Hybrid Variable Neighborhood Search Research Articles

Joint optimization of train timetable and passing facility: A capacity allocation strategy

To addreess the challenge of overcrowding in the urban rail transit system, this paper proposes a novel joint optimization strategy that optimizes the timetable and the passing facility’s working status (PFWS) simultaneously according to the passenger demands. To this end, a mixed-integer nonlinear programming (MINLP) model is formulated to jointly minimize overcrowding on trains and platforms, as well as the passenger travel time. The passing facility setting and the passenger flow activities depiction are particularly taken into account by the rigorous constraints. Subsequently, we design a simulation-based hybrid variable neighborhood search (SHVNS) algorithm to efficiently generate near-optimal solution. This algorithm incorporates a variable neighborhood search (VNS) algorithm, a randomly reset strategy, and a simulation-based algorithm. The VNS algorithm includes five neighborhood operators to enhance search efficiency. Finally, various experiments with the real-world data from Chongqing rail transit line 6 in China are tested to prove the applicability and the performance of our proposed model and algorithm. The computational results show that the joint optimization strategy outperforms a current operation strategy in terms of the overcrowded situation on trains and platforms, often by more than 60%, and some management implications and theoretical support can be obtained for the urban rail transit operator.

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 hybrid variable neighborhood search heuristic for the sustainable time-dependent truck-drone routing problem with rendezvous locations

A hybrid variable neighborhood search heuristic for the sustainable time-dependent truck-drone routing problem with rendezvous locations

The paired pickup and delivery problem with profit in a two-echelon delivery system with multiple trucks and drones

ABSTRACT Recently researchers proposed truck and drone coordination to increase delivery efficiency and suggested various truck-drone routing problems. In this paper, we also focused on truck and drone coordination and introduced the paired pickup and delivery problem with profit in a two-echelon delivery system. To solve the problem, we propose a hybrid variable neighborhood search algorithm. For this algorithm, we adapted existing neighborhood search operators from the literature and considering the structure of the proposed problem, developed new neighborhood search operators. Also, we have carried out numerous computational experiments to evaluate the proposed solution methods’ performance, where the results show the efficiency of the proposed algorithms. The results highlight that in the paired pickup and delivery problem, for small values of drone operational costs, employing the two-echelon truck and drone routing system increases the profit by up to 5.6 percent in comparison to the vehicle routing system with drones.

Hybrid Variable Neighborhood Search for Solving School Bus-Driver Problem with Resource Constraints

The School Bus-Driver Problem with Resource Constraints (SBDP-RC) is an optimization problem with many practical applications. In the problem, the number of vehicles is prepared to pick a number of pupils, in which the total resource of all vehicles is less than a predefined value. The aim is to find a tour minimizing the sum of pupils’ waiting times. The problem is NP-hard in the general case. In many cases, reaching a feasible solution becomes an NP-hard problem. To solve the large-sized problem, a metaheuristic approach is a suitable approach. The first phase creates an initial solution by the construction heuristic based on Insertion Heuristic. After that, the post phase improves the solution by the General Variable Neighborhood Search (GVNS) with Random Neighborhood Search combined with Shaking Technique. The hybridization ensures the balance between exploitation and exploration. Therefore, the proposed algorithm can escape from local optimal solutions. The proposed metaheuristic algorithm is tested on a benchmark to show the efficiency of the algorithm. The results show that the algorithm receives good feasible solutions fast. Additionally, in many cases, better solutions can be found in comparison with the previous metaheuristic algorithms.

Optimal allocation with known covariates into two treatments under generalized linear model through Hybrid VNS algorithm

The problem of optimal allocation of experimental units with known covariates into several treatment groups under linear ANCOVA model has already been discussed by the same authors and the optimal allocation design has been derived through an efficient algorithm named as hybrid variable neighborhood search (VNS) algorithm. In this work, we have addressed the same issue with regard to D- and A-optimality under the generalized linear model (GLM) set-up, assuming the response variable to be count or binary. The issue of mean covariate balance is also addressed through a constrained optimal approach in which a compromise between balance and optimality is maintained. The performance of the optimal and constrained optimal allocation design obtained through the hybrid VNS algorithm, under various GLM frameworks, has been investigated through simulation studies and real-life example.

