Neighborhood Search Research Articles

Adaptive large neighborhood search algorithm with reinforcement search strategy for solving extended cooperative multi task assignment problem of UAVs

In response to the increasing complexity of military operations, the use of multiple heterogeneous unmanned aerial vehicles (UAVs) is essential for efficiently executing complex missions. This paper introduces an extended cooperative multi-task assignment problem (ECMTAP), which involves deploying heterogeneous UAVs from different base stations to accomplish specific missions, with a focus on minimizing overall mission completion time. ECMTAP categorizes targets into various types, each associated with unique task sets including {reconnaissance}, {attack, evaluation}, and {reconnaissance, attack, evaluation}. ECMTAP requires that attack tasks follow reconnaissance tasks, and evaluation tasks follow attack tasks, adding complexity due to specific timing constraints on each task. To tackle this problem, we propose a novel algorithm, the reinforcement search strategy-based adaptive large neighborhood search (RSALNS). To enhance the search capability, RASLNS utilizes two key destroy-repair operations: the intra-target tasks adjustment strategy and the evaluation tasks adjustment strategy. The former operation dismantles and reconstructs task sequences within a target, potentially resulting in suboptimal assignment of evaluation tasks, while the latter operation reassigns these tasks based on the first operation's output. Extensive experiments validate the effectiveness of the RSALNS algorithm in solving the ECMTAP, demonstrating its capability to generate high-quality solutions efficiently.

Variable Neighborhood Search for Minimizing the Makespan in a Uniform Parallel Machine Scheduling

Variable Neighborhood Search for Minimizing the Makespan in a Uniform Parallel Machine Scheduling

Multiobjective flexible job-shop scheduling optimization for manufacturing servitization

PurposeWith the development trend of China’s service-oriented manufacturing moving toward intelligence and personalization, the deep integration of manufacturing and service has become a synergistic challenge for enterprises.Design/methodology/approachAn improved migratory bird optimization (IMBO) algorithm is proposed to solve the multiobjective FJSP model. First, this paper designs an integer encoding method based on job-machine. The algorithm adopts the greedy decoding method to obtain the optimal scheduling solution. Second, this paper combines three initialization rules to enhance the quality of the initial population. Third, three neighborhood search strategies are combined to improve the search capability and convergence of the solution space. Furthermore, the IMBO algorithm introduces the concepts of nondominated ranking and crowding degree to update the population better. Finally, the optimal solution is obtained after multiple iterations.FindingsThrough the simulation of 15 benchmark studies and a production example of a furniture enterprise, the IMBO algorithm is compared with three other algorithms: the improved particle swarm optimization algorithm, the global and local search with reinitialization-based genetic algorithm and the hybrid grey wolf optimization algorithm. The experiment results show the effectiveness of the IMBO algorithm in solving the multiobjective FJSP.Practical implicationsThe study does not consider the influence of disturbance factors, such as emergency interventions and equipment failures, on scheduling in actual production processing. It is necessary to further study the dynamic FJSP problem.Originality/valueThe study proposes an IMBO algorithm to solve the multiobjective FJSP problem. It also uses three initialization rules to broaden the range of the solution space. The study applies multiple crossover strategies to avoid the algorithm falling into local optimality.

The low-carbon vehicle routing problem with dynamic speed on steep roads

The low-carbon vehicle routing problem with dynamic speeds on steep roads (LCVRPDS-SR) considers the combined effects of dynamic speeds, steep roads, and loads on carbon emissions. Earlier low-carbon vehicle routing problems typically assumed that vehicles travel at a constant speed on flat roads. However, such models do not apply in urban or rural areas with steep roads. Although the subsequent studies further explored the effect of steep roads, their performance are still suboptimal since they fail to take into account the varying speeds on the terrain. This paper proposes an extended LCVRPDS-SR model that tackles dynamic speed decisions on steep roads for the low-carbon vehicle routing problem. The objective function is non-linear and considers only environmental factors. Then an improved adaptive large neighborhood search algorithm is presented, including a new speed optimization algorithm and several improved removal and insertion operators. Extensive experiments are conducted on the generated instances to verify the effectiveness of the model and algorithm and derive managerial insights. The significant reduction in greenhouse gas emissions is achieved when considering dynamic speeds.

