Neighborhood Search Research Articles

A proposal towards a VNS-based decision support tool for large scale location-covering-type problems

We describe a number of typical features, constraints and objectives, frequently appearing in spatial system-design of maximum covering-type, such as emergency response systems. We then indicate some heuristic local search methods topped by a Variable Neighbourhood Search to construct and search for good solutions. These ideas were intended to form the basis for a possibly multi-objective spatial decision support system.

Human–Robot Collaborative Scheduling in Energy-Efficient Welding Shop

Human-robot collaborative scheduling has been widely applied in modern manufacturing industry. A rational scheduling of human-robot cooperation plays an important role in improving production efficiency. However, human-robot collaborative scheduling problem in welding production has not been studied so far. Thus, this paper addresses a human-robot collaborative welding shop scheduling problem (HCWSSP) with minimization objectives of makespan and total energy consumption (TEC). To solve this multi-objective HCWSSP, a Pareto-based memetic algorithm (PMA), which hybridizes a genetic operator and variable neighborhood search (VNS), is presented to obtain a set of trade-off solutions between makespan and TEC. In PMA, each solution is represented by two parts, i.e., job processing sequence and resource assignment. A novel integrated initialization strategy is proposed to generate one initial population with high quality and good diversity. Furthermore, five kinds of VNS are designed to improve exploitation capability of PMA. Experimental results on test problems manifest that the proposed PMA performs better than its competitors.

A VNS-based approach for solving the Manhattan metric straddle carrier routing problem with buffer areas

This paper presents a metaheuristic approach for solving an optimization problem that arises at container terminals where straddle carriers (SCs) transport containers between the stacking areas and the seaside. In such container terminals, operational efficiency depends mainly on SC routing. SCs routes should consider the order in which containers are unloaded and loaded at the quay cranes (QCs), taking into account the limited capacity of the buffer area of each QC where containers are temporarily stored after being handled by a QC or an SC. Besides the precedence relations (i.e., container sequences) and buffer capacities, the solution framework considers safety constraints. Efficient routing of SCs directly contributes to minimizing the idle time of QCs, thereby improving their overall productivity and minimizing the turnaround time of vessels, which is the objective of the problem. Specifically, we present two different variants of the Variable Neighborhood Search (VNS) algorithm. Each variant is initialized in both a greedy and a random manner. These algorithms address the problem by incorporating four LS operators commonly utilized in vehicle routing problems. We perform a comparative analysis of the results of these four approaches against each other and against solutions generated by an exact solver. Our numerical experiments show that the proposed algorithms perform better than the used solver, especially for bigger instances. A comparison with the results from the literature is also given and shows that the proposed VNS-based approach provides competitive results.

A multi-objective fuzzy flexible job shop scheduling problem considering the maximization of processing quality

This paper analyzes practical production characteristics, including customer's stringent quality requirements and uncertain processing time in aircraft shaft parts manufacturing. Considering the above characteristics, we propose a multi-objective fuzzy aircraft shaft parts production scheduling problem considering the maximization of production quality. We define this problem as a multi-objective fuzzy flexible job shop scheduling problem (MO-fFJSP) with fuzzy processing time. To address this problem, we developed an improved multi-objective spider monkey optimization (IMOSMO) algorithm. IMOSMO integrates strategies such as genetic operators, variable neighborhood search and Pareto optimization theory on the framework of the conventional Spider Monkey Optimization (SMO) framework and discretize the continuous SMO algorithm to solve MO-fFJSP. To enhance the efficiency of the algorithm, we further adjust the sequence of the local leader learning phase and the global leader learning phase within the proposed IMOSMO framework. We conduct a comparative analysis between the performance of IMOSMO and NSGA-Ⅱ using 28 cases of varying scales. The computational results demonstrate the superiority of our algorithm over NSGA-Ⅱ in terms of both solution diversity and quality. Moreover, the performance of the proposed algorithm upgrades as the problem scale increases.

