Neighborhood Search Research Articles

Multi-Objective Sustainable Supply Chain Network Planning Based on Proximity Optimization With Hybrid Genetic Algorithm Variable Neighborhood Search Strategy

Multi-Objective Sustainable Supply Chain Network Planning Based on Proximity Optimization With Hybrid Genetic Algorithm Variable Neighborhood Search Strategy

Skewed general variable neighborhood search to solve the multi-compartment vehicle routing problem

Skewed General Variable Neighborhood Search (SGVNS) is shown to be a powerful and robust methodology for solving vehicle routing problems. In this paper we suggest new SGVNS for solving the multi-compartment vehicle routing problem (MCVRP). The problem of multi-compartment vehicle routing is of practical importance in the petrol and food delivery and waste collection industries. A comparison between our algorithm and the memetic algorithm and the tabu search is provided. It was clear that the proposed algorithm is capable of solving the available instances. Skewed General Variable Neighborhood Search was used because it makes it easy to explore the space of realizable solutions for MCVRP. As a result, the SGVNS is much faster and more effective. It is able to solve 50 to 484 customers from the literature.

A hybrid genetic algorithm with variable neighborhood search for batch dispersion problem to improve traceability

Batch dispersion problem (BDP) restricts batch traceability in large-scale discrete production and negatively impacts batch recall costs. However, previous research has ignored the complexity of the BDP in their analyses. This paper investigates the BDP under the composed bill of materials (BOM) and develops a mathematical model for the BDP with the goal of minimizing the total batch dispersion by utilizing the batch dispersion as a measure of the degree of dispersed usage of part batches. BDP-GAVNS, a hybrid genetic algorithm with variable neighborhood search, is devised for the BDP based on the demonstration that the BDP is an NPC problem. In BDP-GAVNS, memory banks were introduced to increase the diversity of individuals performing crossover operations. Additionally, the encoding method and infeasible solution repair program are designed according to the characteristics of BDP. Numerical experiments validate the viability and effectiveness of BDP-GAVNS in solving BDP. They demonstrate that (1) the optimal combination occurs when the ratio of individuals produced by the three types of population initialization methods, namely global selection (GS), local selection (LS), and random selection (RS), to the population takes values of 0.30, 0.10, and 0.60, respectively; (2) The memory bank enriches the source of individuals required for crossover operations and improves the performance of crossover operations; and (3) The BDP-GAVNS is more effective than the other five heuristic algorithms including genetic algorithms in seeking the optimal solution of BDP.

An adaptive large neighbourhood search algorithm for blocking flowshop scheduling problem with sequence-dependent setup times

An adaptive large neighbourhood search algorithm for blocking flowshop scheduling problem with sequence-dependent setup times

A novel hybrid algorithm of genetic algorithm, variable neighborhood search and constraint programming for distributed flexible job shop scheduling problem

A novel hybrid algorithm of genetic algorithm, variable neighborhood search and constraint programming for distributed flexible job shop scheduling problem

An adaptive large neighborhood search for the order picking process: the case of a retail distribution company in Italy

Order picking is one of the most important activities in warehouse management. By optimising the picking process, warehouse operations can be managed efficiently in terms of both time and logistics costs. In this work we apply operations research techniques to support the order picking process of an Italian retail distribution company. The picking process poses several challenges from an optimisation point of view, and the related optimization problems are very complex. Therefore, we define an Adaptive Large Neighbourhood Search (ALNS) algorithm that heuristically solves the problem. The proposed metaheuristic is tested on a set of 101 real orders processed by the company within one day. The computational experiments show that the ALNS shows good performances in terms of effectiveness, compared to the optimal solution, as well as allows to implement a better organisation of the order picking process than the one currently adopted by the company.

Hybrid Grey-Wolf Algorithm approach for Solving energy efficient permutation flowshop scheduling problem with Setup Times

Energy efficient permutation flowshop scheduling problem (PFSP) is widely studied to reduce the energy consumption in manufacturing systems. Most traditional studies make the premise that setup time is a component of processing time. However, this assumption does not satisfy the small batches, more varieties manufacturing models criterion. Therefore, energy efficient PFSP with setup times thus becomes a pressing problem that needs to be resolved. The contribution of this paper includes: (1) a permutation flowshop scheduling problem (PFSP) mathematical model by considering energy consumed and setup time by each machine in the system; (2) a grey-wolf algorithm optimization with variable neighborhood search (VNS) method (GWO_VNS) is proposed for the multi-objective PFSP model; (3) solved 9 benchmarks problems of Taillard (1993) [8] for the minimization of flowtime (FT) and energy consumption (EC). The performance of the proposed GWO_VNS algorithm is evaluated on the benchmark problems selected from the published literature [15]. It is noted that the proposed algorithm performed better on both the objectives i.e., FT and EC minimization. Overall, the proposed algorithm achieved 33.25% and 30.56% average improvement in FT and EC minimization respectively on benchmark problems.