Spatial downscaling of rainfall from general circulation models: A chaos theory-based framework

The present study proposes a new chaos theory-based local approximation technique for the spatial downscaling of rainfall from GCM outputs. The approach involves: (1) reconstruction of relevant climate data in the state space using an embedding technique; and (2) use of a neighbor search procedure in the reconstructed space and local approximation for downscaling. The approach is implemented for downscaling of monthly rainfall at a spatial resolution of 0.25° × 0.25° across India. The effectiveness of the approach is first demonstrated using the NCEP/NCAR reanalysis dataset (1979–2014) and then tested using the outputs from five CMIP6 GCMs (CMCC-ESM2, CESM2-WACCM, CMCC-CM2-HR4, E3SM-1-1-ECA, and BCC-ESM1) (1961–2014). Eight climate variables (eastward wind, northward wind, relative humidity, specific humidity, surface temperature, air temperature, sea level pressure, and geopotential height) are considered as predictors, with the observed rainfall data from the India Meteorological Department (IMD) as the predictand. Three statistical measures, namely correlation coefficient (CC), Nash-Sutcliffe efficiency (NSE), and normalized root mean square error (NRMSE), are used for evaluation. The results indicate that the local approximation downscaling approach yields promising results for both reanalysis dataset and GCM outputs. Among the five GCMs, the CMCC-ESM2 model yields the best results. Comparison of the results from the chaos theory-based approach with those of the linear regression-based approach indicates better performance of the former. The proposed framework is general and can be applied for any climate variable, GCM, and region.

Progressive failure process-considered deformation safety diagnosis method for in-service high arch dam

Conventional methods cannot effectively characterize the progressive failure process, which limits their use in the deformation safety diagnosis of in-service high arch dam. This work proposes a novel method utilizing the frontier theories of mathematics, mechanics, and dam safety monitoring. First, considering the implicit assumption of traditional method, hybrid model (HM) is improved to calibrate the elastic deformation state of high arch dam. Second, the adaptive proportion selection and the neighborhood search strategy are applied to improve artificial bee colony (ABC) algorithm. The undetermined parameters of HM are optimized by the improved ABC. Third, the dangerous degrees of single-point deformation and multi-point deformation are characterized using HM analysis results and information entropy. On this basis, the progressive diagnosis criteria are constructed based on probability principle. Finally, two case studies are conducted to validate the proposed methodology. The analysis results demonstrate that the performance of HM is better than that of the statistical model (SM); the improved ABC promotes the HM performance; and the progressive diagnosis criteria compared with the traditional confidence interval criteria have stricter probabilistic and physical meanings. The 5-level safety control is realized, promoting the emergency response ability of high arch dam in operating condition.

A practical and robust approach for solving the multi‐compartment vehicle routing problem under demand uncertainty using machine learning

AbstractThe multi‐compartment vehicle routing problem is an extension of the vehicle routing problem and consists of designing a set of routes to perform the collection or delivery of different product types from customers with minimal costs. The product types are incompatible with each other and must be transported separately in multiple compartments. In practice, several uncertainties such as uncertainty in demands can arise, where the exact demand of customers is not known at the time of planning. To deal with these uncertainties, decision‐makers have to rely on robust solutions. A solution is considered robust when it can resist perturbations in every possible demand scenario as much as possible. In the day to day business of most logistic companies, historical data about each customer can be stored and used to make intelligent decisions regarding the expected demands. In this article, we propose an adaptive large neighborhood search for solving the robust multi‐compartment vehicle routing problem under demand uncertainty and present a robust solution approach for the problem in practical settings by employing machine learning. We show that by using our approach, the solutions obtained have lower recourse costs and have a lower gap between expected and actual costs, which is a favorable outcome to have in practice.

