Search Results (63)

The rapid rise of e-commerce is intensifying pressure on last-mile delivery networks, making the strategic placement of parcel lockers an urgent urban challenge. In this work, we adapt multilayer two-mode Social Network Analysis to the parcel-locker siting problem, modeling city-scale systems as bipartite networks linking spatially resolved demand zones to locker locations using only open-source demographic and geographic data. We introduce two new Social Network Analysis metrics, Dual centrality and Coverage centrality, designed to identify both structurally critical and highly accessible lockers within the network. Applying our framework to Milan, Rome, and Naples, we find that conventional coverage-based strategies successfully maximize immediate service reach, but tend to prioritize redundant hubs. In contrast, Dual centrality reveals a distinct set of lockers whose presence is essential for maintaining overall connectivity and resilience, often acting as hidden bridges between user communities. Comparative analysis with state-of-the-art multi-criteria optimization baselines confirms that our network-centric metrics deliver complementary, and in some cases better, guidance for robust locker placement. Our results show that a network-analytic lens yields actionable guidance for resilient last-mile locker siting. The method is reproducible from open data (potential-access weights) and plug-in compatible with observed assignments. Importantly, the path-based results (Coverage centrality) are adjacency-driven and thus largely insensitive to volumetric weights. Full article

Aiming for convenience and the low cost of goods transfer between towns, this paper proposes a trade hub location and allocation method based on a novel artificial eagle-inspired optimization algorithm. Firstly, the trade hub location and allocation model is established, taking the total cost consisting of construction and transportation costs as the objective function. Then, to solve the nonlinear model, a novel artificial eagle optimization algorithm (AEOA) is proposed by simulating the collective migration behaviors of artificial eagles when facing a severe living environment. Three main strategies are designed to help the algorithm effectively explore the decision space: the situational awareness and analysis stage, the free exploration stage, and the flight formation integration stage. In the first stage, artificial eagles are endowed with intelligent thinking, thus generating new positions closer to the optimum by perceiving the current situation and updating their positions. In the free exploration stage, artificial eagles update their positions by drawing on the current optimal position, ensuring more suitable habitats can be found. Meanwhile, inspired by the consciousness of teamwork, a formation flying method based on distance information is introduced in the last stage to improve stability and success rate. Test results from the CEC2022 suite indicate that the AEOA can obtain better solutions for 11 functions out of all 12 functions compared with 8 other popular algorithms. Faster convergence speed and stronger stability of the AEOA are also proved by quantitative analysis. Finally, the trade hub location and allocation method is proposed by combining the optimization model and the AEOA. By solving two typical simulated cases, this method can select suitable hubs with lower construction costs and achieve reasonable allocation between hubs and the rest of the towns to reduce transportation costs. Thus, it is used to solve the trade hub location and allocation problem of Henan province in China to help the government make sound decisions. Full article

The hub location problem is constantly examined in the field of air transportation, especially when designing networks for passenger airlines or express cargo providers. The competition that characterizes these businesses combined with the small benefit margins of the industry puts more pressure on finding innovative optimization tools when designing networks, locating hubs, and opening new routes with the minimum cost, usually under strict capacity constraints. This review covers the hub location problem in air transportation and its different mathematical models in preparation for a detailed SLR. Full article

This paper presents an enhanced hub location model tailored to port–hinterland logistics planning, grounded in the Capacitated Multiple-Allocation Hub Location Problem (CMAHLP). The formulation incorporates nonlinear cost structures, hub-specific operating costs, adaptive capacity constraints, and a feasibility condition based on the Social Net Present Value (NPVsocial) to support the design of intermodal freight networks under asymmetric spatial and socio-environmental conditions. The empirical case focuses on Spain, leveraging its strategic position between Asia, North Africa, and Europe. The model includes four major ports—Barcelona, Valencia, Málaga, and Algeciras—as intermodal gateways connected to the 47 provinces of peninsular Spain through calibrated cost matrices based on real distances and mode-specific road and rail costs. A Genetic Algorithm is applied to evaluate 120 scenarios, varying the number of active hubs (4, 6, 8, 10, 12), transshipment discounts (α = 0.2 and 1.0), and internal parameters. The most efficient configuration involved 300 generations, 150 individuals, a crossover rate of 0.85, and a mutation rate of 0.40. The algorithm integrates guided mutation, elitist reinsertion, and local search on the top 15% of individuals. Results confirm the central role of Madrid, Valencia, and Barcelona, frequently accompanied by high-performance inland hubs such as Málaga, Córdoba, Jaén, Palencia, León, and Zaragoza. Cities with active ports such as Cartagena, Seville, and Alicante appear in several of the most efficient network configurations. Their recurring presence underscores the strategic role of inland hubs located near seaports in supporting logistical cohesion and operational resilience across the system. The COVID-19 crisis, the Suez Canal incident, and the persistent tensions in the Red Sea have made clear the fragility of traditional freight corridors linking Asia and Europe. These shocks have brought renewed strategic attention to southern Spain—particularly the Mediterranean and Andalusian axes—as viable alternatives that offer both geographic and intermodal advantages. In this evolving context, the contribution of southern hubs gains further support through strong system-wide performance indicators such as entropy, cluster diversity, and Pareto efficiency, which allow for the assessment of spatial balance, structural robustness, and optimal trade-offs in intermodal freight planning. Southern hubs, particularly in coordination with North African partners, are poised to gain prominence in an emerging Euro–Maghreb logistics interface that demands a territorial balance and resilient port–hinterland integration. Full article

