Agnieszka Deja

The growth of smart city manufacturing clusters has increased demand for urban parcel distribution, intensifying energy consumption and pressure on centralised logistics facilities. This study develops a scenario-based linear programming (LP) framework for evaluating energy-efficient configurations of a multi-layer last-mile logistics network incorporating retail parcel micro-hubs located in supermarket parking areas. The model integrates manufacturing facilities, consolidation centres, micro-hubs, and urban demand zones while accounting for parcel flows, facility capacities, and transport-related electricity consumption. A case study of Szczecin, Poland, compares centralised, decentralised, and hybrid delivery structures. The results show substantial variation in energy performance across the analysed configurations. The best-performing configuration reduced estimated transport-related electricity consumption from 263.22 to 139.92 kWh/day (46.8%), while other decentralised and hybrid configurations produced less favourable outcomes. Sensitivity analysis further showed that increasing the share of direct CLN-based distribution reduced the number of trips by 27.5–51.3% and total vehicle-kilometres travelled by 11.3–48.8%, while estimated transport-related electricity consumption increased by 0.4–14.8%. This indicates that the energy performance of RPMH-supported distribution depends on network configuration and last-mile transport mode rather than decentralisation alone.

The development of green production types such as personalized production and shared manufacturing, which use additive technologies in city multifloor manufacturing clusters (CMFMCs), has led to an increase in last-mile parcel delivery (LMPD) activity. This study investigates the integration of electric vehicles and crowdshipping systems into smart CMMCs to improve urban logistics operations related to the distribution of products to consumers. The aim of this study is to improve the LMPD performance of these integrated systems and to provide alternative solutions for sustainable city logistics using the potential of crowdshipping and vehicle sharing fleets (VSFs) in the city logistics nodes (CLNs) of CMFMCs. The issues presented by the loading–unloading operations and sustainable crowdshipping scenarios for LMPD in CMFMCs are considered. This paper presents a new performance evaluation model for crowdshipping LMPD in CMFMCs using VSFs. The case study shows that the proposed model enables the analysis of LMPD performance in CMFMCs, taking into account their finite production capacity, and that it facilitates the planning of cargo turnover and the structure of VSFs consisting of e-bicycles, e-cars, and e-light commercial vehicles (e-LCVs). The model is verified based on a case study for sustainable LMPD scenarios using VSFs. The proposed model enables the planning of both short- and long-term logistics operations with the specified performance indicator of VSF usage in CMFMCs. The validity of using the integrated potential of crowdshipping and vehicle sharing services for LMPD under demand uncertainty in CMFMCs is discussed. This study should prove useful for decision-making and planning processes related to LMPD in CMFMCs and large cities.

The development of smart sustainable megapolises is associated with the formation of city multifloor manufacturing clusters (CMFMCs) in them directly in the residential area in order to reduce the supply chain from the manufacturer to consumers. Additive technologies (ATs) belong to green technologies because they are considered environmentally sustainable due to less production waste and the ability to reuse of product materials within the circular economy concept. Sustainable development of ATs and additive manufacturing management has become a priority sphere for scientific research, and the use of ATs in the city manufacturing has become any daily reality. Nevertheless, the issues of additive manufacturing management within the CMFMCs have not yet been sufficiently studied. The primary goal of this study was to examine the possibilities of additive manufacturing management in the CMFMCs of the megapolis due to the rational facilities multi-floor layout in production buildings, considering the structure of city manufacturing and business process reengineering related to the needs of the production services market. This paper presents a novel model of facilities multi-floor layout in the production buildings of the CMFMCs, considering the structure of city manufacturing, morphological analysis of the additive manufacturing equipment (AME) used, the balance of material and energy flows under infrastructure capacity limitations of megapolis. The model was verified based on a case study for various options of the floor-by-floor grouping of AME in a building of the CMFMCs. Management solutions for maintaining the flow balance of material, energy and water resources in the CMFMCs are discussed. The results may be useful for additive manufacturing management in an urban environment, taking into account the needs of the production services market in the megapolis.

