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As one of the primary sources of carbon emissions, transportation sector has proposed various measures to reduce its carbon emissions. Introducing energy-efficient and low-carbon autonomous trucks into freight transportation is highly promising, but faces various challenges, especially safety issues. This study addresses eco-friendly lane reservation-based autonomous truck transportation network design for transportation safety and low carbon emissions. It aims to optimally implement dedicated truck lanes in an existing network and design dedicated routes for autonomous truck transportation to simultaneously minimise the negative impact caused by dedicated truck lanes and carbon emissions of the entire transportation system. We first formulate this problem into a bi-objective integer linear program. Then, an ϵ-constraint-based two-stage algorithm (ETSA) is proposed to solve it based on explored problem properties. A case study based on the well-known Sioux Falls network is conducted to demonstrate the applicability of the proposed model and algorithm. Computational results for 310 instances from the literature demonstrate that the proposed algorithm significantly outperforms the ϵ-constraint combined with the proposed ILP in obtaining the Pareto front. Moreover, helpful managerial insights are derived based on sensitivity analysis. [ABSTRACT FROM AUTHOR]

Kernel-based online classification algorithms, such as the Perceptron, NORMA, and passive-aggressive, are renowned for their computational efficiency but have been criticized for slow convergence. However, the parallel projection algorithm, within the adaptive projected subgradient method framework, exhibits accelerated convergence and enhanced noise resilience. Despite these advantages, a specific sparsification procedure for the parallel projection algorithm is currently absent. Additionally, existing online classification algorithms, including those mentioned earlier, heavily rely on the kernel width parameter, rendering them sensitive to its choices. In an effort to bolster the performance of these algorithms, we propose a two-stage classification algorithm within the Cartesian product space of reproducing kernel Hilbert spaces. In the initial stage, we introduce an online double-kernel classifier with parallel projection. This design aims not only to improve convergence but also to address the sensitivity to kernel width. In the subsequent stage, the component with a larger kernel width remains fixed, while the component with a smaller kernel width undergoes updates. To promote sparsity and mitigate model complexity, we incorporate the projection-along-subspace technique. Moreover, for enhanced computational efficiency, we integrate the set-membership technique into the updates, selectively exploiting informative vectors to improve the classifier. The monotone approximation of the proposed classifier, based on the designed ϵ -insensitive function, is presented. Finally, we apply the proposed algorithm to equalize a nonlinear channel. Simulation results demonstrate that the proposed classifier achieves faster convergence and lower misclassification error with comparable model complexity. [ABSTRACT FROM AUTHOR]

In the distribution field, distribution costs and customer service satisfaction are extremely important issues for enterprises. However, both the Vehicle Routing Problem with Soft Time Windows (VRPSTW) and the Vehicle Routing Problem with Fuzzy Time Windows (VRPFTW) have certain deficiencies in describing real-world scenarios. Therefore, this paper considers both soft time windows and fuzzy time windows, improving upon the traditional VRPSTW and VRPFTW models to establish a more comprehensive and realistic model called the Vehicle Routing Problem with Soft Time Windows and Fuzzy Time Windows (VRPSFTW). Secondly, to solve the relevant problems, this paper proposes a Directed Mutation Genetic Algorithm integrated with Large Neighborhood Search (LDGA), which fully utilizes the advantages of the Genetic Algorithm (GA) in the early stages and appropriately adopts removal and re-insertion operators from the Large Neighborhood Search (LNS). This approach not only makes efficient use of computational resources but also compensates for the weaknesses of crossover and mutation operators in the later stages of the genetic algorithm. Thereby, it improves the overall efficiency and accuracy of the algorithm and achieves better solution results. In addition, in order to solve multi-objective problems, this paper employs a two-stage solution approach and designs two sets of algorithms based on the principles of "cost priority" and "service-level priority". Simulation experiments demonstrated that the algorithms designed in this study achieved a more competitive solving performance. [ABSTRACT FROM AUTHOR]

The application of Trajectory-Based Operation (TBO) and Free-Route Airspace (FRA) can relieve air traffic congestion and reduce flight delays. However, this new operational framework has higher requirements for the reliability and efficiency of the trajectory, which will be significantly influenced if the analysis of wind uncertainty during trajectory planning is insufficient. In the literature, trajectory planning models considering wind uncertainty are developed based on the time-invariant condition (i.e., three-dimensional), which may potentially lead to a significant discrepancy between the predicted flight time and the real flight time. To address this problem, this study proposes a trajectory planning model considering time-variant wind uncertainty (i.e., four-dimensional). This study aims to optimize a reliable and efficient trajectory by minimizing the Mean-Excess Flight Time (MEFT). This model formulates wind as a discrete variable, forming the foundation of the proposed time-variant predicted method that can calculate the segment flight time accurately. To avoid the homogeneous assumption of distributions, we specifically apply the first four moments (i.e., expectation, variance, skewness, and kurtosis) to describe the stochasticity of the distributions, rather than using the probability distribution function. We apply a two-stage algorithm to solve this problem and demonstrate its convergence in the time-variant network. The simulation results show that the optimal trajectory has 99.2% reliability and reduces flight time by approximately 9.2% compared to the current structured airspace trajectory. In addition, the solution time is only 2.3 min, which can satisfy the requirement of trajectory planning. [ABSTRACT FROM AUTHOR]

With the development of market economy, cold chain logistics has become the mainstream of the current transportation industry. Reducing transportation costs and optimizing transportation routes from an environmentally friendly perspective is the main research focus. This study starts with an emphasis on environmental protection and cost savings and optimizes existing cold chain logistics expenses. Using the clustering and annealing algorithms, the path optimization model with the lowest cost is constructed and analyzed. The K‐means algorithm is utilized to cluster and partition logistics areas, and then optimized simulated annealing algorithm is used to control and utilize logistics costs and resources. The experimental results show that the optimized algorithm reduces costs by 11.36% and increases the loading rate of the vehicle by 11.95%. The delivery time has been reduced by 18.1%. The two‐stage algorithm can optimize and improve the path model, reduce transportation costs, improve cold chain transportation efficiency, and verify the feasibility of the model. [ABSTRACT FROM AUTHOR]

