Neighborhood Search Research Articles

Multi-compartment waste collection vehicle routing problem with bin washer

This paper introduces a new variant of the Multi-Compartment Waste Collection Problem (MC-WCP), which we call the MC-WCP with Bin Washer (MC-WCP-BW). The problem involves a fleet of compressed refuse collection trucks equipped with a bin washer, which compresses each waste type in the corresponding compartment of the vehicle. The vehicles can also wash the bins. Separation sites and water refilling stations are considered in the problem. A subset of the bins must be washed when emptied, which is not mandatory for the others. The problem is modeled using a mixed-integer linear programming formulation incorporating multiple visits to separation sites and water refilling stations. An efficient Hybrid Variable Neighborhood Search (HVNS) algorithm is developed and evaluated on a set of instances from the literature and newly generated instances. Computational experiments show that our proposed algorithm can identify solutions of better quality in a shorter computational time, as compared with the current state-of-the-art algorithms. The potential benefits of the compaction operation under several scenarios are also examined via extensive analyses.

A practical vehicle routing problem in small and medium cities for fuel consumption minimization

Today, reducing fossil fuel consumption in the transport sector is one of the important requirements to preserve the ecological environment. In this paper, we investigated a logistics problem in small and medium cities. We proposes the time-dependent vehicle routing problem model in small and medium cities considering heterogeneous fleets, time windows, multi-trip, time dependency, and road network. We conducted a numerical analysis on fuel consumption minimizing path selection between two customer nodes and found that the path selection is independent of the payload in most cases. The model is formulated as a mathematical integer linear programming model, and small problems are solved using a commercial optimization solver. For large-scale instances, we proposed a heuristic algorithm based on the variable neighborhood search and evaluated its performance. The proposed algorithm can decrease fuel consumption by 25 % compared to manual routing and scheduling. Furthermore, for provide useful managerial insights, sensitivity analyses were performed considering different vehicle capacities.

Intelligent bulk cargo terminal scheduling based on a novel chaotic-optimal thermodynamic evolutionary algorithm

This paper presents a chaotic optimal thermodynamic evolutionary algorithm (COTEA) designed to address the integrated scheduling problems of berth allocation, ship unloader scheduling, and yard allocation at bulk cargo terminals. Our proposed COTEA introduces a thermal transition crossover method that effectively circumvents local optima in the scheduling solution process. Additionally, the method innovatively combines a good point set with chaotic dynamics within an integrated initialization framework, thereby cultivating a robust and exploratory initial population for the optimization algorithm. To further enhance the selection process, our paper proposes a refined parental selection protocol that employs a quantified hypervolume contribution metric to discern superior candidate solutions. Postevolution, our algorithm employs a Cauchy inverse cumulative distribution-based neighborhood search to effectively explore and enhance the solution spaces, significantly accelerating the convergence speed during the scheduling solution process. The proposed method is adept at achieving multiobjective optimization, simultaneously improving the service level and reducing costs for bulk cargo terminals, which in turn boosts their competitiveness. The effectiveness of our COTEA is demonstrated through extensive numerical simulations.

Streamflow Prediction Using Complex Networks.

The reliable prediction of streamflow is crucial for various water resources, environmental, and ecosystem applications. The current study employs a complex networks-based approach for the prediction of streamflow. The approach consists of three major steps: (1) the formation of a network using streamflow time series; (2) the calculation of the clustering coefficient (CC) as a network measure; and (3) the use of a clustering coefficient-based nearest neighbor search procedure for streamflow prediction. For network construction, each timestep is considered as a node and the existence of link between any node pair is identified based on the difference (distance) between the streamflow values of the nodes. Different distance threshold values are used to identify the critical distance threshold to form the network. The complex networks-based approach is implemented for the prediction of daily streamflow at 142 stations in the contiguous United States. The prediction accuracy is quantified using three statistical measures: correlation coefficient (R), normalized root mean square error (NRMSE), and Nash-Sutcliffe efficiency (NSE). The influence of the number of neighbors on the prediction accuracy is also investigated. The results, obtained with the critical distance threshold, reveal that the clustering coefficients for the 142 stations range from 0.799 to 0.999. Overall, the prediction approach yields reasonably good results for all 142 stations, with R values ranging from 0.05 to 0.99, NRMSE values ranging from 0.1 to 12.3, and the NSE values ranging from -0.89 to 0.99. An attempt is also made to examine the relationship between prediction accuracy and the catchment characteristics/streamflow statistical properties (drainage area, mean flow, coefficient of variation of flow). The results suggest that the prediction accuracy does not have much of a relationship with the drainage area and the mean streamflow values, but with the coefficient of variation of flow. The outcomes from this study are certainly promising regarding the application of complex networks-based concepts for the prediction of streamflow (and other hydrologic) time series.