Last mile delivery routing problem using autonomous electric vehicles

This paper presents a study on the application of Autonomous Delivery Vehicles (ADVs) in last-mile delivery for urban logistics. Specifically, we focus on a routing problem using multi-stop ADVs, with the goal of minimizing route and vehicle usage costs while satisfying several constraints associated with load and battery capacities, maximum route duration for ADVs, and maximum walking distance for customers. We refer to this problem as the Autonomous Delivery Vehicle Routing Problem (ADVRP) and present its mixed-integer linear programming formulation. Due to the NP-hardness of the problem, we propose a two-phase metaheuristic approach that first clusters customers and determines stopping locations for the ADVs, followed by a phase that determines the optimal routes for the ADVs using hybrid variable neighborhood search and simulated annealing. To evaluate our proposed solution methodology, we conduct computational experiments on various related Vehicle Routing Problems (VRPs) from the literature and newly generated ADVRP instances. The results show that the proposed two-phase metaheuristic approach can produce high-quality solutions with minimal computational effort while outperforming an exact solver in 26 medium- and large-sized instances of ADVRP and reaching optimal solutions in most VRP instances and related problems. Furthermore, we conduct sensitivity analyses on selected problem parameters and present a case study in Istanbul, Turkey to provide managerial insights for implementing ADVs in urban logistics.

A hybrid simulated annealing and variable neighborhood search algorithm for the close-open electric vehicle routing problem

AbstractElectric Vehicles (EVs) are the future of transportation, but due to their battery and charging technology they cannot yet directly replace traditional vehicles. Nonetheless, EVs are a great option for city-logistics, due to the small distances and their zero local emissions. In this paper, a novel variant of the Electric Vehicle Routing Problem (EVRP), called Close-Open EVRP (COEVRP), is presented. It considers ending EV trips at Charging Stations, as opposed to other EVRP variants that only allow for en-route charging. This new variant follows a traditional routing scheme, allowing EVs to recharge only at the end of their route. The objective is to minimize energy consumption, as well as the number of vehicles. The energy consumption function takes into account the weight of the transported items. A mathematical formulation for the problem is presented and small instances were solved using a commercial solver. To solve larger instances, a hybrid metaheuristic combining Simulated Annealing and Variable Neighborhood Search algorithm was employed and thoroughly tested.

Preface

China is currently the world’s largest producer of wind and solar energy. China has made great efforts in promoting decarbonization in energy and reducing greenhouse gas emissions. In the future, China will accelerate the development of low-carbon and zero-carbon technologies to help promote green development. China has a clear pathway to build a more sustainable, secure and inclusive energy future.Following this concept, the 2022 International Conference on Renewable Energy and Electrical Technology (ICREET 2022) took place held during December 16th-18th, 2022 in Xi’an, China (virtual event). The conference provided a platform of international standards where all the participants discussed and shared persuasive key advances in renewable energy and electrical technology. It also offered a unique venue for renewing professional relationships and for remaining up-to-date variations in the challenging and expanding fields.This volume contains the papers presented at the 2022 International Conference on Renewable Energy and Electrical Technology. The papers covering various topics including Renewable Energy Power Generation, Natural Gas Hydrate Development Technology, Energy Internet Technology, Electric Motors and Drives, Power System Analysis, etc. have been through rigorously reviewed by experts and meet the requirements of publication.The online conference attracted about 60 individuals, and three sophisticated professors performed their keynote speeches during the conference. Among them, Prof. Pierluigi Siano from University of Salerno, Italy, one of our keynote speakers, shared with us his research on Energy Communities in Smart Grids. A novel scalable and privacy-preserving distributed parallel optimization that allows the participation of large-scale aggregation of prosumers with residential PV-battery systems in the market for the ancillary service (ASM) was proposed in this study. To consider both reserve capacity and reserve energy, day-ahead and real-time stages in the ASM were considered. A method, based on hybrid Variable Neighborhood Search (VNS) and distributed parallel optimization was designed for the day ahead and real-time optimization. All the keynote speakers made brilliant speeches combing their own research domains and experiences, and shared unique insights, creating a great platform for academic communication.On behalf of the Conference Organizing Committee, we would like to extend our sincere acknowledgement to all the keynote speakers, peer reviewers, and all the participants. Particularly, our heartfelt thanks and appreciation go to the Editors and Managers, Journal of Physics: Conference Series for their help and kind cooperation during the preparation of the proceedings. Hopefully, all participants and other interested readers can benefit significantly from the proceedings and find it rewarding.The Committee of ICREET 2022List of Committee member is available in this Pdf.

