification of the resource management process configuration, creating the project’s baseline configuration, developing a project configuration management plan, and monitoring configurations during project execution.

Inputs to the process include information about project resources, data regarding the resource pool, and requirements for configuration changes. Utilizing the configuration management information system and corporate standards allows the Project Management Office (PMO) and human resource management specialists to determine configuration requirements, create descriptions of baseline resource configurations, and develop configuration management plans, configuration changes, and reports. The results can be applied in implementing agile transformation projects of human resource management processes in a multi-project environment.

When implementing projects in a multi-project environment, configuration changes occur:

• Alteration of the project portfolio structure;
• Modification of the profile of critical competencies;
• Adjustment of the resource pool;
• Transformation of the project team;
• Revision of the human resource management policy within the project.

Due to changes in the conditions of the multi-project environment’s operation, new configuration elements may be introduced into the resource pool. Depending on the proposed level of aggregation, the configuration element can be represented by the project team (when considering a multi-project environment), an executor within a specific project team, or an intact team (an indivisible group of executors).

The specifics of applying configuration types are discussed in

Table 1. Applying configuration items.

Level of Detail	Configuration Element	Environment
Project/program/portfolio	Project team	Multi-project
Team	Executor	Project-based/multi-project
Project	Intact team	Multi-project/project-based

.

A graph model, a particular type of bipartite (two-part) graph, can be used to visualize the configuration element. As proposed in [36], we will call the type of graphs Org (V¹, V², R) as graphs in which the set of vertices is divided into two subsets: V¹ (corresponding to the set of executors) and V² (corresponding to the set of functions performed by the executors), with R representing the number of graph edges.

In general, Org (V¹, V², R) is a bipartite graph where the set of vertices is divided into two subsets V¹ = {v¹₁, …, v¹_n} and V² = {v²₁, …, v²_k} with neighborhoods of vertices, respectively. O(v¹₁), …, O(v¹_n) for the subset of vertices V¹ and O(v²₁), …, O(v²_k) for the subset of vertices V², possessing the following properties:

1.

n ≥ |O(v²_j)| ≥ 1, j = 1…k. For each function, at least one configuration element should be assigned (with a high degree of detail, one executor is assigned to each function; at the multi-project management level, a team/intact team is assigned to the project).

2.

k > |O(v¹_i)| ≥ 1, i = 1…n. Each executor must perform at least one function.

Analysis of configuration elements described by Org (V¹, V², R) graphs showed that they can be derived from a set of bipartite graphs by excluding graphs containing isolated vertices. In the project environment, project teams do not provide an excessive composition, ensuring cold, functional redundancy (i.e., team members not involved in the project are transferred to the “Available” status in the resource pool). Thus, the number of Org (V¹, V², R) graphs containing n, k vertices and R edges is determined by the number of bipartite graphs of size n, k, and the number of bipartite graphs that do not satisfy properties 1 and 2:

$$D_{n.k}^R=F_{n,k}^R-U_{n,k}^R,$$

(1)

where $D_{n,k}^R$ is the number of Org (V¹, V², R) graphs of a given dimension.

$F_{n,k}^R$ is the number of bipartite graphs of a given dimension.

$U_{n,k}^R$ is the number of bipartite graphs of a given dimension containing isolated vertices.

Let φ(Z(G), 1 + x) be a certain function, equivalent to the substitution of 1 + x^m in place of S_m in Z(G). Then, b_n,k(x) = ϕ(Z(S_n × S_k),1 + x), i.e.,:

$$b_{n,k}\left(x\right)=\phi \left(\frac{1}{n!k!}{{\displaystyle \sum }}_{\left(\alpha ,\beta \right)}^{}{{\displaystyle \prod }}_{r,t=1}^{n,k}{S}_{\left[r,t\right]}^{\left(r,t\right)}{j}_r\left(\alpha \right)j_t\left(\beta \right),1+x\right).$$

(2)