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

Testing initial solutions in a variable neighbourhood search for binary integer programming problems

Testing initial solutions in a variable neighbourhood search for binary integer programming problems

Модели и алгоритмы локального поиска для маршрутизации транспортных средств с возвратами и временными окнами

A vehicle routing problem for servicing objects with the possibility of distributing work between vehicle taking into account time windows is considered. We discuss approaches to constructing integer linear programming models and their modifications. A large neighborhood search algorithm is proposed to find approximate solutions. Here an improving solution is constructed using the “destroy” and “repair” methods at each step. A series of instances with various structures are proposed, in particular demonstrating the reason of returns to objects for vehicles. The results of an experimental evaluation of models and algorithms are presented.

An Adaptive Large Neighborhood Search for Multi-depot Electric Vehicle Routing Problem with Time Windows

An Adaptive Large Neighborhood Search for Multi-depot Electric Vehicle Routing Problem with Time Windows

Optimization of Static Patient Admission Scheduling using the Variable Neighborhood Search Method

Planning and resource management are important aspects of a company's operational sustainability. With good management, companies can achieve their targets while minimizing operational costs. The same goes for hospitals. Various challenges related to resources are experienced by hospitals, such as scheduling nurses, patient surgeries, and patient appointments. Therefore, this paper aim at optimizing patient admission scheduling in order to improve hospital resource efficiency. To be more specific, patient admission scheduling, also known as the Patient Admission Scheduling Problem (PASP), is a scheduling problem that considers patient preferences and needs, bed availability, resource efficiency, and utilization. This problem is highly relevant, especially for large hospitals. The number of rooms, specialists, and facilities makes manual scheduling extremely difficult. It is due to the varied preferences, needs, and lengths of stay for patients in hospital. Various methods, both heuristic and exact, have been proposed, including the use of integer programming methods. However, for a large search space, this method requires a very long computational time. To address the PASP, this study applies the Variable Neighborhood Search (VNS) algorithm and random selection as optimization algorithms. The method was chosen because it has been proven effective to solve some combinatorial optimization problems in prior studies. Seven types of neighborhoods are implemented to find the best combinations in optimizing the PASP. The results show that the VNS algorithm outperforms the random selection algorithm, as it is able to generate 5 out of 7 solutions that are better, reducing penalties by 27.84% to 55.29%. The expected impact of this study is to increase the hospital patient satisfaction whereas in the same time minimize the operational cost.

An Improved Bilevel Algorithm Based on Ant Colony Optimization and Adaptive Large Neighborhood Search for Routing and Charging Scheduling of Electric Vehicles

An Improved Bilevel Algorithm Based on Ant Colony Optimization and Adaptive Large Neighborhood Search for Routing and Charging Scheduling of Electric Vehicles

A flexible train composition strategy with extra-long trains for high-speed railway corridors with time-varying demand

To accommodate the uneven spatio-temporal distribution of passenger demand and improve the maximum transportation capacity for high-speed railway (HSR) corridors, this study proposes a demand-oriented flexible train composition strategy. The proposed strategy allows flexibility in selecting the number of train composition units (e.g., train carriages) for each train to accommodate the demand variations, where extra-long trains (longer than the length of the train station platform) might be utilized. Under such a strategy, a seat allocation method coupled with a train stopping position control strategy is proposed to avoid the need for cross-carriage walking during passenger boarding or alighting (for those extra-long trains). The train stopping position control strategy specifies the carriages that dock at or extend beyond the station platform, while the seat allocation method allocates seats within each carriage to specific origin-destination pairs. The studied problem can be formulated as an integer nonlinear programming model, where the weighted sum of the HSR operating cost and passenger travel time cost is minimized. The proposed model is then reformulated into an integer linear programming model via a series of linearization techniques. A tailored heuristic algorithm based on the variable neighborhood search (VNS) and GUROBI solver is designed to produce high-quality solutions for large-scale problems. Two numerical examples, i.e., a small example and a real-world Shanghai-Hangzhou HSR line example, are presented to illustrate the effectiveness of the proposed approach. The computation results show that, in comparison to traditional fixed and flexible train composition strategies without extra-long trains, the proposed strategy can significantly reduce the total operating cost and total travel time cost.