Solving a Production Lot-Sizing and Scheduling Problem from an Enhanced Inventory Management Perspective

In this study, we consider a production lot-sizing and scheduling problem found in the fruit juice production industry from an enhanced inventory management perspective. The problem can be classified as a P2SMM (two-stage multi-machine lot-scheduling) problem. We extended the classical P2SMM problem by incorporating an additional inventory management aspect of finished products to reflect a possible real-life case problem, specifically regarding the shelf-life concept and limited warehouse capacity, with a possibility of outsourcing the warehousing demand to a third-party logistics company. We developed the mixed integer linear programming (MILP) model to fully represent the considered problem (due to the NP-hard nature of the problem, only small-scale instances could be solved to optimality), and the hybrid variable neighborhood search with linear programming (VNS/LP) model to solve both small and real-life large-scale problem instances. The goal of the developed models is to minimize total costs that consist of the cost of backordering, the cost of planned minimum and maximum stock level violation, the cost of warehouse capacity overflow, the costs of production setup time and unused available production time. The main idea of the VNS/LP model is to solve the scheduling segment of P2SMM (the production sequence) via a VNS heuristic, and the lot-sizing segment of P2SMM via the linear programming (LP) model. Based on the results from five variants of the problem setup, a potential decision maker can have an overview of the impact of different important input parameters (production time costs, warehouse capacity and costs, inventory related costs and production demand) on the total cost of a production process and improve its efficiency in changing conditions.

An adaptive hybrid variable-large neighborhood search algorithm for profit maximization problem in designing production lines

Designing production lines is an important research issue for both academy as well as industry since it should consider both system efficiency and production costs. In this study, the buffer allocation problem (BAP) is solved to maximize the profit of unreliable lines. The profit of a production line is a function of the throughput and various costs, such as WIP, holding and buffer space costs. In this respect, a crucial decision-making problem, i.e., BAP, is how to allocate the finite buffers to deal with the trade-off between maximizing the throughput of the line and production costs. In this study, profit maximization problem is formulated considering two different objective functions and solved under different constraint sets. An adaptive hybrid variable neighborhood search algorithm that incorporates large neighborhood search as a part of the diversification strategy is proposed to solve the problem. Moreover, a new initialization procedure based on the buffer location providing more profit is proposed to reduce the search effort. The efficiency of the proposed algorithm is tested on existing benchmark problems as well as the newly introduced large-sized data sets. In addition to these experiments, a comprehensive experimental design is conducted to determine the influencing factors on the problem at hand. The experimental study reveals that the proposed solution algorithm is capable to solve the profit maximization problem for large production lines, and the number of machines and the reliability parameters are the most influential factors in solving the BAP for profit maximization. Moreover, it has been observed that the proposed initialization procedure significantly reduces the search effort.

Hybrid variable neighborhood search for automated warehouse scheduling

Hybrid variable neighborhood search for automated warehouse scheduling

Electric vehicle routing problem with flexible deliveries

Growing concerns about the climate change have forced governments to initiate tighter environmental regulations and tougher emission reduction targets, increasing the interest on electromobility. Logistics operators started employing electric vehicles (EVs) and must face new operational planning challenges. Moreover, with an ever-growing interest in e-commerce, parcel delivery is taking new shapes by offering flexible delivery options to the customers. To mitigate these issues, we introduce the Electric Vehicle Routing Problem with Flexible Deliveries (EVRP-FD), where the customers are served using a fleet of EVs that can recharge their batteries along their routes. In this problem, a customer may specify different delivery locations for different time windows. Our objective is to serve the customers while minimising the total travelled distance using minimum number of vehicles. We first give the mathematical model and then develop a hybrid Variable Neighbourhood Search coupled with Tabu Search by proposing new mechanisms to solve the problem effectively. Then, we verify the performance of our algorithm on instances from the literature. We also introduce new instances for the EVRP-FD and perform an extensive computational study to investigate the trade-offs associated with different operational factors. Finally, we present a case study in Nottingham, UK to provide further insights.

