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.

Section Purpose This study aims to provide a comprehensive analysis of the evolving research landscape of digital twins (DTs) in logistics. It investigates emerging technological and operational trends, examines how DT applications vary across different implementation scales, and assesses the alignment of current research with the United Nations Sustainable Development Goals (SDGs). AbstractSection Methodology A systematic bibliometric analysis was conducted on a curated dataset of 389 publications from the Scopus database (2017–2025). 2017 is effectively when digital twin research in logistics began to proliferate. The methodology integrates citation analysis, keyword co-occurrence mapping, and thematic clustering to identify the intellectual structure, thematic evolution, and collaborative patterns within the field. AbstractSection Originality/value This paper's originality is threefold. First, it offers a more robust justification for digitalisation by mapping the complex pressures driving DT adoption. Second, it introduces a novel scale-based taxonomy (object, infrastructure, system) that provides a new framework for classifying and understanding the maturity of DT applications in logistics. Third, unlike previous reviews, it systematically connects the functions and impacts of DTs to specific SDGs, thereby bridging a critical gap between technological innovation and sustainability governance. AbstractSection Findings The analysis reveals a rapid maturation of the field, with research shifting from a narrow focus on simulation toward broader themes of sustainability, resilience, and AI-driven optimisation. Key findings indicate a significant gap between the conceptual potential of DTs and their practical implementation, particularly concerning system-level integration and data interoperability. While DT applications show a strong conceptual alignment with SDG 9 (Infrastructure), SDG 11 (Sustainable Cities), and SDG 12 (Responsible Consumption), there is a notable lack of empirical evidence and quantifiable metrics to validate their real-world sustainability impacts. AbstractSection Implications The findings provide strategic insights for managers and policymakers to guide the adoption of DTs for enhanced operational efficiency and sustainability. For academics, this study clarifies the current state of knowledge, highlights critical research gaps, such as the need for quantitative impact assessments and cross-sectoral studies, and proposes future research directions focused on developing integrated frameworks for digital sustainability governance in logistics.

The irregularities of evolutionary development, continuous improvement of environmental component into transport and raise many unsolved problem in the fields of transport in logistics systems and material flows management. The city parameters analyses had highlighted connections with transport services technology. Also the distribution stage of the logistic chain, which consists of following market participants: incoming material flow, retailers and Transportation Company (carrier) in different cities has been considered. Transportation service is made by road transport. The paper compares results of transportation servicing in different cities and logistic chains with same technology. As performance indicator net present value has been used. Existing criteria for evaluating the effectiveness of distribution of material flows does not sufficiently take into account the time value of money and possible options for investment, so when estimating the performance of servicing of business investment indicators should be used. Paper should elucidate the impact of cities on the transport service efficacy. Practical suggestions on logistics system functioning and transportation service of retailers in urban area have been proposed. Obtained results discovered overall impact of city’s parameters (density of streets and roads network’ irregularity factor; automobilizationlevel; urban square; scheme of road network; city radius) on logistics according to model which include more than 50 parameters and allows finding regularities of changing logistics efficiency on them.