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Advances in Intelligent Cluster Collaborative Control for Optimization Decision-Making
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Advances in Intelligent Cluster Collaborative Control for Optimization Decision-Making

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 22 July 2026 | Viewed by 1661

Special Issue Editors

Dr. Lei Zhu

E-Mail Website
Guest Editor
Department of Industrial and System Engineering, University of North Carolina, Charlotte, NC 28223, USA
Interests: smart mobility and spatial sensing (GPS/GIS/LiDAR/camera/radar); big data and artificial intelligence in transportation; shared and automated mobility and micro-mobility modeling and simulation; transportation sustainability, safety, and electrification
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wei Yue

E-Mail Website
Guest Editor
College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Interests: intelligent vehicle cooperative control and unmanned swarm cooperative search
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advancement of artificial intelligence, Internet of Things, and edge computing, intelligent cluster systems, such as multi-robot systems, drone swarms, and distributed sensor networks, have emerged as key enablers of sustainable transformation across various sectors. These systems are increasingly deployed in smart manufacturing, environmental monitoring, disaster response, and logistics, offering substantial potential to enhance resource efficiency, reduce carbon emissions, and strengthen adaptive capacity in dynamic environments. However, the collaborative control and optimization of such clusters remain challenging due to their inherent complexity, scalability demands, and operational uncertainties. In this Special Issue, we address these challenges by exploring advanced theories, models, and algorithms for intelligent cluster coordination and optimization, with a strong emphasis on improving system autonomy, energy efficiency, and environmental performance in support of global sustainability agendas, including the UN Sustainable Development Goals, such as SDG 9, SDG 11, and SDG 13. We particularly welcome contributions that demonstrate how intelligent collaborative control technologies facilitate sustainable innovation by advancing energy conservation, resource circularity, and low-carbon transitions in areas such as mobility, agriculture, manufacturing, and smart cities.

We are pleased to invite you to contribute your latest research findings and reviews to this Special Issue.

Aim of the Special Issue and how the subject relates to the journal scope

In this Special Issue, we aim to present cutting-edge research on intelligent cluster systems, with a focus on collaborative control and optimization decision-making that will directly support sustainable development. This topic aligns closely with the journal's scope regarding automation, intelligent systems, computational intelligence, and decision support systems, particularly through applications that reduce environmental impact, enhance resource efficiency, and promote resilient and low-carbon infrastructure. By fostering interdisciplinary dialogue among systems engineering, computational intelligence, and sustainability science, in this Issue, we seek to advance decision-making frameworks that integrate lifecycle efficiency, data-driven environmental management, and policy-aware design. We encourage submissions that explore how intelligent cluster technologies contribute to sustainable industrial transformation, environmental well-being, and socially responsible innovation.

Suggested themes and article types for submissions

In this Special Issue, submissions of original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Multi-agent reinforcement learning for sustainable cluster control;

Distributed optimization for energy and resource efficiency;

Swarm intelligence and bio-inspired collaboration in sustainable applications;

Dynamic task allocation and scheduling for low-carbon operations;

Consensus and formation control supporting resilient and sustainable infrastructures;

Human–cluster interaction for sustainable decision-making;

Resilience and fault tolerance in cluster systems for sustainable services;

Edge intelligence and real-time decision-making in sustainable applications;

Applications in smart cities, green logistics, precision agriculture, environmental surveillance, and sustainable manufacturing.

You may choose our Joint Special Issue in Applied Sciences.

Dr. Lei Zhu
Prof. Dr. Wei Yue
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent cluster systems
  • cooperative control
  • optimization algorithms
  • multi-agent systems
  • swarm robotics
  • sustainable decision-making
  • distributed intelligence
  • autonomous systems
  • edge computing
  • task allocation
  • sustainability
  • green computing
  • low-carbon operations
  • resource efficiency

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Published Papers (3 papers)