Introduce the function ψ, equal to the sum of the coefficients in b_n,k for the terms xⁱ, where i = R_min to R_max. If in ψ only one parameter R is specified, then R_min = R_max.

$$F_{n,k}^R={\psi }_R\left[\phi \left(\frac{1}{n!k!}{{\displaystyle \sum }}_{\left(\alpha ,\beta \right)}^{}{{\displaystyle \prod }}_{r,t=1}^{n,k}{S}_{\left[r,t\right]}^{\left(r,t\right)}{j}_r\left(\alpha \right)j_t\left(\beta \right),1+x\right)\right].$$

(3)

Violations of the properties of Org (V¹, V², R) graphs lead to isolated vertices in the graph. Since an isolated vertex does not participate in the distribution of edges, a graph of dimension n, k, containing R edges and m isolated vertices, degenerates into a graph of dimension n − m₁, k − m₂, where m = m₁ + m₂.

The number of bipartite graphs containing isolated vertices must be determined. It should be noted that the following scenarios are possible: |V¹| ≠ |V²|, i.e., n ≠ k; |V¹| = |V²|, i.e., n = k.

In the general form:

$$U_{n,k}^R=\left\{\begin{array}{c}{F}_{n-1,k}^R+F_{n,k-1}^R, n\ne k,\\ 2F_{n-1,k}^R-F_{n-1,k-1, }^{R}n\ne k.\end{array}\right..$$

(4)

Let λ = 1 when n = k and 0 otherwise. Introduce the notation:

$$\theta \left(n,k\right)=\phi \left(\frac{1}{n!k!}{{\displaystyle \sum }}_{\left(\alpha ,\beta \right)}^{}{{\displaystyle \prod }}_{r,t=1}^{n,k}{S}_{\left[r,t\right]}^{\left(r,t\right)}{j}_r\left(\alpha \right)j_t\left(\beta \right),1+x\right).$$

(5)

The final formula is given by:

$$D_{n,k}^R={\psi }_R\left[\theta \left(n,k\right)\right]-\left({\psi }_R\left[\theta \left(n-1,k\right)\right]+{\psi }_R\left[\theta \left(n,k-1\right)\right]-\lambda {\psi }_R\left[\theta \left(n-1,k-1\right)\right]\right).$$

(6)

The next stage of the constructive enumeration of Org graphs is the development of an algorithm for constructing a set of typical representatives. A group of performers or a group (denoted by G) is a subset of performers H = {h₁, ..., h_t} belonging to Q that implement a subset of functions F = {f₁, ..., f_t} belonging to A.

To determine typical representatives, we will consider the following equivalence classes (

Table 2. Classification of equivalence types of configuration items in a multi-project environment.

Type of Equivalence	Description	Environment
ST equivalence	Projects that have the same combination of involved stakeholders	Multi-project environment
STP equivalence	Two stakeholder groups, ST1 and ST2, are called STP equivalent ($S{T}^1\approx S{T}^2$) if they implement an identical project portfolio	Multi-project environment
F equivalence	Two groups of performers, G1 and G2, are termed F equivalent ($G^1\approx G^2$) if F1 = F2	Project/multi-project environment with standard projects and mass customization projects
H equivalence	Two groups, G1 and G2, are termed H equivalent ($G^1\approx G^2$) if H1 = H2. H equivalent types are described using a type of representative (composition of the group)	Project/multi-project environment when intact teams are simultaneously involved in multiple projects
ST equivalence	Projects that have the same combination of involved stakeholders	Multi-project environment

).

At the core of the constructive enumeration of organizational structure’s configurational elements lies the logical–combinatorial method for constructing formal models of team formation and functioning [37].

Configuration management of a project’s human resources reflects the adaptive capabilities of project teams in a multi-project environment. Considering the given equivalence, identifying a typical representative of the configuration allows for resource aggregation. Reducing data dimensionality by considering an intact team as a configurational element is pertinent since the task of resource allocation with specified constraints is NP-hard.