Urban public safety management relies heavily on video surveillance systems, which provide crucial visual data for resolving a wide range of incidents and controlling unlawful activities. Traditional methods for target detection predominantly employ a two-stage approach, focusing on precision in identifying objects such as pedestrians and vehicles. These objects, typically sparse in large-scale, lower-quality surveillance footage, induce considerable redundant computation during the initial processing stage. This redundancy constrains real-time detection capabilities and escalates processing costs. Furthermore, transmitting raw images and videos laden with superfluous information to centralized back-end systems significantly burdens network communications and fails to capitalize on the computational resources available at diverse surveillance nodes. This study introduces DiffRank, a novel preprocessing method for fixed-angle video imagery in urban surveillance. The method strategically generates candidate regions during preprocessing, thereby reducing redundant object detection and improving the efficiency of the detection algorithm. Drawing upon change detection principles, a background feature learning approach utilizing shallow features has been developed. This approach prioritizes learning the characteristics of fixed-area backgrounds over direct background identification. As a result, alterations in ROI are efficiently discerned using computationally efficient shallow features, markedly accelerating the generation of proposed Regions of Interest (ROIs) and diminishing the computational demands for subsequent object detection and classification. Comparative analysis on various public and private datasets illustrates that DiffRank, while maintaining high accuracy, substantially outperforms existing baselines in terms of speed, particularly with larger image sizes (e.g., an improvement exceeding 300 % at 1920×1080 resolution). Moreover, the method demonstrates enhanced robustness compared to baseline methods, efficiently disregarding static targets like mannequins in display windows. The advancements in candidate area preprocessing enable a balanced approach between detection accuracy and overall detection speed, making the algorithm highly applicable for real-time on-site analysis in edge computing scenarios and cloud-edge collaborative computing environments.

Urban public safety management relies heavily on video surveillance systems, which provide crucial visual data for resolving a wide range of incidents and controlling unlawful activities. Traditional methods for target detection predominantly employ a two-stage approach, focusing on precision in identifying objects such as pedestrians and vehicles. These objects, typically sparse in large-scale, lower-quality surveillance footage, induce considerable redundant computation during the initial processing stage. This redundancy constrains real-time detection capabilities and escalates processing costs. Furthermore, transmitting raw images and videos laden with superfluous information to centralized back-end systems significantly burdens network communications and fails to capitalize on the computational resources available at diverse surveillance nodes. This study introduces DiffRank, a novel preprocessing method for fixed-angle video imagery in urban surveillance. The method strategically generates candidate regions during preprocessing, thereby reducing redundant object detection and improving the efficiency of the detection algorithm. Drawing upon change detection principles, a background feature learning approach utilizing shallow features has been developed. This approach prioritizes learning the characteristics of fixed-area backgrounds over direct background identification. As a result, alterations in ROI are efficiently discerned using computationally efficient shallow features, markedly accelerating the generation of proposed Regions of Interest (ROIs) and diminishing the computational demands for subsequent object detection and classification. Comparative analysis on various public and private datasets illustrates that DiffRank, while maintaining high accuracy, substantially outperforms existing baselines in terms of speed, particularly with larger image sizes (e.g., an improvement exceeding 300 % at 1920×1080 resolution). Moreover, the method demonstrates enhanced robustness compared to baseline methods, efficiently disregarding static targets like mannequins in display windows. The advancements in candidate area preprocessing enable a balanced approach between detection accuracy and overall detection speed, making the algorithm highly applicable for real-time on-site analysis in edge computing scenarios and cloud-edge collaborative computing environments. [ABSTRACT FROM AUTHOR]

The equality-constrained lasso problem augments the standard lasso by imposing additional structure on regression coefficients. Despite the broad utilities of the equality-constrained lasso, existing algorithms are typically computationally inefficient and only applicable to linear and logistic models. In this article, we devise a fast solution to the equality-constrained lasso problem with a two-stage algorithm: first obtaining candidate covariate subsets of increasing size from unconstrained lasso problems and then leveraging an efficient combined alternating direction method of multipliers/Newton-Raphson algorithm. Our proposed algorithm leads to substantial speedups in getting the solution path of the constrained lasso and can be easily adapted to generalized linear models and Cox proportional hazards models. We conduct extensive simulation studies to demonstrate the computational advantage of the proposed method over existing solvers. To further show the unique utility of our method, we consider two real-world data examples: a microbiome regression analysis and a myeloma survival analysis; neither example could be solved by naively fitting the constrained lasso problem on the full predictor set. for this article are available online. [ABSTRACT FROM AUTHOR]

Driven by Chinese double carbon policy, logistics companies should ensure both environmental preservation and fluency in the last-mile delivery. With the objective of minimizing the total distribution distance, a model for location of mobile recharge stations and the route planning is formulated, which considers some constraints such as load, power and service capacity of recharge stations. Subsequently, a two-stage algorithm is proposed. The genetic algorithm is used to generate the initial routing plans in the first stage, and the RSI algorithm is designed to locate recharge stations and adjust routing plans in the second stage. Tested by the CVRP benchmark cases, the results show that the average increasing rate of travel distances caused by visiting recharge station is less than 5%. Compared with PSO and SA, the results between the proposed algorithm and the other two algorithms are respectively -4.04% and -3.65%. Also, the adaptability of the model is verified through the sensitivity analysis with the main model parameters such as power consumption rate. Therefore, the mode is feasible if logistics companies can afford to use exclusive mobile recharge stations and accept the increase of travel distances which is less than 8%. [ABSTRACT FROM AUTHOR]