The intermodal express network of high-speed railways and highways can fully utilize the flexibility of highways and the advantages of high-speed railways, such as low cost, high efficiency, and low carbon emission. This paper studies the hub location and flow assignment problem in the intermodal express network of high-speed railways and highways, which can not only increase the transportation efficiency but also provide door-to-door service. Considering the characteristics of multiple modes, flow balance, carbon emission, capacity constraints, and time constraints in the intermodal express network, a mixed-integer linear programming model is proposed with the objective of minimizing the total cost by determining the hub locations, allocations, mode selections, and flow assignments. Owing to the NP-hard computational complexity, an improved genetic algorithm with local search is designed by combining the genetic operators and two optimization strategies to solve the problem effectively. Lastly, numerical experiments are conducted to validate the feasibility of the model and the effectiveness of the algorithm. Full article

This paper introduces a novel neuromorphic-inspired hyper-heuristic framework (NeuHH) for solving the Capacitated Single-Allocation p-Hub Location Routing Problem (CSAp-HLRP), a challenging combinatorial optimization problem that jointly addresses hub location decisions, capacity constraints, and vehicle routing. The proposed framework employs Spiking Neural Networks (SNNs) as the decision-making core, leveraging their temporal dynamics and spike-timing-dependent plasticity (STDP) to guide the real-time selection and adaptation of low-level heuristics. Unlike conventional learning-based hyper-heuristics, NeuHH provides biologically plausible, event-driven learning with improved scalability and interpretability. Experimental results on benchmark instances demonstrate that NeuHH outperforms classical metaheuristics, Lagrangian relaxation methods, and reinforcement learning-based hyper-heuristics. Specifically, NeuHH achieves superior performance in total cost minimization (up to 13.6% reduction), load balance improvement (achieving a load balance factor of as low as 1.04), and heuristic adaptability (reflected by higher heuristic switching frequency). These results highlight the framework’s potential for real-time and energy-efficient logistics optimization in large-scale dynamic networks. Full article

With the rapid acceleration of global urbanization and the advent of smart city initiatives, large metropolises confront the dual challenges of surging logistics demand and constrained surface transportation resources. Traditional surface logistics networks struggle to support sustainable urban development in high-density areas due to traffic congestion, high carbon emissions, and inefficient last-mile delivery. This paper addresses the layout optimization of a hub-and-spoke underground space logistics system (ULS) network for smart cities under stochastic scenarios by proposing an immune-inspired multi-objective particle swarm optimization (IS-MPSO) algorithm. By integrating a stochastic robust Capacity–Location–Allocation–Routing (CLAR) model, the approach concurrently minimizes construction costs, maximizes operational efficiency, and enhances underground corridor load rates while embedding probability density functions to capture multidimensional uncertainty parameters. Case studies in Beijing’s Fifth Ring area demonstrate that the IS-MPSO algorithm reduces the total objective function value from 9.8 million to 3.4 million within 500 iterations, achieving stable convergence in an average of 280 iterations. The optimized ULS network adopts a “ring–synapse” topology, elevating the underground corridor load rate to 59% and achieving a road freight alleviation rate (RFAR) of 98.1%, thereby shortening the last-mile delivery distance to 1.1 km. This research offers a decision-making paradigm that balances economic efficiency and robustness for the planning of underground logistics space in smart cities, contributing to the sustainable urban development of high-density regions and validating the algorithm’s effectiveness in large-scale combinatorial optimization problems. Full article