An adaptive large neighbourhood search for multi-depot electric vehicle routing problem with time windows

An adaptive large neighbourhood search for multi-depot electric vehicle routing problem with time windows

Learning Adaptive Hypersphere: Boosting Efficiency on Approximate Nearest Neighbor Search

Learning Adaptive Hypersphere: Boosting Efficiency on Approximate Nearest Neighbor Search

Real-Time Implementation of Predictive Control in Power Inverters Based on Nearest Neighbor Searching

Real-Time Implementation of Predictive Control in Power Inverters Based on Nearest Neighbor Searching

Hybrid heuristic for the one-dimensional cutting stock problem with usable leftovers and additional operating constraints

The One-Dimensional Cutting Stock Problem consists in cutting long bars into smaller ones, to satisfy customers’ demand, minimizing waste and cost. In this paper the standard problem is extended with the inclusion of additional constraints that are generally neglected in scientific literature, although relevant in many industrial applications. We also modified the standard objective function, by assuming that bars may have a different economical value and a different processing or shipping priority. Moreover, in line with business requirements, among solutions that generate the same cutting waste, we prefer the ones that generate a low number of leftovers, especially if leftovers are long, so that the likelihood of their reuse is high. To solve the problem, we propose a Simulated Annealing based heuristic, which exploits a specific neighbor search. The heuristic is implemented in a parametric way that allows the user to set the priorities of the bars and to choose the specific sub-set of constraints he or she wants to consider. The heuristic is finally tested on many problem instances, and it is compared to three benchmarks and to one commercial software. The outcomes of this comparative analysis demonstrate both its quality and effectiveness.

Chunk2vec: A novel resemblance detection scheme based on Sentence‐BERT for post‐deduplication delta compression in network transmission

AbstractDelta compression, as a complementary technique for data deduplication, has gained widespread attention in network storage systems. It can eliminate redundant data between non‐duplicate but similar chunks that cannot be identified by data deduplication. The network transmission overhead between servers and clients can be greatly reduced by using data deduplication and delta compression techniques. Resemblance detection is a technique that identifies similar chunks for post‐deduplication delta compression in network storage systems. The existing resemblance detection approaches fail to detect similar chunks with arbitrary similarity by setting a similarity threshold, which can be suboptimal. In this paper, the authors propose Chunk2vec, a resemblance detection scheme for delta compression that utilizes deep learning techniques and Approximate Nearest Neighbour Search technique to detect similar chunks with any given similarity range. Chunk2vec uses a deep neural network, Sentence‐BERT, to extract an approximate feature vector for each chunk while preserving its similarity with other chunks. The experimental results on five real‐world datasets indicate that Chunk2vec improves the accuracy of resemblance detection for delta compression and achieves higher compression ratio than the state‐of‐the‐art resemblance detection technique.

Scalable neighbour search and alignment with uvaia.

Despite millions of SARS-CoV-2 genomes being sequenced and shared globally, manipulating such data sets is still challenging, especially selecting sequences for focused phylogenetic analysis. We present a novel method, uvaia, which is based on partial and exact sequence similarity for quickly extracting database sequences similar to query sequences of interest. Many SARS-CoV-2 phylogenetic analyses rely on very low numbers of ambiguous sites as a measure of quality since ambiguous sites do not contribute to single nucleotide polymorphism (SNP) differences. Uvaia overcomes this limitation by using measures of sequence similarity which consider partially ambiguous sites, allowing for more ambiguous sequences to be included in the analysis if needed. Such fine-grained definition of similarity allows not only for better phylogenetic analyses, but could also lead to improved classification and biogeographical inferences. Uvaia works natively with compressed files, can use multiple cores and efficiently utilises memory, being able to analyse large data sets on a standard desktop.