In the distribution field, distribution costs and customer service satisfaction are extremely important issues for enterprises. However, both the Vehicle Routing Problem with Soft Time Windows (VRPSTW) and the Vehicle Routing Problem with Fuzzy Time Windows (VRPFTW) have certain deficiencies in describing real-world scenarios. Therefore, this paper considers both soft time windows and fuzzy time windows, improving upon the traditional VRPSTW and VRPFTW models to establish a more comprehensive and realistic model called the Vehicle Routing Problem with Soft Time Windows and Fuzzy Time Windows (VRPSFTW). Secondly, to solve the relevant problems, this paper proposes a Directed Mutation Genetic Algorithm integrated with Large Neighborhood Search (LDGA), which fully utilizes the advantages of the Genetic Algorithm (GA) in the early stages and appropriately adopts removal and re-insertion operators from the Large Neighborhood Search (LNS). This approach not only makes efficient use of computational resources but also compensates for the weaknesses of crossover and mutation operators in the later stages of the genetic algorithm. Thereby, it improves the overall efficiency and accuracy of the algorithm and achieves better solution results. In addition, in order to solve multi-objective problems, this paper employs a two-stage solution approach and designs two sets of algorithms based on the principles of “cost priority” and “service-level priority”. Simulation experiments demonstrated that the algorithms designed in this study achieved a more competitive solving performance.

This study designs a two-stage algorithm to address the bid generation problem of carriers when adding new vehicle routes in the presence of the existing vehicle routes to provide transportation service. To obtain the best auction combination and bid price of the carrier, a hybrid integer nonlinear programming model is introduced. According to the characteristics of the problem, a set of two-stage hybrid algorithms is proposed, innovatively integrating block coding within a genetic algorithm framework with a depth-first search approach. This integration effectively manages routing constraints, enhancing the algorithm's efficiency. The block coding and each route serve as decision variables in the set partition formula, enabling a comprehensive exploration of potential solutions. After a simulation-based analysis, the algorithm has been comprehensively validated analytically and empirically. The improvement of this research lies in the effectiveness of the proposed algorithm, i.e., the ability to handle a broader range of problem scales with less time in addressing complex operator bid generation in combinatorial auctions. [ABSTRACT FROM AUTHOR]

Although integrated energy systems (IES) are currently modest in size, their scheduling faces strong challenges, stemming from both wind generation disturbances and the system's complexity, including intrinsic heterogeneity and pronounced non-linearity. For this reason, a two-stage algorithm called the Multi-Objective Group Search Optimizer with Pre-Exploration (MOGSOPE) is proposed to efficiently achieve the optimal solution under wind generation disturbances. The optimizer has an embedded trainable surrogate model, Deep Neural Networks (DNNs), to explore the common features of the multi-scenario search space in advance, guiding the population toward a more efficient search in each scenario. Furthermore, a multi-scenario Multi-Attribute Decision Making (MADM) approach is proposed to make the final decision from all alternatives in different wind scenarios. It reflects not only the decision-maker's (DM) interests in other indicators of IES but also their risk preference for wind generation disturbances. A case study conducted in Barry Island shows the superior convergence and diversity of MOGSOPE in comparison to other optimization algorithms. With respect to numerical performance metrics HV, IGD, and SI, the proposed optimizer exhibits improvements of 3.1036%, 4.8740%, and 4.2443% over MOGSO, and 4.2435%, 6.2479%, and 52.9230% over NSGAII, respectively. What's more, the effectiveness of the multi-scenario MADM in making final decisions under uncertainty is demonstrated, particularly in optimal scheduling of IES under wind generation disturbances.

Fault classification in power transmission lines is important in distance relaying for identifying the accurate phases implicated in the fault occurrence. Generally, the accuracy of fault classification algorithms is evaluated by simulation data, which shows quite different characteristics from real fault data. Also, most of the previous works on fault classification used a single-stage method such as a rule-based algorithm or machine learning-based algorithm. Because of the diverse characteristics of real fault data, the performance of the single-stage method is limited. To address these issues, this paper proposes a novel two-stage algorithm that combines the strengths of rule-based and machine-learning algorithms to improve the accuracy of real fault data. A case study using real fault data shows that the proposed two-stage algorithm outperforms other conventional single-stage algorithms.

The application of Trajectory-Based Operation (TBO) and Free-Route Airspace (FRA) can relieve air traffic congestion and reduce flight delays. However, this new operational framework has higher requirements for the reliability and efficiency of the trajectory, which will be significantly influenced if the analysis of wind uncertainty during trajectory planning is insufficient. In the literature, trajectory planning models considering wind uncertainty are developed based on the time-invariant condition (i.e., three-dimensional), which may potentially lead to a significant discrepancy between the predicted flight time and the real flight time. To address this problem, this study proposes a trajectory planning model considering time-variant wind uncertainty (i.e., four-dimensional). This study aims to optimize a reliable and efficient trajectory by minimizing the Mean-Excess Flight Time (MEFT). This model formulates wind as a discrete variable, forming the foundation of the proposed time-variant predicted method that can calculate the segment flight time accurately. To avoid the homogeneous assumption of distributions, we specifically apply the first four moments (i.e., expectation, variance, skewness, and kurtosis) to describe the stochasticity of the distributions, rather than using the probability distribution function. We apply a two-stage algorithm to solve this problem and demonstrate its convergence in the time-variant network. The simulation results show that the optimal trajectory has 99.2% reliability and reduces flight time by approximately 9.2% compared to the current structured airspace trajectory. In addition, the solution time is only 2.3 min, which can satisfy the requirement of trajectory planning.