Due to global climate change, the frequency and intensity of floods will be increasing in the decades to come. Under these conditions, there is an urgent need to develop such relatively simple and robust models and methods, which help the logistical preparatory and crisis management work in case of this natural disaster. In the crisis management phase, the integrated complex command centers and logistical hubs play an essential role. It is an open-ended question: how do we determine the optimal location of these hubs, and find an optimal compromise between their radius of supply and vulnerability? The current article presents a simple and fast method to determine the optimal position of hubs, minimizing their vulnerability, in cases when there is no chance to control the flood of the river (no dam), and in cases when there is a natural or artificial barrier, preventing the flow of water (dam scenario). Based on a system of equations, applying the Gumbel distribution of maximal water levels in various years, the article offers numerical examples to prove the simplicity and practical applicability of the method developed. This approach can supply a decision support system, based on AI. The paper concludes with policy implications. Full article

In the face of the urgent need for the coordinated development of regional rural functions and the orderly and efficient integration of urban and rural areas, the problem of how to accurately identify the spatial correlation relationships and characteristics of rural elements among regions in Northeast China has become a key issue that urgently needs to be resolved. The results show the following: (1) The overall spatial correlation network (SCN) in the Northeast region from the perspective of rural element gravity has obvious differences. Each province has generated a strong connection center, and “strip-shaped” connection belts have been formed across provinces and cities. (2) From the perspective of the spatial pattern of the strong connection attributes of rural elements, Heilongjiang Province presents a polygonal “rhombus network”, Jilin Province presents a closed-loop “triangle network”, and Liaoning Province presents an irregular “trapezoid network”. (3) The connection relationships of rural element nodes within the provincial scope show that Yichun is an important hub connecting all directions within the province; Changchun and Siping have become the central nodes connecting the nodes on the northwest–southeast wings; Fuxin and Yingkou have become the central locations connecting the nodes on the southwest–northeast sides. (4) There are four sectors in the network, and the rural element transfer mechanism among the sectors shows that Block I and Block II are net spillover sectors, playing the role of “resource-based” sectors, and transmitting information to the net inflow Block IV through the broker Block III, presenting a “gradient” transmission mode. Full article

Today, facility location planning primarily pertains to the long-term strategic and operational decision-making of large public and private organizations, and the significant costs associated with facility location, construction, and operation have turned location research into long-term decision-making. Presenting a hub location model for the green supply chain can address the current status of facilities and significantly improve demand coverage at an acceptable cost. Therefore, in this study, a network of facilities for hub location in the service site domain, considering existing and potential facilities under probable scenarios, has been proposed. After presenting the mathematical model, validation was performed on a small scale, followed by sensitivity analysis of the main parameters of the model. Furthermore, a metaheuristic algorithm was employed to analyze the NP-Hardness of the model. Additionally, two metaheuristic algorithms, NSGAII and MOPSO, were developed to demonstrate the efficiency of the model. Based on the conducted analysis, it can be observed that the computational time increases exponentially with the size of sample problems, indicating the NP-Hardness of the problem. However, the NSGAII algorithm performs better in terms of computational time for medium-sized problems compared to the MOPSO algorithm. These algorithms were chosen due to their proven efficiency in handling NP-hard optimization problems and their ability to balance exploration and exploitation in search spaces. Full article

Vehicle-to-grid (V2G) technology is emerging as an innovative paradigm for improving the electricity grid in terms of stabilization and demand response, through the integration of electric vehicles (EVs). A cornerstone in this field is the estimation of the aggregated available capacity (AAC) of EVs based on available data such as origin–destination mobility data, traffic and time of day. This paper considers a real case study, consisting of two aggregation points, identified in the city of Padua (Italy). As a result, this study presents a new method to identify potential applications of V2G by analyzing floating car data (FCD), which allows planners to infer the available AAC obtained from private vehicles. Specifically, the proposed method takes advantage of the opportunity provided by FCD to find private car users who may be interested in participating in V2G schemes, as telematics and location-based applications allow vehicles to be continuously tracked in time and space. Linear and nonlinear dynamic models with different input variables were developed to analyze their relevance for the estimation in one-step- and multiple-step-ahead prediction. The best results were obtained by using traffic data as exogenous input and nonlinear dynamic models implemented by multilayer perceptrons and long short-term memory (LSTM) networks. Both structures achieved an $R^2$ of 0.95 and 0.87 for the three-step-ahead AAC prediction in the two hubs considered, compared to the values of 0.88 and 0.72 obtained with the linear autoregressive model. In addition, the transferability of the obtained models from one aggregation point to another was analyzed to address the problem of data scarcity in these applications. In this case, the LSTM showed the best performance when the fine-tuning strategy was considered, achieving an $R^2$ of 0.80 and 0.89 for the two hubs considered. Full article