Exact solutions of some OR-library test instances for the p-next center problem

OR-Library is a platform that provides standardized examples for testing problem-solving algorithms in many fields of the operational research and combinatorial optimization. One of the problems emerged in the previous decade is the p-next center problem. The solution to the p-next center problem implies locating p centers in order to minimize the maximum user distance to the closest center plus the distance between that center and the center closest to it. There are several heuristic algorithms for solving this NP-hard problem that give optimal or near-optimal solutions. We propose an algorithm for solving the p-next center problem based on the variable neighborhood search method, capable of recognizing whether some of the found solutions from the OR-Library test set are exact. As a result of the algorithm execution, more than 50% of the solutions are identified as globally optimal. The paper presents a table with the found exact solution values for the p-next center problem from the OR-Library test set.

Efficient Neural Collaborative Search for Pickup and Delivery Problems.

In this paper, we introduce Neural Collaborative Search (NCS), a novel learning-based framework for efficiently solving pickup and delivery problems (PDPs). NCS pioneers the collaboration between the latest prevalent neural construction and neural improvement models, establishing a collaborative framework where an improvement model iteratively refines solutions initiated by a construction model. Our NCS collaboratively trains the two models via reinforcement learning with an effective shared-critic mechanism. In addition, the construction model enhances the improvement model with high-quality initial solutions via curriculum learning, while the improvement model accelerates the convergence of the construction model through imitation learning. Besides the new framework design, we also propose the efficient Neural Neighborhood Search (N2S), an efficient improvement model employed within the NCS framework. N2S exploits a tailored Markov decision process formulation and two customized decoders for removing and then reinserting a pair of pickup-delivery nodes, thereby learning a ruin-repair search process for addressing the precedence constraints in PDPs efficiently. To balance the computation cost between encoders and decoders, N2S streamlines the existing encoder design through a light Synthesis Attention mechanism that allows the vanilla self-attention to synthesize various features regarding a route solution. Moreover, a diversity enhancement scheme is further leveraged to ameliorate the performance during the inference of N2S. Our NCS and N2S are both generic, and extensive experiments on two canonical PDP variants show that they can produce state-of-the-art results among existing neural methods. Remarkably, our NCS and N2S could surpass the well-known LKH3 solver especially on the more constrained PDP variant.

A Deep Reinforcement Learning-Based Adaptive Large Neighborhood Search for CapacitatedElectric Vehicle Routing Problems

A Deep Reinforcement Learning-Based Adaptive Large Neighborhood Search for CapacitatedElectric Vehicle Routing Problems

Extraction of Tooth Cusps based on DBSCAN Density Clustering and Neighborhood Search Algorithm.

A good tooth cusp extraction is helpful in evaluating the effect of cosmetic dental work in virtual tooth surgery. We propose a new tooth cusp extraction, which integrates the DBSCAN (density-based spatial clustering of applications with noise) clustering algorithm with the neighborhood search algorithm to extract tooth cusp from a three-dimensional cloud-point tooth model. This method used the point cloud height and curvature to screen out the dented point set. Then we employ the DBSCAN clustering algorithm to segment different feature regions of the tooth surface and generate the candidate point set. Finally, the candidate point set was accurately located at the tooth apex through the neighborhood search algorithm and the traversal search method of non-maximum suppression. The experimental results show that the proposed method is superior to the traditional watershed algorithm-based methods by calculating the recall rate and accuracy rate, and also has higher extraction speed and extraction precision than manual extraction methods.

New computational results for integrated production and outbound distribution scheduling problems for a product with a short lifespan

In this paper, we consider an integrated production and outbound distribution scheduling problem with a single production site, and its extension to multiple plants. A set of orders must be satisfied such that the required pieces from a single product must be first processed on a single machine in a plant, then must be delivered to the customers before their lifespan expire using a single vehicle. The goal is to minimize the makespan of the solution, which is the return time of the vehicle after its last trip. We propose an elementary variable neighborhood search to solve the problem, using two new local search operators. Our computational results show that this simple procedure outperforms the existing, sometimes complex approaches on the widely used benchmark dataset. We also review the existing computational results, and demonstrate that in some cases the comparisons in the literature are invalid due to the use of different rounding of the data. By re-evaluating the accessible solutions we provide a fair comparison for each rounding method. We also consider the extension of the problem to multiple plants, and adapt our solution approach for this extension. Our experiments show that our method is competitive in terms of solution quality with the existing solution approach for the problem.

A novel variable neighborhood search approach for cell clustering for spatial transcriptomics.