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In the recent decades, the recognition that uncertainty lies at the heart of modern project management has induced considerable research efforts on robust project scheduling for dealing with uncertainty in a scheduling environment. The literature generally provides two main strategies for the development of a robust predictive project schedule, namely robust resource allocation and time buffering. Yet, the previous studies seem to have neglected the potential benefits of an integration between the two. Besides, few efforts have been made to protect simultaneously the project due date and the activity start times against disruptions during execution, which is desperately demanded in practice. In this paper, we aim at constructing a proactive schedule that is not only short in time but also less vulnerable to disruptions. Firstly, a bi-objective optimisation model with a proper normalisation of the two components is proposed in the presence of activity duration variability. Then a two-stage heuristic algorithm is developed which deals with a robust resource allocation problem in the first stage and optimally determines the position and the size of time buffers using a simulated annealing algorithm in the second stage. Finally, an extensive computational experiment on the PSPLIB network instances demonstrates the superiority of the combination between resource allocation and time buffering as well as the effectiveness of the proposed two-stage algorithm for generating proactive project schedules with composite robustness. [ABSTRACT FROM AUTHOR]

While various approaches have been developed to minimize rebar cutting waste, such as optimizing cutting patterns and the lap splice position, reducing rebar usage by minimizing the number of splices remains uninvestigated. In response to these issues, a two-stage optimization algorithm was developed that prioritizes the use of special-length rebar to achieve a near-zero rebar cutting waste (N0RCW) of less than 1%, while also reducing overall rebar usage. The two-stage algorithm first optimizes the lap splice position for continuous rebar considering the use of a special-length rebar, which reduces the number of splices required. It then integrates a special-length minimization algorithm to combine the additional rebar. The algorithm was applied to beam structures in a small-sized factory building project, and it resulted in a notable reduction of 29.624 tons of rebar, equivalent to 12.31% of the total purchased quantity. Greenhouse gas emissions were reduced by 102.68 tons, and associated costs decreased by USD 30,256. A rebar cutting waste of 0.93%, which is near zero, was achieved. These findings highlight the significant potential of the proposed algorithm for reducing rebar waste and facilitating sustainable construction practices. The algorithm is also applicable to other reinforced concrete projects, where the associated advantages will be amplified accordingly. [ABSTRACT FROM AUTHOR]

After suffering from a grid blackout, distributed energy resources (DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points (SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed, and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.

This paper considers a multi-depot heterogeneous vehicle routing problem (MDHVRP) with time windows, which is very crucial for hazardous materials transportation. For this reason, we formalize this problem as a multi-objective MDHVRP optimization model, where the actual load dependent risk of hazardous materials transportation is considered. To solve the optimization problem, we propose a hybrid multi-objective evolutionary algorithm (HMOEA) and a two-stage algorithm (TSA). In addition, we verify the performance of the proposed algorithms by experiments on the modified Solomon’s VRPTW examples. In the experiment, it can be seen from the distribution of Pareto solution sets and the convergence distribution of IGD values that HMOEA is significantly superior to the other three algorithms in searching for Pareto solutions, as well as in the convergence and diversity of the algorithm. HMOEA and TSA were compared, and the minimum cost obtained by TSA was 13.38% lower than HMOEA, while the minimum risk was 81.69% higher than HMOEA. The advantages of each algorithm in finding solutions in reality were analyzed. A comparison was made between multi-depots heterogeneous VRP and multi-depots homogeneous VRP in the C101 instance, and the results showed that scheduling heterogeneous vehicles would reduce risk and cost.

By the asymptotic oracle property, non-convex penalties represented by minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) have attracted much attentions in high-dimensional data analysis, and have been widely used in signal processing, image restoration, matrix estimation, etc. However, in view of their non-convex and non-smooth characteristics, they are computationally challenging. Almost all existing algorithms converge locally, and the proper selection of initial values is crucial. Therefore, in actual operation, they often combine a warm-starting technique to meet the rigid requirement that the initial value must be sufficiently close to the optimal solution of the corresponding problem. In this paper, based on the DC (difference of convex functions) property of MCP and SCAD penalties, we aim to design a global two-stage algorithm for the high-dimensional least squares linear regression problems. A key idea for making the proposed algorithm to be efficient is to use the primal dual active set with continuation (PDASC) method to solve the corresponding sub-problems. Theoretically, we not only prove the global convergence of the proposed algorithm, but also verify that the generated iterative sequence converges to a d-stationary point. In terms of computational performance, the abundant research of simulation and real data show that the algorithm in this paper is superior to the latest SSN method and the classic coordinate descent (CD) algorithm for solving non-convex penalized high-dimensional linear regression problems. [ABSTRACT FROM AUTHOR]

The reasonable scheduling of agricultural machinery can avoid their purposeless flow during the operational service and reduce the scheduling cost of agricultural machinery service centers. In this research, a multiregional agricultural machinery scheduling model with a time window was established considering the timeliness of agricultural machinery operation. This model was divided into two stages: In the first stage, regions were divided through the Voronoi diagram, and farmlands were distributed to intraregional service centers. In the second stage, the model was solved using the hybrid particle swarm optimization (HPSO). The algorithm improves the performance of the algorithm by introducing a crossover, mutation, and particle elimination mechanism, and by using a linear differential to reduce the inertia weight and trigonometric function learning factor. Next, the accuracy and effectiveness of the algorithm are verified by different experimental samples. The results show that the algorithm can effectively reduce the scheduling cost, and has the advantages of strong global optimization ability, high stability, and fast convergence speed. Subsequent algorithm comparison proves that HPSO has better performance in different situations, can effectively solve the scheduling problem, and provides a reasonable scheduling scheme for multiarea and multifarmland operations. [ABSTRACT FROM AUTHOR]