Daniel Huici - Bringing Approximate K-Nearest Neighbor Search to Similarity Digests

Daniel Huici - Bringing Approximate K-Nearest Neighbor Search to Similarity Digests

An improved mountain gazelle optimizer based on chaotic map and spiral disturbance for medical feature selection.

Feature selection is an important solution for dealing with high-dimensional data in the fields of machine learning and data mining. In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. First, the gazelle population is initialized using iterative chaotic map with infinite collapses (ICMIC) mapping, which increases the diversity of the population. Second, a nonlinear control factor is introduced to balance the exploration and exploitation components of the algorithm. Individuals in the population are perturbed using a spiral perturbation mechanism to enhance the local search capability of the algorithm. Finally, a neighborhood search strategy is used for the optimal individuals to enhance the exploitation and convergence capabilities of the algorithm. The superior ability of the IMGO algorithm to solve continuous problems is demonstrated on 23 benchmark datasets. Then, BIMGO is evaluated on 16 medical datasets of different dimensions and compared with 8 well-known metaheuristic algorithms. The experimental results indicate that BIMGO outperforms the competing algorithms in terms of the fitness value, number of selected features and sensitivity. In addition, the statistical results of the experiments demonstrate the significantly superior ability of BIMGO to select the most effective features in medical datasets.

GSearch: ultra-fast and scalable genome search by combining K-mer hashing with hierarchical navigable small world graphs.

Genome search and/or classification typically involves finding the best-match database (reference) genomes and has become increasingly challenging due to the growing number of available database genomes and the fact that traditional methods do not scale well with large databases. By combining k-mer hashing-based probabilistic data structures (i.e. ProbMinHash, SuperMinHash, Densified MinHash and SetSketch) to estimate genomic distance, with a graph based nearest neighbor search algorithm (Hierarchical Navigable Small World Graphs, or HNSW), we created a new data structure and developed an associated computer program, GSearch, that is orders of magnitude faster than alternative tools while maintaining high accuracy and low memory usage. For example, GSearch can search 8000 query genomes against all available microbial or viral genomes for their best matches (n= ∼318 000 or ∼3 000 000, respectively) within a few minutes on a personal laptop, using ∼6 GB of memory (2.5 GB via SetSketch). Notably, GSearch has an O(log(N)) time complexity and will scale well with billions of genomes based on a database splitting strategy. Further, GSearch implements a three-step search strategy depending on the degree of novelty of the query genomes to maximize specificity and sensitivity. Therefore, GSearch solves a major bottleneck of microbiome studies that require genome search and/or classification.

Active learning based hyper-heuristic for the integration of production and Transportation: A third-party logistics perspective

The integration problem of production and transportation (IPPT) is one of the most important decision issues in real-life manufacturing and flow industries. To illustrate its potential for improving difficult production and transportation processes, we focus on a challenging case of IPPT, namely the distributed no-wait flow-shop and transportation integrated scheduling problem (DNWFSTISP) considering third party logistics (3PL), which occurs in many practical industries. DNWFSTISP consists of integrating the well-known distributed no-wait flow-shop scheduling and dedicated vehicle routing in the sense of 3PL while minimizing the total cost. First, we investigate the integration approach of the production and transportation of DNWFSTISP and present three problem-specific loading heuristics, which account for both a make-to-order strategy and 3PL providers to improve customer satisfaction. Second, we propose an active learning based hyper-heuristic algorithm (ALHHA) to solve the problem. ALHHA is unique in that it relies on an active learning based high-level exploration to discover promising search regions and a neighborhood search based low-level exploitation to intensively examine given search regions. Third, we assess the composing ingredients of the proposed ALHHA on 40 instances with up to 200 jobs and 7 factories to shed light on their impacts on the performance of the algorithm. Moreover, we make the 40 instances and solved upper bounds publicly available, which would be valuable for future research on DNWFSTISP.