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Research

27 pages, 3473 KB  
Article
Spatiotemporal Evolution and Driving Mechanisms of the Coupling Coordination Among the Digital Economy, Low-Carbon Logistics, and Ecological Environment: Evidence from China
by Qian Zhou, Ligang Wu, Mengyao Zhang, Baotong Chen and Zepeng Qin
Sustainability 2026, 18(10), 4944; https://doi.org/10.3390/su18104944 - 14 May 2026
Viewed by 176
Abstract
In the context of the rapid growth of the digital economy and the continued implementation of China’s “dual carbon” strategy, clarifying the interactive relationships among the digital economy, low-carbon logistics, and the ecological environment is crucial for promoting sustainable regional development and green [...] Read more.
In the context of the rapid growth of the digital economy and the continued implementation of China’s “dual carbon” strategy, clarifying the interactive relationships among the digital economy, low-carbon logistics, and the ecological environment is crucial for promoting sustainable regional development and green transformation. Based on the theoretical mechanisms underlying the coordinated development of these three systems, this study constructs a comprehensive evaluation index system for the Digital Economy–Low-Carbon Logistics–Ecological Environment (DLE) system. The entropy weighting method, a modified coupling coordination model, kernel density estimation, spatial autocorrelation analysis, and the barrier model are integrated to investigate the spatiotemporal evolution and driving mechanisms of coupling coordination among the three systems. The results indicate that (1) the development levels of the digital economy, low-carbon logistics, and the ecological environment have generally increased, although their evolutionary trajectories differ across stages. The digital economy shows the most rapid improvement, low-carbon logistics maintains steady progress, and the ecological environment exhibits gradual optimization. (2) From a temporal perspective, the overall coupling coordination of the national DLE system has shown a fluctuating upward trend, with the coordination type gradually evolving from a near-coordination stage to an initial coordination stage, though it remains at a low-to-medium coordination level overall. (3) From a spatial perspective, the coupling coordination degree presents a stable gradient pattern, with higher levels in eastern China, intermediate levels in central China, and lower levels in western China. Medium- and high-coordination areas are gradually extending from coastal regions to inland areas, while regional disparities remain evident. (4) The spatial autocorrelation results reveal significant positive spatial clustering at the provincial level. Both high-value and low-value clusters show a certain degree of stability, indicating clear spatial spillover effects. (5) An analysis of constraining factors reveals that insufficient scale of digital economic development and innovation application capabilities, constraints on ecological and environmental resource carrying capacity and governance, as well as low operational efficiency and delayed transformation of low-carbon logistics, are the primary types of obstacles hindering the coordinated improvement of the three systems. These findings provide empirical evidence and policy implications for leveraging the digital economy to facilitate low-carbon logistics transformation and enhance coordinated regional sustainability. Full article
(This article belongs to the Special Issue Advances in Intelligent Cluster Collaborative Control for Optimization Decision-Making)
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26 pages, 1296 KB  
Article
Spatiotemporal Evolution and Obstacle Factors of Coupling Coordination Among Low-Carbon Logistics, Regional Economy, and Ecological Environment Systems in the Yellow River Basin
by Qian Zhou, Ligang Wu and Mengyao Zhang
Sustainability 2026, 18(5), 2458; https://doi.org/10.3390/su18052458 - 3 Mar 2026
Viewed by 320
Abstract
Under the background of the “dual carbon” strategy and regional coordinated development, the synergistic evolution of low-carbon logistics, regional economy, and ecological environment in the Yellow River Basin has become a key pathway to achieving high-quality development. Taking nine provinces (autonomous regions) within [...] Read more.
Under the background of the “dual carbon” strategy and regional coordinated development, the synergistic evolution of low-carbon logistics, regional economy, and ecological environment in the Yellow River Basin has become a key pathway to achieving high-quality development. Taking nine provinces (autonomous regions) within the basin as the study area, this paper constructed a coupling coordination evaluation index system for the LREES (Low-carbon Logistics–Regional Economy–Ecological Environment System), and measured the comprehensive development level of each subsystem using the entropy weight method. Based on the coupling coordination degree model, the temporal evolution of the three systems from 2010 to 2024 was systematically evaluated. In addition, global and local spatial autocorrelation models were introduced to identify spatial clustering patterns, while the obstacle degree model was used to identify key constraints at both the criterion and indicator levels. The results revealed that: the overall development level of the LREES systems steadily increased, with reduced regional disparities; the coupling coordination degree showed a trend of “fluctuating rise–gradual coordination,” with the average value increasing from 0.450 to 0.623, indicating continuously enhanced synergy; spatially, a gradient pattern of “downstream > midstream > upstream” emerged, accompanied by significant positive spatial autocorrelation; resource endowment and development scale were major constraints, while construction level, operational efficiency, and governance capacity were secondary. High-frequency obstacle indicators included per capita water resources, total import and export volume, and urban sewage treatment capacity. These findings offer theoretical support and policy guidance for promoting green transformation, enhancing system synergy, and advancing coordinated regional development in the Yellow River Basin. Full article
(This article belongs to the Special Issue Advances in Intelligent Cluster Collaborative Control for Optimization Decision-Making)
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28 pages, 7036 KB  
Article
Towards Sustainable Urban Logistics: Route Optimization for Collaborative UAV–UGV Delivery Systems Under Road Network and Energy Constraints
by Cunming Zou, Qiaoran Yang, Junyu Li, Wei Yue and Na Yu
Sustainability 2026, 18(2), 1091; https://doi.org/10.3390/su18021091 - 21 Jan 2026
Cited by 1 | Viewed by 793
Abstract
This paper addresses the optimization challenges in urban logistics with the aim of enhancing the sustainability of last-mile delivery. By focusing on the collaborative delivery between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), we propose a novel approach to reducing energy [...] Read more.
This paper addresses the optimization challenges in urban logistics with the aim of enhancing the sustainability of last-mile delivery. By focusing on the collaborative delivery between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), we propose a novel approach to reducing energy consumption and operational inefficiencies. A bilevel mixed-integer linear programming (Bilevel-MILP) model is developed, integrating road network topology with dynamic energy constraints. Departing from traditional single-delivery modes, the paper establishes a multi-task continuous delivery framework. By incorporating a dynamic charging point selection strategy and path–energy coupling constraints, the model effectively mitigates energy limitations and the issue of repeated returns for UAV charging in complex urban road networks, thereby promoting more efficient resource utilization. At the algorithmic level, a Collaborative Delivery Path Optimization (CDPO) framework is proposed, which embeds an Improved Sparrow Search Algorithm (ISSA) with directional initialization and a Hybrid Genetic Algorithm (HGA) with specialized crossover strategies. This enables the synergistic optimization of UAV delivery sequences and UGV charging decisions. The simulation results demonstrate that, in scenarios with a task density of 20 per 100 km2, the proposed CDPO algorithm reduces the total delivery time by 33.9% and shortens the UAV flight distance by 24.3%, compared to conventional fixed charging strategies (FCSs). These improvements directly contribute to lowering energy consumption and potential emissions. The road network discretization approach and dynamic candidate charging point generation confirm the method’s adaptability in high-density urban environments, offering a spatiotemporal collaborative optimization paradigm that supports the development of sustainable and intelligent urban logistics systems. The obtained results provide practical insights for the design and deployment of efficient UAV–UGV collaborative logistics systems in urban environments, particularly under high-task-density and energy-constrained conditions. Full article
(This article belongs to the Special Issue Advances in Intelligent Cluster Collaborative Control for Optimization Decision-Making)
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