Summary: Principal Component Analysis (PCA) is a popular multivariate analytic tool which can be used for dimension reduction without losing much information. Data vectors containing a large number of features arriving sequentially may be correlated with each other. An effective algorithm for such situations is online PCA. Existing Online PCA research works revolve around proposing efficient scalable updating algorithms focusing on compression loss only. They do not take into account the size of the dataset at which further arrival of data vectors can be terminated and dimension reduction can be applied. It is well known that the dataset size contributes to reducing the compression loss – the smaller the dataset size, the larger the compression loss while larger the dataset size, the lesser the compression loss. However, the reduction in compression loss by increasing dataset size will increase the total data collection cost. In this paper, we move beyond the scalability and updation problems related to Online PCA and focus on optimising a cost‐compression loss which considers the compression loss and data collection cost. We minimise the corresponding risk using a two‐stage PCA algorithm. The resulting two‐stage algorithm is a fast and an efficient alternative to Online PCA and is shown to exhibit attractive convergence properties with no assumption on specific data distributions. Experimental studies demonstrate similar results and further illustrations are provided using real data. As an extension, a multi‐stage PCA algorithm is discussed as well. Given the time complexity, the two‐stage PCA algorithm is emphasised over the multi‐stage PCA algorithm for online data. [ABSTRACT FROM AUTHOR]

Aiming at the problems of complex urban road network, low efficiency of logistics distribution, and the difficulty of large-scale logistics distribution area division and routing planning, this paper proposes a two-stage logistics distribution vehicle routing optimization (VRP) method based on the establishment of a multi-factor complex road network constrained logistics distribution mathematical model. Considering the complex traffic elements and road network topological structure in logistics and distribution, in the first stage, a heuristic simulated annealing (HSA) distribution region partitioning algorithm is proposed with the objective of balancing vehicle task load to divide the urban logistics distribution network under complex road networks, so as to reduce the region scale and path search cost. In the second stage of route decision making, aiming at minimizing the total cost of logistics distribution, combining the VRP problem with complex road network conditions, a heuristic path search method combined with complex road network model constraints is proposed. In this stage, a hybrid genetic beam search(HGBS) algorithm is used to plan the path nodes, reduce the randomness of the model in the initial search for paths by heuristic genetic algorithms, then combine with Beam Search methods to reduce the space and time used for the search, and use optimization algorithms to improve the accuracy of independent sub-region routing optimization and the rationality of overall physical distribution route selection. Finally, the proposed method is validated in this paper with two practical cases. The experimental results show that the two-stage decision-making algorithm proposed in this paper has certain advantages in partitioning schemes, minimizing total cost and iteration times. Through comparison, the optimization ability of this method for logistics distribution networks is proved.

While various approaches have been developed to minimize rebar cutting waste, such as optimizing cutting patterns and the lap splice position, reducing rebar usage by minimizing the number of splices remains uninvestigated. In response to these issues, a two-stage optimization algorithm was developed that prioritizes the use of special-length rebar to achieve a near-zero rebar cutting waste (N0RCW) of less than 1%, while also reducing overall rebar usage. The two-stage algorithm first optimizes the lap splice position for continuous rebar considering the use of a special-length rebar, which reduces the number of splices required. It then integrates a special-length minimization algorithm to combine the additional rebar. The algorithm was applied to beam structures in a small-sized factory building project, and it resulted in a notable reduction of 29.624 tons of rebar, equivalent to 12.31% of the total purchased quantity. Greenhouse gas emissions were reduced by 102.68 tons, and associated costs decreased by USD 30,256. A rebar cutting waste of 0.93%, which is near zero, was achieved. These findings highlight the significant potential of the proposed algorithm for reducing rebar waste and facilitating sustainable construction practices. The algorithm is also applicable to other reinforced concrete projects, where the associated advantages will be amplified accordingly.

Electric vehicle routing problem (EVRP) is a special vehicle routing problem, which owns several specific characteristics of EV technology including the limited charging stations and the limited cruising range of EVs. This paper proposes a two-stage heuristic algorithm for EVRP with a kind of pseudo node, termed EVRPPN-TSH, which not only contributes to the model innovation but also to the algorithm design. In model innovation, the pseudo node is introduced into the EVRP model, forming EVRPPN, to reduce the search space and time complexity for obtaining service orders of customers from O (n) to O (1). Moreover, different from other research works which only use total distance as the fitness function, an improved fitness function is proposed, which takes all the distance, the electricity and capacity constraints compliance into consideration. In algorithm design, we first adopt the two-stage idea, which divides EVRP into the capacitated vehicle routing problem (CVRP) and a fixed route vehicle charging problem (FRVCP). Then we design a two-stage heuristic algorithm, termed as TSH. Specifically, for CVRP, a route division heuristic algorithm and a depot move heuristic algorithm are designed to divide routes. Further, an adjustment strategy with three operators is designed for adjusting the customer order. For FRVCP, an existing heuristic algorithm is adopted and enhanced by a variable extent shaking algorithm to avoid local trapped. Experimental results show that EVRPPN-TSH generally outperforms other state-of-the-art algorithms. Moreover, EVRPPN-TSH can obtain the best-known solution in all the 7 test cases, and update the best-known solution in test case E101 from 834.84 to 834.22. • In model innovation, we introduce pseudo node in EVRP model to reduce complexity. • An improved fitness function which considers constraint compliance is proposed. • In algorithm design, we propose two-stage heuristic approach to improve performance. • Illustrate the superiority of the improved model and the proposed algorithm. [ABSTRACT FROM AUTHOR]