Deep Learning to Hash with Application to Cross-View Nearest Neighbor Search

Learning hash functions for approximate nearest neighbor search of high-dimensional data has received a surge of interests in recent years. Most existing methods are often concerned with learning hash functions for nearest neighbor search on high-dimensional data from a single source. In many real-world applications, data can be collected from diverse sources or represented using different feature descriptors. This raises an open challenge, i.e., the Cross-View Nearest Neighbor Search (CVNNS), where the representation of a query instance can be different from that of target instances to be retrieved in database. The key challenge of cross-view search is to learn an effective shared representation which can effectively connect the query instance and the target instances to be retrieved. In this paper, we present a new cross-view nearest neighbor search scheme by applying the emerging deep learning to hash techniques. In particular, we investigate two different architectures of deep Restricted Boltzmann Machines (RBMs) for learning to hash toward cross-view nearest neighbor search, and conduct extensive experiments to examine their empirical performance on diverse settings of cross-view image retrieval tasks. The encouraging results show that our technique outperforms the state-of-the-art approaches.

Locality sensitive hashing scheme based on online-learning

Locally Sensitive Hashing (LSH) algorithms are classical algorithms commonly used on the c-Approximate Nearest Neighbor (c-ANN) search problem. When using Euclidean distance to measure sample similarity and solve the c-ANN problem, the traditional approach is to utilize the Exact Euclidean Locality Sensitive Hashing (E2LSH) algorithm based on the p-stable distribution. However, the uncertainty of the p-stable distribution causes the hash buckets constructed by the E2LSH algorithm to vary in queries. Therefore, this paper proposes the OLLSH algorithm based on the Weighted Majority algorithm in the Online-Learning framework, which selects the hash buckets with more stable query accuracy by weighted voting on the hash buckets generated by the E2LSH algorithm. Then, we conduct simulation experiments on synthetic dataset and four real data sets and conclude that the proposed OLLSH algorithm improves the accuracy compared to the original algorithm with the same memory usage.

Three-way evidence theory-based density peak clustering with the principle of justifiable granularity

Clustering by fast search and find of density peaks (DPC) is an effective clustering approach that can find all the cluster centers at once with just one parameter and without iterative processing. However, the cutoff distance, a key parameter of density measurement in the DPC approach, affects the quality of the final clustering results. Its selection relies on experimental experience and lacks of a semantic explanation. Furthermore, the allocation strategy of the traditional DPC approach may cause several points to be assigned incorrectly, leading to subsequent points being assigned incorrectly and ultimately forming continuous allocation errors. To overcome the deficiencies, this paper proposes a novel three-way evidence theory-based density peak clustering with the principle of justifiable granularity (3 W-PEDP). First, the computation of the cutoff distance is converted into the search for nearest neighbors. From the perspective of granular computing, 3 W-PEDP transforms the neighbor selection issue into the construction of justifiable granularity. And the optimal neighbors can be achieved with the construction of coverage and specificity criteria. Second, inspired by three-way clustering, we adopt a two-stage method for sample allocation. On the one hand, for core point allocation, a two-layer nearest neighbor is constructed based on the achieved optimal neighbors. On the other hand, we designed a new evidence mass function to guide us in assigning the remaining points. In this novel evidence mass function, not only the labels of the assigned samples are considered, but also the information of the neighborhoods around the unassigned samples is fused. Finally, we assess the effectiveness of 3 W-PEDP on numerous public synthetic datasets and UCI real-world datasets. Then, detail comparing results with several popular clustering methods are presented. In addition, experimental studies verify the effectiveness of constructing justifiable granularity in selecting the optimal neighbors. The experimental results demonstrate 3 W-PEDP has good adaptability and robustness, which can achieve better clustering performance. Our source code is available at https://github.com/Luyangabc/3W-PEDP.