Ring hierarchical hub network design problem: Exact and heuristic solution methods

In this paper, we study the hierarchical hub network design problem with a ring-star-star structure. In this problem, the hubs are located in two layers. In the first layer, central hubs (main hubs) are located in a ring structure, and in the second layer, secondary hubs are located. Each secondary hub is allocated to a central hub. Other demand points can also be allocated to each of these hubs (central hubs or secondary hubs). The objective is to minimize transportation costs on the network. This problem applies to communication networks when establishing direct links between demand nodes is not cost-effective, and there are two levels of service for customers. Also, the ring structure is used to reduce costs associated with full communication between central hubs. We present a mixed-integer programming model for the problem and report the results of the problem solving for a numerical example on the CAB dataset. Also, we present three solution methods for the problem. First, by exploiting the decomposable structure of the proposed model, we introduce an accelerated Benders decomposition algorithm. Then, we present a hybrid genetic algorithm that uses the Dijkstra algorithm to evaluate solutions. Next, we present a hybrid variable neighborhood search algorithm that uses the Dijkstra algorithm to calculate the cost of each solution. Also, a relax-and-fix algorithm is proposed to find near-optimal feasible solutions for the problem. Computational results are presented on the USA423 dataset. The results show that the proposed relax-and-fix algorithm can solve instances with up to 100 nodes. Also, the proposed hybrid algorithms can solve instances with up to 423 nodes. The performance of the proposed hybrid variable neighborhood search is better than the proposed hybrid genetic algorithm in solving large-sized instances. • The paper formulates the hierarchical hub network using a ring structure for the central hubs. • Minimizing the total flow cost while satisfying the demands is considered. • The paper develops solution methodologies based on Accelerated Benders Decomposition, Hybrid Genetic Algorithm, and Hybrid Variable Neighborhood Search Algorithm. • The paper presents computational studies using CAB and USA423 datasets.

Solving Multi-Objective Paired Single Row Facility Layout Problem Using Hybrid Variable Neighborhood Search

The footwear industry is distinguished by its manual assembly line and a high proportion of shared workstation configuration. This study focuses on a subset of the single row facility layout problem known as the paired single row facility layout problem. As one of type of single-row facility layout, the paired single row facility layout problem cannot be solved quickly. Further, different objectives also need to be considered in the decision-making process. Therefore, multi-objective approaches are proposed to minimize the penalty of material handler usage while maximizing the adjacency function based on each workstation's closeness rating. A Single Row Facility Layout is an NP-hard problem; this problem also belongs to the NP-hard problem class. As a result, we propose a hybrid method combining variable neighborhood search (VNS) and genetic algorithm (GA) to solve the problem of obtaining the optimal configuration of a multi-objective paired single-row assembly line. A heuristic approach was used to create the schematic representation solution. To obtain the neighborhood solutions, a hybrid VNSGA was used. The schematic representation solution employs crossover and variable neighborhood descent. Using the concept of VNS, the neighborhood was changed in each generation.

A hybrid moth flame optimization and variable neighbourhood search technique for optimal design of IIR filters

A hybrid moth flame optimization and variable neighbourhood search technique for optimal design of IIR filters

The multi-depot heterogeneous VRP with backhauls: formulation and a hybrid VNS with GRAMPS meta-heuristic approach

In this paper, we investigate the Multi-Depot Heterogeneous VRP with Backhauls. Though the problem is a generalisation of three existing routing problems, this is the first time this combined routing problem is investigated. A mathematical formulation is first presented followed by some tightening. A powerful and novel hybridisation of Variable Neighbourhood Search (VNS) with the Greedy Randomized Adaptive Memory Programming Search is proposed. As there are no problem instances available for bench-marking and evaluation purposes, we generated data sets by combining those from existing vehicle routing problems. The proposed meta-heuristic obtains a number of optimal solutions for small instances and yields about \(13\%\) gap from the lower bounds compared to nearly \(40\%\) and \(20\%\) average gap values for our CPLEX implementation and the VNS without hybridisation, respectively.