• An optimization model for precast concrete component transportation to minimize costs is developed; • Heterogeneous vehicle and process of transportation task assignment are incorporated as constraints; • An improved two-stage ACO-Dijkstra algorithm is designed based on the shortest path idea and the improved transition probability equation; • Optimal transportation plans with minimum costs can be quickly generated with real-time data to improve decision-making for precast concrete plants; • Computational results denote that heterogeneous vehicles with hybrid time windows contribute to transportation cost decrease. Recently, prefabricated construction has been vigorously promoted, resulting in high demand for precast concrete (PC) components. The transportation scheduling optimization problem of PC components with various kinds from multiple projects arises. Unlike conventional cargo, PC components are characterized by shape heterogeneity, large volume, and strict delivery time limits. Based on three characteristics, a heterogeneous fixed fleet vehicle routing problem (HFFVRP) for PC components is introduced, where heterogeneous vehicles, allocation of PC components to size-matching vehicles, and hybrid time windows are considered. Then a two-stage solution strategy based on the improved ant colony optimization (ACO) and Dijkstra algorithm is designed to obtain optimal vehicle routes under minimum transportation costs. The results indicate that the improved ACO-Dijkstra algorithm outperforms in obtaining optimal transportation plans for heterogeneous vehicles compared with manual decision-making and other heuristic algorithms. Sensitivity analysis denotes that utilizing heterogeneous vehicles contributes to reductions in transportation costs, and vehicle configuration should be adjusted along with demand scales. The proposed model and algorithm extend the theoretical basis of construction industrial applications. [ABSTRACT FROM AUTHOR]

Based on the cluster, a new model of truck and drone joint distribution is proposed to solve the problem of difficult door-to-door delivery in rural areas. Considering the load and endurance of the drones, a mixed integer programming model with time windows is established to minimize the total operating cost, and a two-stage algorithm is proposed. First, the truck stops are calculated through the improved K-means algorithm, and then genetic simulated annealing algorithm is adopted to optimize the joint distribution route of trucks and drones. Comparing with the traditional K-means algorithm plus CPLEX results, it can prove the feasibility and effectiveness of the algorithm and model. The case study selects a rural area in Jiangsu for the application research of terminal logistics distribution. The results show that the joint delivery model of truck and drone can effectively reduce the total operating cost compared with the pure truck transportation model. The research results can provide new ideas and reference value for the application of drones in terminal distribution in rural areas. [ABSTRACT FROM AUTHOR]

The traditional least squares (LS) and gradient descent (GD) algorithms can estimate the parameters of the regression models. They can be inefficient when the models have complex structures: (1) the unknown parameters in the information vector make the algorithm be impossible to update the parameters; (2) the zigzagging nature of the gradient descent algorithm and the complex structures lead to slow convergence rates; and (3) the step-size and derivative function calculations may be unsolvable for complex nonlinear models. This paper proposes two kinds of algorithms for exponential nonlinear models. The first is the two-stage algorithm, which decomposes the complex model into a linear part and a nonlinear part, where the linear part is estimated using the LS algorithm and the nonlinear part is identified based on the GD algorithm. The second is the particle swarm optimization algorithm which can simultaneously obtain all the parameters. To increase the convergence rates, the Aitken method is also introduced. The simulation results demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Introduction: Identifying predictors of patient outcomes evaluated over time may require modeling interactions among variables while addressing within-subject correlation. Generalized linear mixed models (GLMMs) and generalized estimating equations (GEEs) address within-subject correlation, but identifying interactions can be difficult if not hypothesized a priori. We evaluate the performance of several variable selection approaches for clustered binary outcomes to provide guidance for choosing between the methods. Methods: We conducted simulations comparing stepwise selection, penalized GLMM, boosted GLMM, and boosted GEE for variable selection considering main effects and two-way interactions in data with repeatedly measured binary outcomes and evaluate a two-stage approach to reduce bias and error in parameter estimates. We compared these approaches in real data applications: hypothermia during surgery and treatment response in lupus nephritis. Results: Penalized and boosted approaches recovered correct predictors and interactions more frequently than stepwise selection. Penalized GLMM recovered correct predictors more often than boosting, but included many spurious predictors. Boosted GLMM yielded parsimonious models and identified correct predictors well at large sample and effect sizes, but required excessive computation time. Boosted GEE was computationally efficient and selected relatively parsimonious models, offering a compromise between computation and parsimony. The two-stage approach reduced the bias and error in regression parameters in all approaches. Conclusion: Penalized and boosted approaches are effective for variable selection in data with clustered binary outcomes. The two-stage approach reduces bias and error and should be applied regardless of method. We provide guidance for choosing the most appropriate method in real applications. [ABSTRACT FROM AUTHOR]

Industry 4.0 is a concept that assists companies in developing a modern supply chain (MSC) system when they are faced with a dynamic process. Because Industry 4.0 focuses on mobility and real-time integration, it is a good framework for a dynamic vehicle routing problem (DVRP). This research works on DVRP. The aim of this research is to minimize transportation cost without exceeding the capacity constraint of each vehicle while serving customer demands from a common depot. Meanwhile, new orders arrive at a specific time into the system while the vehicles are executing the delivery of existing orders. This paper presents a two-stage hybrid algorithm for solving the DVRP. In the first stage, construction algorithms are applied to develop the initial route. In the second stage, improvement algorithms are applied. Experimental results were designed for different sizes of problems. Analysis results show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract Introduction: Identifying predictors of patient outcomes evaluated over time may require modeling interactions among variables while addressing within-subject correlation. Generalized linear mixed models (GLMMs) and generalized estimating equations (GEEs) address within-subject correlation, but identifying interactions can be difficult if not hypothesized a priori. We evaluate the performance of several variable selection approaches for clustered binary outcomes to provide guidance for choosing between the methods. Methods: We conducted simulations comparing stepwise selection, penalized GLMM, boosted GLMM, and boosted GEE for variable selection considering main effects and two-way interactions in data with repeatedly measured binary outcomes and evaluate a two-stage approach to reduce bias and error in parameter estimates. We compared these approaches in real data applications: hypothermia during surgery and treatment response in lupus nephritis. Results: Penalized and boosted approaches recovered correct predictors and interactions more frequently than stepwise selection. Penalized GLMM recovered correct predictors more often than boosting, but included many spurious predictors. Boosted GLMM yielded parsimonious models and identified correct predictors well at large sample and effect sizes, but required excessive computation time. Boosted GEE was computationally efficient and selected relatively parsimonious models, offering a compromise between computation and parsimony. The two-stage approach reduced the bias and error in regression parameters in all approaches. Conclusion: Penalized and boosted approaches are effective for variable selection in data with clustered binary outcomes. The two-stage approach reduces bias and error and should be applied regardless of method. We provide guidance for choosing the most appropriate method in real applications.