A discrete moth-flame optimization algorithm for multiple automated guided vehicles scheduling problem in a matrix manufacturing workshop

The growing need for customized and varied production has highlighted the significance of smart and automated factories in the manufacturing sector. In this particular context, the scheduling of multiple Automated Guided Vehicles (AGVs) plays a pivotal role in enhancing the efficiency of operations within an intelligent manufacturing shop. This study centers on the material handling process within a matrix manufacturing shop with the objective of identifying the most cost-effective transportation routes for materials. To accomplish the specified objective, this research aims to identify an optimal solution for reducing transportation costs. In particular, this study formulates a mixed-integer linear programming model and introduces a novel discrete variant of the moth-flame optimization (MFO) algorithm, named DMFO, to address the scheduling problem. The DMFO algorithm incorporates several significant enhancements. Firstly, a population initialization method is proposed, which combines a nearest-neighbor-based adaptive heuristic and a random sorting technique to ensure the formation of a well-structured population. Secondly, the flame generation mechanism and spiral flight search processes within the MFO have been redefined to achieve a more optimal balance between exploration and exploitation. A neighborhood search mechanism is subsequently devised, employing the concept of neighborhood relevance to accelerate the convergence process. Additionally, a heuristic approach is introduced to reduce the computational cost. Moreover, a population regeneration mechanism is proposed to avoid the algorithm falling into a local optimum. To validate the effectiveness of the DMFO, a comparative analysis is conducted using a dataset of 110 real-world factory instances. In this analysis, eight well-established optimization algorithms are employed. The simulation results consistently demonstrate that the relative percentage deviation (RPD) value of the DMFO tends to approach 0% more closely compared to other algorithms, thereby substantiating the effectiveness of the proposed algorithm.

Railway line planning with passenger routing: Direct-service network representations and a two-phase solution approach

The railway line planning problem (LPP) plays a crucial role in determining the quality of services provided to passengers, as well as operation costs borne by railway companies. In periodic railway LPPs, it is common to consider passenger transfers between train lines to realize a general passenger travel cost setting in the railway system. While detecting passenger transfers requires incorporating passenger routing into mathematical formulations, thereby significantly complicating the problem. Studies on transfer-included LPPs are generally based on the Change&Go network that is constructed based on a pre-given line pool, which however is usually non-exhaustive due to computational intractability. To efficiently include passenger transfers in large-scale railway LPPs, this paper proposes a novel extended direct-service network representation of LPP, where lines are dynamically generated within the optimization process, and part of passenger transfers between lines can be precisely captured without the need for explicit modeling of passengers’ distribution on specific lines. A two-phase solution approach based on the representation is designed. The first phase formulates LPP with part of transfers as a path-based service network design model, solved using a branch-price-and-cut algorithm. The second phase conducts a neighborhood search around the first-phase solution to seek better ones when considering all passenger transfers. Numerical results showcase the good performance of the two-phase solution approach. It delivers optimal solutions in 18 out of 24 test instances for a small case and achieves optimality gaps within 2.85% across all small instances. The large case study of China’s Shandong high-speed railway network whose line pool size reaches millions demonstrates the scalability of the approach and its advantage over the traditional Change&Go method with partial line pools and an exact model developed in the paper.

Sustainable location-routing problem for medical waste management using electric vehicles

The sustainable management of medical waste is essential for environmental protection and public health safety, contributing to urban development and societal well-being. This study focuses on the medical waste Location-Routing Problem with Electric Vehicles (EVs), aiming to mitigate infection risk while providing cost-effective and energy-efficient logistics solutions. It encompasses diverse elements such as disposal location selection, vehicle scheduling, recharge strategies, time windows, working duration, and load-dependent energy consumption. To tackle this complex problem, we introduce an Evolutionary Decomposition-based Adapted Large Neighborhood Search algorithm (E-ALNS/D), which surpasses existing methods such as MOEA/D-HGS, MOEA/D-LNS, MPGA, and NSGAII-LNS across various urban settings. Empirical analysis and a case study reveal opportunities for enhancing medical waste management with EVs. Key findings show that optimizing service times, adopting fast-charging infrastructure, and strategically placing temporary disposal facilities in response to unforeseen demand spikes significantly reduce energy consumption, lower infection risk, and cut charging cost. Furthermore, selecting peripheral over central disposal locations better minimizes infection risks. This research offers valuable insights for decision-makers in EV-based medical waste management, emphasizing the need to balance public health, environmental sustainability, and economic efficiency. Our methodology paves the way for future research in sustainable medical waste management with EVs.