Distinguishing classes of equivalent typical configurational elements at the project team level will enable the selection of an optimal variant for resource distribution among tasks, taking into account specified criteria (cost, competence, reliability, etc.).

4. Results and Discussion

Let us consider the modeling of resource redistribution, considering H equivalence and F equivalence. The modified responsibility matrix, which reflects the capabilities of performers to execute a given function and the characteristics (cost) of the performers, is presented in

Table 3. Modified responsibility matrix.

Q/A	a1	a2	a3	a4	a5
q1	5	4	0	0	7
q2	0	7	6	0	0
q3	3	0	5	0	0
q4	4	0	0	0	5
q5	0	5	0	0	8
q6	7	0	0	5	0
q7	0	0	9	6	0
q8	0	0	8	7	0
q9	0	6	0	9	6

.

Determining the number of options for distributing configuration elements among tasks is necessary. Methods outlined in [37] were utilized in the modeling process. Constraint: the minimum number of configuration elements is 3, and the maximum is 5.

According to the classification by Patanakul P. and Milosevich D. [38], the efficiency criterion includes:

• Distribution of tasks among performers in the project;
• Team characteristics.

For the initial matrix, considering the constraints, 1021 distribution options were obtained (

Table 4. Distribution options.

Number of Configuration Elements	Number of Distribution Options	Comments
1	0	Does not meet the constraints
2	3	Does not meet the constraints
3	128
4	535
5	358

).

Let us consider the options for distributing tasks among teams containing three configuration elements (the minimum team composition). The set of options is divided into equivalence classes.

Table 5. Typical representatives.

Option No.	Typical Representative	Number of Distribution Options
1	1 2 6	4
2	1 2 7	3
3	1 2 8	3
4	1 2 9	6
5	1 3 6	3
6	1 3 7	3
7	1 3 8	3
8	1 3 9	7
9	1 4 7	3
10	1 4 8	3
11	1 5 7	3
12	1 5 8	3
13	1 6 7	3
14	1 6 8	3
15	1 7 8	2
16	1 7 9	7
17	1 8 9	7
18	2 3 9	3
19	2 4 6	2
20	2 4 7	2
21	2 4 8	2
22	2 4 9	4
23	2 5 6	2
24	2 6 9	4
25	3 4 9	3
26	3 5 6	2
27	3 5 7	2
28	3 5 8	2
29	3 5 9	3
30	3 6 9	3
31	3 7 9	3
32	3 8 9	3
33	4 5 7	2
34	4 5 8	2
35	4 7 9	4
36	4 8 9	4
37	5 6 7	2
38	5 6 8	2
39	6 7 9	3
40	6 8 9	3

presents the typical representatives considering H equivalence.

The typical representatives have the most function distribution variants for options 8, 16, and 17. Utilizing configurations with typical representatives with the most function distribution variants will ensure the capability for function redistribution during the project execution. The distribution of performers for the typical representative 1 3 9 is presented in

Table 6. Function distribution options for the typical representative 1 3 9.

Option No.	Distribution of Tasks Among Performers	Option Characteristic
1	1 9 3 9 1	32
2	1 1 3 9 1	30
3	1 1 3 9 9	29
4	1 9 3 9 9	31
5	3 1 3 9 1	28
6	3 1 3 9 9	27
7	3 9 3 9 1	30

.

For the typical representative 1 3 9, the minimum characteristic value is 27 (sixth work distribution variant), and the maximum is 32 (first work distribution variant).

The distribution of performers for the typical representative 1 7 9 is presented in

Table 7. Function distribution options for the typical representative 1 7 9.

Option No.	Distribution of Tasks Among Performers	Option Characteristic
1	1 9 7 7 1	33
2	1 1 7 9 1	34
3	1 1 7 9 9	33
4	1 9 7 7 9	32
5	1 9 7 9 1	36
6	1 9 7 9 9	35
7	1 9 7 9 9	35

.

For the typical representative 1 7 9, the minimum characteristic value is 32 (fourth work distribution variant), and the maximum is 36 (fifth work distribution variant).