This paper proposes a School Bus Stop location and Routing Problem with Walking Accessibility and Mixed Load (SBSLRP-WA-ML), where the individual difference of walking accessibilities among students and the possibility of serving students attending different schools with the same bus simultaneously are considered. We first develop a mixed integer programming model for SBSLRP-WA-ML with the objective of minimizing the total commuting time, including walking time from the residence to school, in-vehicle travel time, and service time at stops. A two-stage solution method is then developed. In stage 1, an iterative clustering method based on k-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to locate bus stops aiming at minimizing the number of stops subject to various walking accessibilities. In stage 2, an improved ant colony optimization algorithm (IACO) integrating two local search operators is devised, which is used to generate bus routes with minimal total commuting time. A number of instances of different sizes are generated to verify the solution approach, and the influential factors with respect to total commuting time are analyzed. The model is also compared to the door-to-door school bus services. Comparison to similar methods and sensitivity analysis of parameters are also conducted to analyze the performance and robustness of the proposed approach.

Storage space allocation and handling operation problems are two main crucial problems in container terminals. Early research efforts, however, are seldom devoted to studying them together. Therefore, this paper considers these two problems simultaneously for outbound containers in rail-water intermodal container terminals (RWICTs), where rail-mounted gantry cranes, inner trucks, and quay cranes are involved. A two-stage problem is proposed: Stage 1 is to determine locations of the containers and reduce the overlapping amount, considering container weight, departure time, destination ports, and containers left from earlier planning periods in railway container yards, according to the locations of containers from Stage 1; and Stage 2 aims at obtaining optimal job sequences of different types of equipment and minimizing makespans of handling operations, considering some operational constraints, particularly rehandling time and inner truck congestion. To solve the problem, a two-stage heuristic algorithm is proposed, where the rolling planning horizon and a new update strategy are introduced. A heuristic algorithm is introduced in Stage 1 and a novel two-layer genetic algorithm is proposed in Stage 2, which introduces proximity principles and the reselection operation. Afterward, the results from Stage 2 are used to resolve the first stage problem, while the problem in Stage 2 is also resolved using the new results from Stage 1. This iterative process continues until there are no more improvements in Stage 1. Finally, the results of the computational experiments indicate that the proposed model and solution approaches are effective and efficient in solving the two-stage problem for outbound containers in RWICTs.

An iterative two-stage proximal algorithm for approximate solution of equilibrium problems in Hadamard spaces is considered. This algorithm is an analog of the already studied two-stage algorithm for equilibrium problems in a Hilbert space. For Lipschitz-type pseudo-monotone bifunctions, a theorem on the weak convergence of sequences generated by the algorithm is proved. [ABSTRACT FROM AUTHOR]

Copyright of Cybernetics & Systems Analysis / Kibernetiki i Sistemnyj Analiz is the property of V.M. Glushkov Institute of Cybernetics of NAS of Ukraine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To improve the punctuality rate of flights and reduce the impact of adverse weather on flights，the study of real-time air traffic flow management is carried out，considering holding strategy and rerouting strategy. A mathematical model which minimizes the total flight duration from the start point of the rerouting to the end point of the rerouting is established，and a two-stage algorithm is proposed to solve it. First，a genetic algorithm is applied to optimize the holding time and rerouting path. Then，the path is adjusted to be the best one based on the idea of“transformation from curl to straight”and“dichotomy”. The simulation results show that the proposed algorithm could find better solutions than the geometric tangent method； compared with static diversion，airspace utilization is improved；compared with the single holding strategy， the total flight duration is reduced by 17.04%；compared with the single rerouting strategy，the total flight duration is reduced by 3.98%. It verifies the effectiveness of aircraft holding time and rerouting path real-time integrated optimization under adverse weather in the real-time air traffic flow management. [ABSTRACT FROM AUTHOR]

Subscription bus services (SBS) is a convenient and low-carbon rapid transport mode for passengers' daily commute. The network design becomes a vital problem because it closely relates to both operators' profit and passengers' daily convenient traveling. In this paper, a two-stage model is formulated to optimize the subscription bus services network design (SBSND). During the first stage we minimize the total service distance and the number of vehicles as a single target objective function; vehicle capacity utilization rate, service time, ratio of service distance and linear distance between origin and destination are limited by means of constraints. During the second stage we share the same objective function and similar constraints used during the first stage, but the parameter settings of the constraints are different. Correspondingly, a two-stage algorithm is also designed to solve the SBSND problem. Firstly, we obtain the possible service lines, match the passengers and bus capacity. Next, we use Dijkstra to obtain the shortest SBS operation lines. Finally, we form the SBS network. The three phases are the main processes about the algorithm during both stages. The comparison between existing SBS in Chengdu city and the optimized SBS shows the high efficiency of the optimization model and algorithm formulated in this paper: the operation line length increases from 250.6 to 300.9 km; only 40 passengers have no SBS after optimization; the average operation time is reduced from 56.8 to 50.2 min. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern software systems often consist of many different components, each with a number of options. Although unit tests may reveal faulty options for individual components, functionally correct components may interact in unforeseen ways to cause a fault. Covering arrays are used to test for interactions among components systematically. A two‐stage framework, providing a number of concrete algorithms, is developed for the efficient construction of covering arrays. In the first stage, a time and memory efficient randomized algorithm covers most of the interactions. In the second stage, a more sophisticated search covers the remainder in relatively few tests. In this way, the storage limitations of the sophisticated search algorithms are avoided; hence, the range of the number of components for which the algorithm can be applied is extended, without increasing the number of tests. Many of the framework instantiations can be tuned to optimize a memory‐quality trade‐off, so that fewer tests can be achieved using more memory. [ABSTRACT FROM AUTHOR]