This paper introduces a new approach to cell clustering using the Variable Neighborhood Search (VNS) metaheuristic. The purpose of this method is to cluster cells based on both gene expression and spatial coordinates. Initially, we confronted this clustering challenge as an Integer Linear Programming minimization problem. Our approach introduced a novel model based on the VNS technique, demonstrating the efficacy in navigating the complexities of cell clustering. Notably, our method extends beyond conventional cell-type clustering to spatial domain clustering. This adaptability enables our algorithm to orchestrate clusters based on information gleaned from gene expression matrices and spatial coordinates. Our validation showed the superior performance of our method when compared to existing techniques. Our approach advances current clustering methodologies and can potentially be applied to several fields, from biomedical research to spatial data analysis.

Variable Neighborhood Search and Alternating Direction Method of Multipliers for Integrated Optimization of Maintenance Windows and Train Timetables

Variable Neighborhood Search and Alternating Direction Method of Multipliers for Integrated Optimization of Maintenance Windows and Train Timetables

Classifying with uncertain data envelopment analysis

Classifications organize entities into categories that identify similarities within a category and discern dissimilarities among categories, and they powerfully classify information in support of analysis. We propose a new classification scheme premised on the reality of imperfect data. Our computational model uses uncertain data envelopment analysis to define a classification's proximity to equitable efficiency, which is an aggregate measure of intra-similarity within a classification's categories. Our classification process has two overriding computational challenges, those being a loss of convexity and a combinatorially explosive search space. We overcome the first challenge by establishing lower and upper bounds on the proximity value, and then by searching this range with a first-order algorithm. We address the second challenge by adapting the p-median problem to initiate our exploration, and by then employing an iterative neighborhood search to finalize a classification. We conclude by classifying the thirty stocks in the Dow Jones Industrial average into performant tiers, by classifying prostate treatments into clinically effectual categories, and dividing airlines into peer groups.

A hybrid variable neighborhood search with bootstrap resampling technique for credit scoring problem

Credit scoring models have played a vitally important role in the granting credit by lenders and financial institutions. Recently, these have gained more attention related to the risk management practice. Many modeling techniques have been developed to evaluate the worthiness of borrowers. This paper presents a credit scoring model via one of local search methods – variable neighborhood search (VNS) algorithm. The optimizing VNS neighborhood structure is a useful method applied to solve credit scoring problems. By simultaneously tuning the neighborhood structure, the proposed algorithm generates optimized weights which are used to build a linear discriminant function. The experimental results obtained by applying this model on simulated and real datasets prove its high efficiency and evaluate its significant value on credit scoring.

A Knowledge-Assisted Variable Neighborhood Search for Two-Sided Assembly Line Balancing Considering Preventive Maintenance Scenarios

A Knowledge-Assisted Variable Neighborhood Search for Two-Sided Assembly Line Balancing Considering Preventive Maintenance Scenarios

Advancing Healthcare Accessibility through a Neighborhood Search Recommendation Tool

Advancing Healthcare Accessibility through a Neighborhood Search Recommendation Tool

A hybrid heuristic approach for the multi-objective multi depot vehicle routing problem

Efficiency in logistics is often affected by the fair distribution of the customers along the routes and the available depots for goods delivery. From this perspective, in this study, the Multi-depot Vehicle Routing Problem (MDVRP), by considering two objectives, is addressed. The two objectives in conflict for MDVRP are the distance traveled by vehicles and the standard deviation of the routes’ length. A significant standard deviation value provides a small distance traveled by vehicles, translated into unbalanced routes. We have used a weighted average objective function involving the two objectives. A Variable Neighborhood Search algorithm within a Chu-Beasley Genetic Algorithm has been proposed to solve the problem. For decision-making purposes, several values are chosen for the weight factors multiplying the terms at the objective function to build up a non-dominated front of solutions. The methodology is tested in large-size instances for the MDVRP, reporting noticeable results for managerial insights.

Truck-drone joint path planning for post-disaster emergency material deployment considering fairness

As the expert gear of the emergency rescue system, drones are frequently utilized to distribute supplies following a calamity. The cost and effectiveness of rescue efforts as well as equitable distribution should be taken into account when allocating emergency supplies to disaster-affected areas. This work explores the emergency material allocation problem for truck-drone joint transportation with dynamic energy restrictions based on taking the fairness of emergency material allocation into consideration. In order to guarantee the equitable distribution of materials, the psychological stress experienced by the victims at each catastrophe site is measured using the relative deprivation cost. An adaptive large-scale neighborhood search method serves as the foundation for the creation of a two-stage heuristic algorithm, which reduces the overall cost of the system. The integer programming model MIP is built for this purpose. The research findings can serve as a useful guide for developing a just and effective emergency drone rescue system, and the testing results demonstrate the viability and effectiveness of the two-stage heuristic algorithm.