The distribution of performers for the typical representative 1 8 9 is presented in

Table 8. Function distribution options for the typical representative 1 8 9.

Option No.	Distribution of Tasks Among Performers	Option Characteristic
1	1 1 8 8 9	30
2	1 1 8 9 1	33
3	1 1 8 9 9	32
4	1 9 8 8 1	33
5	1 9 8 8 9	32
6	1 9 8 9 1	35
7	1 9 8 9 9	34

.

For the typical representative 1 8 9, the minimum characteristic value is 30 (first work distribution variant), and the maximum is 35 (sixth work distribution variant).

Thus, for the given example, given the existing constraints, the best option (criteria for the number of redistribution variants, minimum cost) is a team consisting of configuration elements 1, 3, and 9, with the work distribution presented in

Table 9. Responsibility matrix.

Q/A	a1	a2	a3	a4	a5
q1	0	1	0	0	0
q3	1	0	1	0	0
q9	0	0	0	1	0

.

The obtained results demonstrate that under the given conditions, there are 1021 possible team construction variants. Through constructive enumeration, 128 team variants were identified with the minimum number of performers (3). Following an analysis of configurations considering H equivalence, 40 typical representatives were distinguished (reducing the team variants requiring consideration) by aggregating configuration elements by 69%.

Typical representatives with the maximum number of work distribution variants within the team (8, 16, 17) were identified. The characteristics of the teams for these typical representatives were determined: the minimum team characteristic is 27 (eighth typical representative, work distribution among performers {3, 1, 3, 9, 9}), and the maximum team characteristic is 36 (sixteenth typical representative—work distribution among performers {1, 9, 7, 9, 1}). The variant with the minimum characteristic (cost) was chosen—performers 1, 3, and 9—with the work distribution presented in Table 9.

Consequently, by applying constructive enumeration of configuration elements, a team was formed that possesses the maximum number of work distribution variants (potential for redistributing tasks during project implementation). Furthermore, a work distribution variant with the minimum characteristic was selected, resulting in a cost reduction of 25%.

5. Conclusions

The application of configuration management for a project team has been examined. The primary configuration elements and their specific applications, depending on the project’s nature, have been identified. A model for configuration management of a project’s human resources has been proposed. Constructive enumeration of configuration elements has been discussed. Equivalence types have been suggested based on which typical representatives of configuration elements are determined.

The advantage of the proposed approach is the ability to aggregate and decompose configurations, allowing for scalability according to established requirements. Configuration parameters can be adapted to the organization’s resource needs by taking into account the specific conditions of implementing a project in a particular sector, which contributes to enhancing the resilience of the project management process.

Potential users of the proposed human resource configuration management are companies undergoing agile transformation, critical infrastructure enterprises, and healthcare institutions creating hospital clusters and supercluster medical facilities. Implementing such projects requires constant control over changes and resource provisioning requirements. Applying the proposed approach in the healthcare sector promotes increased efficiency in staffing medical institutions under defined constraints by managing critical competencies, flexible formation, and the operation of adaptive and resilient teams; enhancing the efficiency of human resource utilization in team formation; achieving a social effect due to improved quality of medical services by considering personal–psychological characteristics and effective distribution of project resources; and reducing the influence of the subjective factor and the risk of conflict emergence.

The proposed approach was tested on sample matrices, confirming its increased efficiency. An implementation example of the approach is provided. The obtained results validated the proposed hypotheses: there was a reduction in the number of variants requiring consideration due to the aggregation of configuration elements (a decrease of 69%). Subsequent decomposition and analysis led to a cost reduction of 25%.

The effectiveness of the proposed approach depends on the type of the modified responsibility matrix.

Factors limiting the application of this approach include the dimensionality of the modified responsibility matrix, as this leads to an increase in the complexity of calculations (NP-hard problem), and the nature of resource constraints (qualitative and quantitative). The limitations above can be addressed by developing software for human resource configuration management in a multi-project environment.