AN EVALUATION OF ML TECHNIQUES ON NASDAQ DATASET FOR STOCK MARKET FORECASTING

The uncertainty of stock pricing has popularized stock market prediction as a common practice. Forecasting prices in the market are viewed as problematic, as the hypothesis of efficient markets (EMH) explains. According to the EMH, all accessible information is represented in market prices, and price variations are only the consequence of newly available information. The approach to prediction forecasts the market as either positive or negative based on a variety of input parameters. A combination of derived, fundamental, and pure technical data is utilized in stock forecasts to project future stock prices. Algorithms for machine learning (ML) are made to find patterns in data and utilize those patterns to forecast future events. K Nearest Neighbour (KNN) can process relationships between the numerical data, it is particularly effective in numerical prediction problems for predicting changes in stock value the following day. KNN categorizes freshly input data according to how similar it is to previously taught data, and it does this by clustering the data into coherence subsets or clusters. Using the KNN approach in conjunction with technical analysis, the closest neighbour search strategy yielded the desired outcome. KEYWORDS: Hypothesis, K Nearest Neighbour, Prediction, Stock prices,

A Two-Echelon Multi-Trip Capacitated Vehicle Routing Problem with Time Windows for Fresh E-Commerce Logistics under Front Warehouse Mode

Given the swift expansion of fresh e-commerce, the front warehouse mode can respond quickly and ensure the quality of fresh products. However, the complexity of the supply chain structure under front warehouse mode poses a challenge in reducing logistics costs and improving distribution efficiency while meeting consumers’ immediate delivery demands. Therefore, this paper studies the vehicle routing problem of two-echelon fresh e-commerce under front warehouse mode. Considering trans-shipment time constraints between the two echelons and the characteristics of terminal distribution, this paper initially models the vehicle routing problem for front warehouses as a two-echelon multi-trip capacitated vehicle routing problem with time windows. A mixed-integer linear programming model is subsequently established. To solve the model, a hybrid genetic algorithm integrated with neighborhood search is developed. Matrix coding is employed to merge vehicle selection and route assignment decisions. Simultaneously, neighborhood search is applied to enhance the search capability of algorithms, thereby improving the quality of solutions. Furthermore, the effectiveness and efficiency of the model and algorithm are verified through experiments of varying scales. Finally, comparative strategies and sensitivity analysis highlight the advantages of multi-trip strategies and provide insights into the optimal vehicle capacity limit.

Integrated scheduling of production and material delivery for the intelligent manufacturing system

This paper addresses the issue of integrated scheduling of production and material delivery in an intelligent manufacturing system. The system includes multiple flexible flowlines and a fleet of automated guided vehicles (AGVs), whose capacity is shared among the flowlines. Multiple orders from different customers are processed on the flowlines, with AGVs delivering materials in different batch sizes based on varying product quantities of orders and limited load capacity. The problem is formulated as a mixed-integer linear programming model to minimise total order delays and the number of used AGVs. To handle the complexity caused by the uneven temporal coupling relationship between production and material delivery, a bi-level neighbourhood search solution algorithm is proposed. This algorithm uses variable neighbourhood search for production scheduling and adaptive large neighbourhood search for material delivery scheduling. Extensive numerical experiments using real-case-based instances are conducted to demonstrate the satisfactory performance of our proposed algorithm. Furthermore, we emphasise the advantages of integrated scheduling compared to traditional sequential scheduling across various problem settings.

Physical internet enabled sustainable and resilient production-routing problem with modular capacity

PurposeModular capacity units enable rapid reconfiguration, providing tactical flexibility to efficiently meet customer demand during disruptions and ensuring sustainability. Moreover, the Physical Internet (PI) enhances the potential of modular capacity in addressing efficiency, sustainability, and resilience challenges. To evaluate the sustainability and resilience advantages of the PI-enabled reconfigurable modular system (PI-M system), this paper studies a PI-enabled sustainable and resilient production-routing problem with modular capacity.Design/methodology/approachWe develop a multi-objective optimization model to assess the sustainability and resilience benefits of combining PI and modular capacity in a chemical industry case study. A hybrid solution approach, combining the augmented e-constraint method, construction heuristic, and hybrid adaptive large neighborhood search, is developed.FindingsThe experimental results reveal that the proposed solution approach is capable of obtaining better solutions than the Gurobi and the existing heuristic in a shorter running time. Moreover, compared with the traditional system, the PI only and traditional with modular capacity systems, PI-M system has significant advantages in both sustainability and resilience.Originality/valueTo the best of our knowledge, this study is the first to integrate the PI and modular capacity and investigate sustainability and resilience in the production-routing problem.