The imperative of achieving net zero carbon emissions is driving the transition to renewable energy sources. However, this often leads to carbon tunnel vision by narrowly focusing on carbon metrics and overlooking broader sustainability impacts. To enable these broader impacts to be considered, we have developed a generic approach and a freely available assessment tool on GitHub that not only facilitate the high-level sustainability assessment of renewable energy projects but also indicate whether project-level decisions have positive, negative, or neutral impacts on each of the sustainable development goals (SDGs). This information highlights potential problem areas and which actions can be taken to increase the sustainability of renewable energy projects. The tool is designed to be accessible and user-friendly by developing it in MS Excel and by only requiring yes/no answers to approximately 60 diagnostic questions. The utility of the approach and tool are illustrated via three desktop case studies performed by the authors. The three illustrative case studies are located in Australia and include a large-scale solar farm, biogas production from wastewater plants, and an offshore wind farm. Results show that the case study projects impact the SDGs in different and unique ways and that different project–level decisions are most influential, highlighting the value of the proposed approach and tool to provide insight into specific projects and their sustainability implications, as well as which actions can be taken to increase project sustainability. Full article

Background: This paper introduces an enhanced multi-period p-mobile hub location model that accounts for critical factors such as service time, flow processing delays, and congestion impacts at capacity-constrained hubs. As (urban) transportation networks evolve, mobile hubs play an increasingly vital role in promoting sustainable logistics solutions and addressing complex operational challenges. By enabling the repositioning of hubs across periods, this model seeks to minimize overall costs, particularly in response to dynamic demand fluctuations. Method: To solve this problem, we propose a bi-objective optimization model and introduce a hybrid meta-heuristic algorithm tailored to this application. The algorithm involves a clustering-based technique for evaluating solutions and a refined genetic approach for producing new sets of solutions. Results: Various experiments have been conducted on the Australian Post dataset to evaluate the proposed method. The results have been compared with Multiple-Objecti-ve Particle Swarm Optimization (MOPSO) and Non-Domi-nated Sorting Genetic Algorithm (NSGA-II) using several performance evaluation metrics. Conclusions: The results indicate that the proposed algorithm can provide remarkably better Pareto sets than the other competitive algorithms. Full article

This article presents methods of planning unmanned aerial vehicle (UAV) missions in which individual platforms work together during the reconnaissance of objects located within a terrain. The planning problem concerns determining the flight routes of a swarm, where each UAV has the ability to recognize an object using a specific type of sensor. The experiments described in this article were carried out for drone formation; one drone works as a swarm information hub and exchanges information with the ground control station (GCS). Numerical models for mission planning are presented, which take into account the important constraints, simplifying the description of the mission without too much risk of losing the platforms. Several types of objective functions were used to optimize swarm flight paths. The mission models are presented in the form of mixed integer linear programming problems (MILPs). The experiments were carried out on a terrain model built on the basis of graph and network theory. The method of building a network on which the route plan of a drone swarm is determined is precisely presented. Particular attention was paid to the description of ways to minimize the size of the network on which the swarm mission is planned. The presented methods for building a terrain model allow for solving the optimization problem using integer programming tasks. Full article

The trend in many countries is to promote local consumption of food. This is done by encouraging consumers to connect directly with local farmers or by building hubs that are known as food hubs. Most of the studies on the environmental impact of short food supply chains (SFSCs) focus on the evaluation the greenhouse gas (GHG) emissions in SFSCs where consumers are directly connected to local farmers. Also, these studies mainly focus on GHG emissions due to transportation. To the best of our knowledge, there is no previous study or theoretical models on the estimation and impact of food hub selection and design on total GHG emissions, although they can play an important role in economic, environmental, and social sustainability of an SFSC. In this paper, we develop a framework to estimate GHG emissions from hubs and transportation in a two-echelon SFSC. We introduce a novel approach that utilizes piece-wise linear functions to model the hubs’ GHG emissions combined with an optimization model to calculate the total GHG emission of the SFSC. With this approach, we address the gaps in the literature for a more realistic supply chain model. Our optimization-based approach determines the optimal location, size, and number of food hubs to minimize total GHG emissions. We apply this framework, under various conditions, to the design of a particular SFSC in the Normandy region of France. We also extend the study to other countries. We provide several numerical results that are then analysed. Our analysis shows that the number of foods hubs, their location, and their design may considerably impact the total GHG emissions, depending on the input parameters and data. Furthermore, this study contributes to the advancement of sustainable and green supply chain management, providing valuable insights for practitioners and policy makers aiming to optimize SFSCs for environmental sustainability. Full article