Optimal life-testing plans for joint progressively Type-II censored Birnbaum-Saunders distributions with compound optimal criteria

ABSTRACT In this paper, we first derive the maximum likelihood estimates (MLEs) of the parameters of Birnbaum-Saunders (BISA) distributions based on the joint progressive Type-II censored (JPC) samples. We also discuss using the EM algorithm to obtain the MLEs of the model parameters. Then, we determine the single-objective-criterion optimal JPC schemes for BISA distributions based on the cost minimization criterion, A -optimality criterion, and D -optimality criterion by the complete search method, variable neighborhood search (VNS) algorithm and modified VNS (MVNS) algorithm. These algorithms are compared in terms of accuracy and computation time. In addition, we determine the compound-criterion optimal JPC schemes based on different optimal criteria and the reasonably efficient compound optimal JPC schemes for two competing statistical models, such as the inverse Gaussian and BISA models. The advantages of using the compound optimal scheme over the single-objective-criterion optimal scheme are demonstrated through a real-life data set on the micro-indentation test about the hardness of polymeric bone cement.

Numerical implementation of an extended multi-resolution SPH-PD coupling model for fluid-structure interaction problem with impacting failure

Considering the fluid-structure impacting failure problems with large size, this paper presents an extended multi-resolution smoothed particle hydrodynamics (SPH)-peridynamics (PD) coupling model. The influence domain and the interpolation smoothing length are innovatively introduced to reconstruct governing equations for interactions between particles of diverse resolutions, ensuring system-wide momentum conservation. Then, an adaptive multi-level cell neighborhood search (AMCNS) algorithm is presented, designed to reduce the time of neighborhood search in multi-resolution simulations. Several 2D and 3D validation tests demonstrate the accuracy of the multi-resolution SPH-PD model in describing solid deformation and fluid impact pressure. The multi-resolution SPH-PD model significantly enhances computational efficiency and can depict the process of structural failure under conditions where a wide difference in scale between waves and structures.

Integrated optimization of truck appointment quotas and container relocations for multiple blocks considering transshipment containers handling

The container terminal is a crucial transshipment node in the sea and land cargo transport supply chain, and the efficiency of container collection and distribution has always been a research hotspot. In this paper, we consider the impact of transshipment containers resulting from various uncertainties and integrate the operations of external truck pickups with the handling of transshipment containers in the import yard of the container terminal. In light of the varying number of transshipment containers and transit times for different blocks, coupled with the unpredictable arrival order of external trucks leading to the unknown number of relocations, it is proposed to dynamically adjust the upper limit of the appointed quota for each block. We establish a two stage mixed integer programming model for the truck appointment quota and the block relocation problem. The objective functions aim to minimize the number of relocations and minimize the number of yard crane moves under the maximal appointment quota, respectively. This model is utilized for dynamically regulating the upper limit of the appointed quota for each block and determining the time intervals for picking up the appointed and transshipment containers, along with the yard crane scheduling scheme. The problem is solved using CPLEX and the improved variable neighborhood search algorithm (IVNS). The IVNS incorporates the correction strategy, decoding strategy, and scheduling rules designed to align closely with the characteristics of the problem. Through scheme comparison and sensitivity analysis, five management insights are proposed for terminals. Experimental results demonstrate that the approach presented in this paper effectively enhances the utilization efficiency of yard cranes, reduces yard congestion, and yields benefits for both terminals and trucking companies.

Utilizing Heuristics and Metaheuristics for Solving the Set Covering Problem

A basic combinatorial optimization problem, the Set Covering Problem (SCP) finds extensive use in computer science, operations research, and logistics, among other domains. The SCP’s goal is to determine the smallest number of subsets, or sets, needed to cover every element precisely once given a finite set of items and a collection of subsets of these elements. Numerous practical uses for the SCP exist, such as crew scheduling, truck routing, and facility locating. This paper focuses on obtaining feasible solutions, applying to the obtained solutions constructive heuristics (CH), followed by a redundancy elimination procedure to remove unnecessary sets. To further optimize the quality of the solution, a local search method is also implemented based on the First and Best improvement algorithms. Additionally, the Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Search (VNS) metaheuristics are designed and implemented. Each implemented heuristic underwent testing across 42 instances, with the average deviation from the optimal solution calculated for each instance. The GRASP heuristic demonstrated the most favorable performance, achieving a maximum deviation of 2.26% from the optimal solution, while the VNS approach yielded a maximum deviation of 11.46% from the optimal solution at its best.