This study is devoted to improving the energy efficiency of urban infrastructure systems (UISs), in particular, the centralized water supply of a city multifloor manufacturing cluster (CMFMC), by developing the principles of effective energy consumption management. The CMFMCs are located in the residential area of a megapolis and include manufacturing and service enterprises, residential and non-residential buildings, and a city logistics node. Demand monitoring and identification of the influence of seasonal and social environmental factors on its fluctuations is considered as a tool for identifying changes in the operating conditions of the water supply system (WSS) for the CMFMC facilities. To identify the typical operating conditions of water supply facilities, an approach is proposed that involves the analysis of daily water consumption profiles (WCPs). The formation of a database, the formation of groups of the same type of daily WCPs, and the construction of typical daily WCPs for typical groups and their description are the main stages of the proposed approach. The database contains a set of classification characteristics that describe the daily water consumption and its unevenness, as well as the shape of the daily WCP. The principal component analysis was applied to determine the dominant components of daily water consumption. A set of morphometric parameters was used to describe the shape of the daily WCPs. The methods of cluster and discriminant analysis were used to identify the influence of seasonality and social factors on water consumption and to form groups of the same type of daily WCPs. The analysis of sets of similar type of daily WCPs for typical days of typical seasons was carried out for a formalized description of the typical operating conditions of water supply facilities. The results of the analysis are the clarification of the equations of the dominant components of daily water consumption, the determination of the average values of the characteristics of daily water consumption, and the construction and description of typical daily WCPs for typical operating conditions of water supply facilities. The research results were obtained on the basis of the data of the monitoring systems for water supply enterprises in Ukraine and Poland in 2021–2022. The obtained results are the basis for planning the water supply process and adjusting the operation modes of WSS pumping stations for the CMFMC, as well as planning power consumption for typical operating conditions, which will contribute to increasing the efficiency of water and electricity use.

The manuscript focuses on the subject of environmental solutions for maritime ships. With the increasing volume of cargo transported by sea, it is crucial to minimise its environmental impact. The International Maritime Organization (IMO) has introduced new regulations in recent years to limit environmental damage. IMO’s initial strategy for reducing GHG emissions from ships aims to reduce GHG emissions from vessels by 50% by 2050 and CO₂ emissions by 70% compared to 2008 (Resolution MEPC.304 (72), 2018). This has prompted ship owners to seek solutions to reduce fossil fuel consumption. The study aims to determine the feasibility of using eco-friendly solutions in maritime shipping to meet the challenges and needs of sustainable maritime fleet. Own contribution includes expert findings, which evaluate individual solutions and their potential uses in global maritime fleet. The publication also highlights the current usage of eco-friendly solutions on ships as well as crew and ship owner attitudes towards their future use and presents conceptual solutions. It should be noted that the subject-matter addressed in this paper is topical and very important in view of the limitations being introduced in respect of environmental standards.

This study focuses on the problem of the efficient energy management of an independent fleet of freight electric vehicles (EVs) providing service to a city multi-floor manufacturing cluster (CMFMC) within a metropolis while considering the requirements of smart sustainable electromobility and the limitations of the power system. The energy efficiency monitoring system is considered an information support tool for the management process. An object-oriented formalization of monitoring information technology is proposed which has a block structure and contains three categories of classes (information acquisition, calculation algorithms, and control procedures). An example of the implementation of the class “Operation with the electrical grid” of information technology is presented. The planning of the freight EVs charging under power limits of the charging station (CS) was carried out using a situational algorithm based on a Fuzzy expert system. The situational algorithm provides for monitoring the charging of a freight EV at a charging station, taking into account the charge weight index (CWI) assigned to it. The optimization of the CS electrical load is carried out from the standpoint of minimizing electricity costs and ensuring the demand for EV charging without going beyond its limits. A computer simulation of the EV charging mode and the CS load was performed. The results of modeling the electrical grid and CS load using the proposed algorithm were compared with the results of modeling using a controlled charging algorithm with electrical grid limitations and an uncontrolled charging algorithm. The proposed approach provides a reduction in power consumption during peak hours of the electrical grid and charging of connected EVs for an on-demand state of charge (SOC).

The location of smart sustainable city multi-floor manufacturing (CMFM) directly in the residential area of a megapolis reduces the delivery time of goods to consumers, has a favorable effect on urban traffic and the environment, and contributes to the rational use of land resources. An important factor in the transformation of a smart city is the development of CMFM clusters and their city logistics nodes (CLNs); the key elements of the logistics system of a megapolis. The primary goal of this study was to examine the role of the CLN4.0, as a lead sustainability and smart service provider of a CMFM cluster within the Industry 4.0 paradigm, as well as its value in the system of logistics facilities and networks of a megalopolis. This paper presents an innovative model of a CLN4.0 under supply uncertainty using a material flow analysis (MFA) methodology, which allows for specific parameters of throughput capacity within the CMFM cluster and the management of supply chains (SCs) under uncertainty. The model was verified based on a case study (7th scenario) for various frameworks of a multi-floor CLN4.0. The validity of using a group of virtual CLNs4.0 to support the balanced operation of these framework operations under uncertainty, due to an uneven production workload of CMFM clusters, is discussed. The results may be useful for the decision-making and planning processes associated with supply chain management (SCM) within CMFM clusters in a megapolis.