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.

This study focuses on integrated sustainable waste management (ISWM) within a city multifloor manufacturing (MFM) cluster. Manufacturing activities in residential areas of the urban environment and the associated generation of municipal production waste (MPW) are serious problems. The primary goal of this study is to design smart supply chain (SSC) scenarios for the shipment of MPW from a city MFM cluster under uncertainty. This paper presents a new model of the finite MPW generation capacity for a city MFM cluster on the basis of an analysis of its finite production capacity using the material flow analysis (MFA) methodology. The proposed model allows us to determine the number of transport fleet units needed for the implementation of various supply chain (SC) scenarios of MPW. To select the best scenario for MPW shipment in real time, the application of SSC and SSC management (SSCM) technologies is proposed. SSCM performance indicators are proposed which allow us to evaluate the efficiency of using vehicles for cluster MPW transportation. The numerical values of the SSCM performance indicators for various options regarding the handling of city MFM buildings using trucks are obtained. These evaluations form the basis for the decision-making and planning associated with the SSCs of MPW.

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 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.

The application of multi-floor manufacturing (MFM) in huge cities is related to the rational use of urban areas and the solution to traffic problems. The operation of the city MFM clusters depends on the efficiency of production and transport management considering technical, economic, environmental, and other factors. The primary goal of this paper was to identify and analyze the drivers of sustainable supply chains (SSCs) that influence or encourage the design of sustainable processes in city MFM clusters under uncertainty in supply chains. This paper presents an SSC performance model for city MFM clusters under uncertainty. The proposed model is universal and is based on material flow analysis (MFA) methodology. The presented analysis helps to determine the conditions for rhythmic deliveries with the use of the multi-IRTs. The coefficients of rhythmic deliveries for multiple intelligent reconfigurable trolleys (IRTs) and the capacity loss of freight elevators allow us to periodically assess the sustainability processes in city MFM clusters related to the flow materials. These assessments are the basis for the decision-making and planning of SSCs.

This study focuses on management ways within a city multi-floor manufacturing cluster (MFMC). The application of MFMC in megapolises is closely related to the problem of urban spatial development and the problem of matching transport and logistics services. The operation of the MFMC depends on the efficiency of production and transport management considering technical, economic, end environmental factors. Therefore, conditions affecting decision-making in the field of production planning by MFMCs and accompanying transports within the agglomeration area with the use of the production-service platform were presented. Assumptions were created for the decision model, allowing for the selection of partners within the MFMC to execute the production order. A simplified decision model using the Hungarian algorithm was proposed, which was verified with the use of test data. The model is universal for material flow analysis and is an assessments basis for smart sustainable supply chain decision-making and planning. Despite the narrowing of the scope of the analysis and the simplifications applied, the presented model using the Hungarian algorithm demonstrated its potential to solve the problem of partner selection for the execution of the contract by MFMC.