Utilizing Heuristics and Metaheuristics for Solving the Set Covering Problem

A basic combinatorial optimization problem, the Set Covering Problem (SCP) finds extensive use in computer science, operations research, and logistics, among other domains. The SCP’s goal is to determine the smallest number of subsets, or sets, needed to cover every element precisely once given a finite set of items and a collection of subsets of these elements. Numerous practical uses for the SCP exist, such as crew scheduling, truck routing, and facility locating. This paper focuses on obtaining feasible solutions, applying to the obtained solutions constructive heuristics (CH), followed by a redundancy elimination procedure to remove unnecessary sets. To further optimize the quality of the solution, a local search method is also implemented based on the First and Best improvement algorithms. Additionally, the Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Search (VNS) metaheuristics are designed and implemented. Each implemented heuristic underwent testing across 42 instances, with the average deviation from the optimal solution calculated for each instance. The GRASP heuristic demonstrated the most favorable performance, achieving a maximum deviation of 2.26% from the optimal solution, while the VNS approach yielded a maximum deviation of 11.46% from the optimal solution at its best.

Peridynamics contact model: Application to healing using phase field theory

This paper presents a contact algorithm in non-ordinary state-based peridynamics (NOSB-PD) framework and its application in healing phase field (HPF) theory. The idea here is to exploit the nonlocality in PD to model the contact between two deformable bodies. The proposed method does not require artificial short-range force or additional degrees of freedom, e.g., Lagrange multiplier, which are often used in the conventional classical and PD contact models. In the regions where contact is likely to take place, neighbor search is performed at every time step and extra particles entering the horizon are identified. These extra particles form artificial bonds which transfer forces only in compression. This feature is essential for rebound simulation. Envisioning surface-to-surface bonding as the opposite process of cracking, we propose an HPF theory to model high velocity impact induced bonding between two material bodies. Here an HPF based bonding criterion is postulated and integrated with the proposed contact algorithm. The solution of the governing equations is obtained using the fourth order Runge-Kutta method. The efficacy of the proposed contact algorithm is demonstrated by simulations of impact and rebound of two plates, two cylinders, and cylinder with rectangular prism and comparing the energy time histories with finite element solutions obtained using ANSYS®. The potential of the proposed HPF theory is demonstrated through simulation of impact of two plates where the plates rebound when the impact velocity is below a critical value, and above that velocity, the plates join with each other.

Human–machine collaborative optimization method for dynamic worker allocation in aircraft final assembly lines

The allocation of workers with diverse skills on assembly stations significantly impacts the efficiency of aircraft final assembly (AFA). In a real-life assembly process, workers are allowed to move between multiple stations, to avoid bottleneck stations. The arrangement of worker movement is a practical issue coupled with the worker allocation, but is still ignored. This study formulates a dynamic worker allocation problem in AFA (dWA-AFA), focusing on the multiobjective joint optimization of worker allocation and worker movement. The trade-off between objectives depends on the human preferences that reflect assembly requirements. To address this, a human–machine collaborative optimization method (HMC-O) is proposed, involving a bidirectional collaboration: machine-to-human preference adaption, and human-to-machine experience support. Specifically, we design an adaptive dominance operator for integrating human preferences, and combine it with a two-stage nondominated sorting approach to generate initial optimization solutions. An experience-driven neighborhood search is further developed, which uses human experience to improve solutions. The proposed method is evaluated through two scales of real cases. It is observed the dWA-AFA significantly reduces the takt time, particularly by 14.21 % and 9.25 % in two worker-shortage scenarios respectively. Meanwhile, the HMC-O is competitive in terms of convergence and preference satisfaction for solving dWA-AFA.

VNSMAS: A constraint-based portfolio profit maximization

Stock trading has a more significant influence on the global economy. Stock trading with portfolio optimization became challenging due to the complexity of analyzing the high variance in time series stock data. Efficient portfolio management increases profit and avoids risky situations when investing. The present work aims to model a Variable Neighborhood Search Multi-Agent System for Portfolio Optimization (VNSMASPPO) to optimize the profit on defined trading constraints on buying, selling, and holding trading decisions. This work proposes a novel Variable Neighborhood Search-based Multi-Agent System (VNASMAS) algorithm for profit computation with a constraint-based multi-agent system. The stock price history experimental data sets are collected from 8th August 2016 to 31st March 2023 with 14,567 records. The proposed model achieved an RMSE of 10.11, MAE of 2.75, and MAPE of 0.017, outperforming the literature models. VNSMASPPO maximizes the portfolio profit and is a reliable, adaptable approach.