Abstract Data centers are special-purpose facilities that enable customers to perform cloud based real-time online transactions and rigorous computing operations. Service levels of data center facilities are characterized by response time between query and action, which to a large extent depends on data center location and data travel distance. Another aspect of service level is resource up-time availability, which is determined by data center configuration. Data center location and configuration decisions are, therefore, of great significance to ensure uninterrupted operations in customers of manufacturing and service industries relying on cloud-based computing resources. In this study, following a grid-based location approach, we present two mixed integer linear programming models for capacitated single-source data center location-allocation problems. The first model provides optimal locations, capacities and configurations of data centers, and allocation of demands to open facilities when there is no existing facilities in the region. Our second model considers the decision problem of meeting new demand when the existing demand is met by the already opened facilities. We term these newly arrived demand as replication demand, which results either from emergence of new users of existing customers at distant locations in the future, or as a means of increasing data resilience by creating data replication as a backup. To solve the decision problem for meeting primary and replication demand optimally, we propose a two-stage decision algorithm. The algorithm provides optimal locations, capacities and configurations for new data centers, capacity addition decisions to the existing facilities and subsequent allocation of demands. Both models and solution algorithm are implemented using AMPL programming language and solved with CPLEX solver. The models are found to be scalable and capable to provide high quality solutions in reasonable time. Highlights • Two mixed integer linear programming models are developed for a capacitated data center location-allocation problem. • To ensure a reliable and resilient service, replication demand is considered in our proposed framework. • A two-stage decision algorithm is proposed to solve the decision problem for meeting primary and replication demand. • The proposed models are found to be scalable and our proposed algorithm is capable of providing fast solution. [ABSTRACT FROM AUTHOR]

To analyze the effect of carbon emission quota allocation on the locational marginal price (LMP) of day-ahead electricity markets, this paper proposes a two-stage algorithm. For the first stage of the algorithm, a multi-objective optimization model is established to simultaneously minimize the total costs and carbon emission costs of power systems. Hence, an evenly distributed Pareto optimal solution can be solved effectively by means of the normalized normal constraint method. For the second stage, a tracing model is built with the goal of minimizing the total costs of power systems and satisfying the constraints generated based on the Pareto optimal solution obtained from the first stage. Furthermore, the influence of carbon emission quota allocation on the LMP of electricity markets is analyzed, and different schemes to allocate carbon emission quotas are evaluated on a real 1560-bus and 52-unit system.

This study presents a novel mode-based energy storage control approach. Assuming that an energy storage device (ESD) is equipped with a set of predetermined real-time control modes, the dispatch objective is to select an optimal mode instead of a continuous charging or discharging power value. A two-stage algorithm is developed for mode selection. In the first stage, a sliding 24 h economic model predictive control algorithm is used to determine the power outputs of the ESD for the next 24 h. In the second stage, the output sign for the next time step determines the class of modes to be elected (charging or discharging). The information from the first stage is used to compute the total cost for each selected mode. The mode with the lowest day-ahead cost is chosen. The residential electricity consumption data collected in the PECAN Street Project is used in the simulation to validate the performance of the proposed algorithm. Simulation results show that using a mode-based approach reduces the sensitivity to forecasting errors along with load and solar variability. The algorithm performance is consistent across different load patterns.

A two-stage algorithm is proposed for the estimation of the fundamental frequency of asynchronously sampled signals in power systems. In the first stage, time-domain interpolation reconstructs the power system signal at a new sampling time and the reconstructed signal passes through a tuned sine filter to eliminate harmonics. In the second stage, the fundamental frequency is estimated using a modified curve fitting, which is robust to noise. The evaluation results confirm the efficiency and validity of the two-stage algorithm for accurate estimation of the fundamental frequency even for asynchronously sampled signals contaminated with noise, harmonics, and an inter-harmonic component.

This article presents a variable neighborhood search (VNS) algorithm for the consistent vehicle routing problem (ConVRP). ConVRP is a variant of the vehicle routing problem (VRP). In ConVRP, vehicle routes must be designed for multiple days, and each customer must be visited by the same driver at approximately an identical time on each day. VNS is an efficient algorithmic framework and is widely used. The proposed algorithm consists of two stages. In the first stage, VNS is applied to obtain approximately optimized solutions. The solutions obtained might be infeasible. If a solution is of acceptable quality, the second stage is applied to make it feasible and optimize it further. Several techniques are employed to reduce computation time of the local search stage. A special shaking method is introduced and proofed to be more effective than ordinary methods by experiments. A new method for computing time difference excess is proposed to solve the problem that change of time difference excess caused by operations on an individual day is not obvious. The proposed algorithm is tested on the benchmark ConVRP data set and compared with extant ConVRP approaches from the literature. The results demonstrate that VNS outperforms all the extant ConVRP approaches in terms of quality of solutions obtained. [ABSTRACT FROM AUTHOR]