A stochastic hierarchical approach for the master surgical scheduling problem

For operating rooms in a hospital, the Master Surgical Scheduling Problem concerns the assignments of surgeons to time blocks over a planning horizon. This paper contributes mathematical models for this problem that consider multiple stakeholders and human and physical resources, including surgeons, machines, operating rooms, recovery ward beds, and patients. The models meet external waiting time targets, minimize differences between the actual and target number of patients, maximize the utilization of operating rooms, minimize variations in the assignments of surgeons to both weekdays and operating rooms, minimize the total number of expected patients in the recovery ward, and minimize variations in the recovery ward utilization. In addition to exact methods, hybrid variable neighbourhood search - genetic algorithm and late acceptance hill climbing heuristics are used to obtain solutions. Rival models from the literature are compared to this approach. Results from numerical experiments show that it is possible to develop master surgical schedules that achieve these goals while outperforming the rival models.

A hybrid variable neighborhood search approach for the multi-depot green vehicle routing problem

The Multi-Depot Green Vehicle Routing Problem (MDGVRP) is an extension of the well-known Green Vehicle Routing Problem (GVRP) where a fleet of alternative fuel-powered vehicles (AFVs) are used to serve the customers. GVRP consists of determining AFV tours such that the total distance travelled is minimum. The AFVs depart from the depot, serve a set of customers, and complete their tours at the depot without exceeding their driving range and the maximum tour duration. AFVs may refuel en-route at public refueling stations. In MDGVRP, the AFVs are dispatched from different depot locations and may refuel during the day at any depot or refueling station. We formulate MDGVRP as a mixed integer linear programming model and develop a hybrid General Variable Neighborhood Search and Tabu Search approach by proposing new problem-specific neighborhood structures to solve the problem effectively. We assess the performance of our method using the GVRP dataset from the literature. Our results show that the proposed method can provide high quality solutions in short computation times. Then, we extend these instances to the multi-depot case and compare our solutions for small-size instances with the optimal solutions. We also report our results for large-size problems and investigate the trade-offs associated with operating multiple depots and adopting different refueling policies to provide further insights for both academicians and practitioners.

Minimizing maximum tardiness on a single machine with family setup times and machine disruption

Due to the complexity of manufacturing processes, many industries today try to group their production components into families and use a common setup time to process every family. Also, the failure of machinery has an important impact on the planning of production systems. This paper deals with the problem of single machine scheduling with family setup times and randomly machine breakdown. It is assumed that there is disruption along the planning horizon with uncertain start and duration times based on a specific probability distribution function. The goal is to minimize the maximum expected tardiness. For this problem, a hill climbing based heuristic (HCO) and a novel hybrid variable neighborhood search metaheuristic (HVNS) have been developed. A branch and bound (BB) procedure is also proposed to optimally solve this problem using HCO and HVNS algorithms to derive upper bounds. Comparison of the solutions obtained from the presented algorithms with the mathematical programming model of the problem in small-scale instances shows that the solutions obtained from the proposed algorithms have a good quality in terms of the expected value of the objective function and the solving time. The computational results for executing the proposed algorithms on 36 series of various instances show that the BB procedure is able to solve 97.76% of the instances within the specified time frame and HVNS metaheuristic is capable to solve 41.07% of them, optimally. Also, the maximum mean deviation of HVNS solutions from the optimal solution is reported below 5% for hard instances and below 15% for instances with medium complexity. After performing a non-parametric hypothesis test, it is found that at a confidence level 99.9%, the deviation index for HVNS is less than 1.5% for difficult problems and less than 9% for semi-difficult ones. Finally, the results of solving large scale instances, demonstrate the efficiency of proposed metaheuristic.