Depth extraction of 3D defects on curved surfaces with multi-line lasers

Abstract The adoption of computer vision technology has significantly impacted surface defect inspection by providing a non-contact, cost-effective solution that has been widely accepted. Among the various techniques available, 3D defect inspection using multi-line lasers is notable for its simplicity, high detection speed, and extensive coverage. The accuracy of this method is significantly constrained by the precision of laser stripe extraction. In industrial environments, achieving accurate extraction is hindered by the intricate surface geometries of objects and the challenge of maintaining uniform brightness in multi-line laser stripes. To address these challenges, we propose a novel approach to extract the depth of 3D defects on surfaces using multi-line lasers. Our method combines guided filtering and the Frankle-McCann Retinex (FMR) algorithms to improve the quality of captured images. We have refined the laser stripe extraction process and proposed an advanced adaptive threshold segmentation technique that utilizes the OTSU method to determine threshold coefficients, followed by secondary segmentation based on a neighborhood search. The extracted laser strips are then processed using the quadratic weighted gray gravity method. Additionally, we proposed an innovative region-growth segmentation method based on neighborhood search that effectively segments individual laser strips. We also design a strategy for determining 3D defect depths in situations where precise camera calibration is challenging. The efficacy of our proposed method was rigorously tested on a hot-rolled seamless steel tube with a diameter of 145 mm. The resulting 3D defect depth exhibited an error of less than 0.5 mm, meeting the stringent standards required for practical applications.

A study on multiple types of yard cranes rescheduling in ports considering carbon emission

ABSTRACT This paper investigates the rescheduling problem of multiple oil-driven and electric-driven yard cranes across multiple container yards in semi-automated container terminals under random faults. Efficient fault diagnosis and handling mechanisms are provided to mitigate the effects of faults on the performance of yard operations. At the same time, a rescheduling model is proposed, aiming to minimize weighted carbon emissions and completion time. By analyzing the features of the problem, a tailored adaptive large neighborhood search (ALNS) algorithm is designed to solve the model. The effectiveness of the ALNS is verified through problem instances generated from the operational data of the port of Dalian in China. The factors considered in this paper are relevant to actual port operations, thus offering reliable solutions for scheduling and greening of ports.

Integrating order picking and vehicle routing decisions in a dynamic e-commerce setting

Responding to e-commerce orders as quickly as possible is indispensable for companies competing in the current retailing landscape. After a customer orders online, the requested items should be picked in the warehouse and delivered to the customer’s location. Previous research showed the benefits of integrating the order picking and vehicle routing decisions in a static context. To achieve shorter customer response times, we propose multiple new algorithms in this study, able to handle the integrated problem of picking and delivery in a setting with dynamic order arrivals and considering more complex picking policies. We develop and implement four online large neighbourhood search algorithms, differing in their degree of integration between picking and routing decisions. The algorithms are tested on different operational settings. Differences between the algorithms and the impact of the operating context are analysed by use of an ANOVA. The results highlight the importance of integrated decision-making, as well as the large impact of the operating context on the operational efficiency. Furthermore, we demonstrate the environmental impact of stringent customer requests. These insights can be used by companies to optimise their operations.

Multi-GPU 3D k-nearest neighbors computation with application to ICP, point cloud smoothing and normals computation

The k-Nearest Neighbors algorithm is a fundamental algorithm that finds applications in many fields like Machine Learning, Computer Graphics, Computer Vision, and others. The algorithm determines the closest points (d-dimensional) of a reference set R according to a query set of points Q under a specific metric (Euclidean, Mahalanobis, Manhattan, etc.). This work focuses on the utilization of multiple Graphical Processing Units for the acceleration of the k-Nearest Neighbors algorithm with large or very large sets of 3D points. With the proposed approach the space of the reference set is divided into a 3D grid which is used to facilitate the search for the nearest neighbors. The search in the grid is performed in a multiresolution manner starting from a high-resolution grid and ending up in a coarse one, thus accounting for point clouds that may have non-uniform sampling and/or outliers. Three important algorithms in reverse engineering are revisited and new multi-GPU versions are proposed based on the introduced KNN algorithm. More specifically, the new multi-GPU approach is applied to the Iterative Closest Point algorithm, to the point cloud smoothing, and to the point cloud normal vectors computation and orientation problem. A series of tests and experiments have been conducted and discussed in the paper showing the merits of the proposed multi-GPU approach.