An Energy-Efficient Logistic Drone Routing Method Considering Dynamic Drone Speed and Payload

Unmanned aerial vehicles (UAVs), or drones, are recognized for their potential to improve efficiency in last-mile delivery. Unlike the vehicle routing problem, drone route design is challenging due to several operational signatures, such as speed optimization, multi-trip operation, and energy consumption estimation. Drone energy consumption is a nonlinear function of both speed and payload. Moreover, the high speed of drones can significantly curtail the drone range, thereby limiting the efficiency of drone delivery systems. This paper addresses the trade-off between speed and flight range in a multi-trip drone routing problem with variable flight speeds (DRP–VFS). We propose a new model to specifically consider energy constraints using a nonlinear energy consumption model and treat drone speeds as decision variables. The DRP–VFS is initially formulated using mixed-integer linear programming (MILP) to minimize energy consumption. To solve large-scale instances, we propose a three-phase adaptive large neighborhood search (ALNS) algorithm and compare its performance with a commercial MIP solver. The experimental results demonstrate that the proposed method is capable of effectively identifying suboptimal solutions in practical scenarios. Furthermore, results indicate that operating drones at variable speeds leads to about 21% energy savings compared to fixed speeds, with advantages in cost savings and range extension.

Pixel-MPS: Stochastic Embedding and Density-Based Clustering of Image Patterns for Pixel-Based Multiple-Point Geostatistical Simulation

Multiple-point geostatistics (MPS) is an established tool for the uncertainty quantification of Earth systems modeling, particularly when dealing with the complexity and heterogeneity of geological data. This study presents a novel pixel-based MPS method for modeling spatial data using advanced machine-learning algorithms. Pixel-based multiple-point simulation implies the sequential modeling of individual points on the simulation grid, one at a time, by borrowing spatial information from the training image and honoring the conditioning data points. The developed methodology is based on the mapping of the training image patterns database using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm for dimensionality reduction, and the clustering of patterns by applying the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, as an efficient unsupervised classification technique. For the automation, optimization, and input parameter tuning, multiple stages are implemented, including entropy-based determination of the template size and a k-nearest neighbors search for clustering parameter selection, to ensure the proposed method does not require the user’s interference. The proposed model is validated using synthetic two- and three-dimensional datasets, both for conditional and unconditional simulations, and runtime information is provided. Finally, the method is applied to a case study gold mine for stochastic orebody modeling. To demonstrate the computational efficiency and accuracy of the proposed method, a two-dimensional training image with 101 by 101 pixels is simulated for 100 conditional realizations in 453 s (~4.5 s per realization) using only 361 hard data points (~3.5% of the simulation grid), and the resulting average simulation has a good visual match and only an 11.8% pixel-wise mismatch with the training image.

Nearest‐neighbor, BERT‐based, scalable clone detection: A practical approach for large‐scale industrial code bases

AbstractHidden code clones negatively impact software maintenance, but manually detecting them in large codebases is impractical. Additionally, automated approaches find detection of syntactically‐divergent clones very challenging. While recent deep neural networks (for example BERT‐based artificial neural networks) seem more effective in detecting such clones, their pairwise comparison of every code pair in the target system(s) is inefficient and scales poorly on large codebases. We present SSCD, a BERT‐based clone detection approach that targets high recall of Type 3 and Type 4 clones at a very large scale (in line with our industrial partner's requirements). It computes a representative embedding for each code fragment and finds similar fragments using a nearest neighbor search. Thus, SSCD avoids the pairwise‐comparison bottleneck of other neural network approaches, while also using a parallel, GPU‐accelerated search to tackle scalability. This article describes the approach, proposing and evaluating several refinements to improve Type 3/4 clone detection at scale. It provides a substantial empirical evaluation of the technique, including a speed/efficacy comparison of the approach against SourcererCC and Oreo, the only other neural‐network approach currently capable of scaling to hundreds of millions of LOC. It also includes a large in‐situ evaluation on our industrial collaborator's code base that assesses the original technique, the impact of the proposed refinements and illustrates the impact of incremental, active learning on its efficacy. We find that SSCD is significantly faster and more accurate than SourcererCC and Oreo. SAGA, a GPU‐accelerated traditional clone detection approach, is a little better than SSCD for T1/T2 clones, but substantially worse for T3/T4 clones. Thus, SSCD is both scalable to industrial code sizes, and comparatively more accurate than existing approaches for difficult T3/T4 clone searching. In‐situ evaluation on company datasets shows that SSCD outperforms the baseline approach (CCFinderX) for T3/T4 clones. Whitespace removal and active learning further improve SSCD effectiveness.

Bridging the Domain Gap in Scene Flow Estimation via Hierarchical Smoothness Refinement

This article introduces SmoothFlowNet3D, an innovative encoder-decoder architecture specifically designed for bridging the domain gap in scene flow estimation. To achieve this goal, SmoothFlowNet3D divides the scene flow estimation task into two stages: initial scene flow estimation and smoothness refinement. Specifically, SmoothFlowNet3D comprises a hierarchical encoder that extracts multi-scale point cloud features from two consecutive frames, along with a hierarchical decoder responsible for predicting the initial scene flow and further refining it to achieve smoother estimation. To generate the initial scene flow, a cross-frame nearest-neighbor search operation is performed between the features extracted from two consecutive frames, resulting in forward and backward flow embeddings. These embeddings are then combined to form the bidirectional flow embedding, serving as input for predicting the initial scene flow. Additionally, a flow smoothing module based on the self-attention mechanism is proposed to predict the smoothing error and facilitate the refinement of the initial scene flow for more accurate and smoother estimation results. Extensive experiments demonstrate that the proposed SmoothFlowNet3D approach achieves state-of-the-art performance on both synthetic datasets and real LiDAR point clouds, confirming its effectiveness in enhancing scene flow smoothness.

A learning-based granular variable neighborhood search for a multi-period election logistics problem with time-dependent profits

Planning the election campaign for leaders of a political party is a complex problem. The party representatives, running mates, and campaign managers have to design an efficient routing and scheduling plan to visit multiple locations while respecting time and budget constraints. Given the limited time of election campaigns in most countries, every minute should be used effectively, and there is very little room for error. In this paper, we formalize this problem as the multiple Roaming Salesman Problem (mRSP), a new variant of the recently introduced Roaming Salesman Problem (RSP), where a predefined number of political representatives visit a set of cities during a planning horizon to maximize collected rewards, subject to budget and time constraints. Cities can be visited more than once and associated rewards are time-dependent (increasing over time) according to the day of the visit and the recency of previous visits. We develop a compact Mixed Integer Linear Programming (MILP) formulation complemented with effective valid inequalities. Since commercial solvers can obtain optimal solutions only for small-sized instances, we develop a Learning-based Granular Variable Neighborhood Search and demonstrate its capability of providing high-quality solutions in short CPU times on real-world instances. The adaptive nature of our algorithm refers to its ability to dynamically adjust the neighborhood structure based on the progress of the search. Our algorithm generates the best-known results for many instances.

An improved ALNS for hybrid pickup and drones delivery system in disaster by penalizing deprivation time

Humanitarian logistics aims to reduce the time relief goods and services take to reach affected areas; deprivation time is one of the most significant factors in such conditions. Thus, utilizing a suitable transport fleet, including modern vehicles like drones, is essential. This paper introduces a new hybrid vehicle routing problem with pickup and delivery services, minimizing deprivation costs with multiple trucks and drones in a disaster. The model is presented as a mixed-integer linear programming problem focusing on minimizing deprivation cost in post-disaster situations through proper routing and inventory decisions made by heterogeneous vehicles. The deprivation time is divided into three main categories based on the arrival time of relief goods to affected areas and will be served by multiple relief goods. Due to the complexity of the problem, an improved adaptive large neighborhood search (ALNS) metaheuristic has been developed. This algorithm employs a heuristic algorithm to generate high-quality initial solutions and improve solutions with 17 destroy and repair operators. The algorithm was tested on a large-scale dataset based on real-world data from a crisis such as the Tehran earthquake, and the results demonstrate its superiority in providing better-quality solutions and lower solution time.