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

With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored.In this paper,the wind farm layout optimization problem is defined.Then,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is proposed.GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved.The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives.The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy.The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same cost.The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.

Hybrid artificial bee colony algorithm with variable neighborhood search and memory mechanism

Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.

A fast block-matching algorithm based on variable shape search

Block-matching motion estimation plays an important role in video coding. The simple and efficient fast block-matching algorithm using Variable Shape Search (VSS) proposed in this paper is based on diamond search and hexagon search. The initial big diamond search is designed to fit the directional centre-biased characteristics of the real-world video se- quence, and the directional hexagon search is designed to identify a small region where the best motion vector is expected to locate. Finally, the small diamond search is used to select the best motion vector in the located small region. Experimental results showed that the proposed VSS algorithm can significantly reduce the computational complexity, and provide competitive computational speedup with similar distortion performance as compared with the popular Diamond-based Search (DS) algorithm in the MPEG-4 Simple Profile.

Particle Swarm Optimization Embedded in Variable Neighborhood Search for Task Scheduling in Cloud Computing

In cloud computing system,it is a hot and hard issue to find the optimal task scheduling method that makes the processing cost and the running time minimum. In order to deal with the task assignment,a task interaction graph was used to analyze the task scheduling; a modeling for task assignment was formulated and a particle swarm optimization (PSO)algorithm embedded in the variable neighborhood search (VNS) to optimize the task scheduling was proposed. The experimental results show that the method is more effective than the PSO in processing cost,transferring cost, and running time. When the task is more complex,the effect is much better. So,the algorithm can resolve the task scheduling in cloud computing and it is feasible,valid,and efficient.

Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

Effective Hybrid Teaching-learning-based Optimization Algorithm for Balancing Two-sided Assembly Lines with Multiple Constraints

Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

An improved artificial bee colony algorithm for steelmaking–refining–continuous casting scheduling problem

Steelmaking–refining–Continuous Casting(SCC) scheduling is a worldwide problem, which is NP-hard. Effective SCC scheduling algorithms can help to enhance productivity, and thus make significant monetary savings. This paper develops an Improved Artificial Bee Colony(IABC) algorithm for the SCC scheduling. In the proposed IABC, charge permutation is employed to represent the solutions. In the population initialization, several solutions with certain quality are produced by a heuristic while others are generated randomly. Two variable neighborhood search neighborhood operators are devised to generate new high-quality solutions for the employed bee and onlooker bee phases, respectively. Meanwhile, in order to enhance the exploitation ability, a control parameter is introduced to conduct the search of onlooker bee phase. Moreover, to enhance the exploration ability,the new generated solutions are accepted with a control acceptance criterion. In the scout bee phase, the solution corresponding to a scout bee is updated by performing three swap operators and three insert operators with equal probability. Computational comparisons against several recent algorithms and a state-of-the-art SCC scheduling algorithm have demonstrated the strength and superiority of the IABC.

Intervention decision-making in MAV/UAV cooperative engagement based on human factors engineering

Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering（HFE）.Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that,constraint conditions of intervention decision-making of commanders based on HFE（IDMCBHFE） are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search（VNS） algorithm, a K-best optimization variable neighborhood search mixed algorithm（KBOVNSMA） is designed to solve the model. Finally,through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.

Motion Planning Method for Obstacle Avoidance of 6-DOF Manipulator Based on Improved A* Algorithm

The conventional A* algorithm may suffer from the infinite loop and a large number of search data in the process of motion planning for manipulator. To solve the problem,an improved A* algorithm is proposed in this paper by the means of selecting middle points and applying variable step segments searching during the searching process. In addition,a new method is proposed for collision detection in the workspace. In this paper,the MOTOMAN MH6 manipulator with 6-DOF is applied for motion plan. The algorithm is based on the basis of the simplification for the manipulator and obstacles by cylinder enveloping. Based on the analysis of collision detection,the free space can be achieved which makes it possible for the entire body to avoid collisions with obstacles. Compared with the Conventional A*,the improved algorithm deals with less searching points and performs more efficiently. The simulation developed in VC + + with OpenGL and the actual system experiments prove effectiveness and feasibility of this improved method.

Particle swarm optimization with variable neighborhood search for multiobjective flexible job shop scheduling problem

The simulation on benchmarks is a very simple and efficient method to evaluate the performance of the algorithm for solving flexible job shop scheduling model.Due to the assignment and scheduling decisions,flexible job shop scheduling problem(FJSP)becomes extremely hard to solve for production management.A discrete multi-objective particle swarm optimization(PSO)and simulated annealing(SA)algorithm with variable neighborhood search is developed for FJSP with three criteria:the makespan,the total workload and the critical machine workload.Firstly,a discrete PSO is designed and then SA algorithm performs variable neighborhood search integrating two neighborhoods on public critical block to enhance the search ability.Finally,the selection strategy of the personal-best individual and global-best individual from the external archive is developed in multi-objective optimization.Through the experimental simulation on matlab,the tests on Kacem instances,Brdata instances and BCdata instances show that the modified discrete multi-objective PSO algorithm is a promising and valid method for optimizing FJSP with three criteria.

A Hybrid Heuristic Algorithm for Solving COVID-19’s Social Distancing at Universities Campus

Social distancing during COVID-19 has become one of the most important measures in reducing the risks of the spread of the virus. Implementing thesemeasures at universities is crucial and directly related to the physical attendance ofthe populations of students, professors, employees, and other members on campus. This research proposes an automated scheduling approach that can help universities and schools comply with the social distancing regulations by providingassistance in avoiding huge assemblages of people. Furthermore, this paper proposes a novel course timetable-scheduling scheme based on four main constraints.First, a distance of two meters must be maintained between each student inside theclassroom. Second, no classrooms should contain more than 20% of their regularcapacity. Third, there would be no back-to-back classes. Lastly, no lectures shouldbe held simultaneously in adjacent classrooms. The proposed approach wasimplemented using a variable neighborhood search (VNS) approach with an adaptive neighborhood structure (AD-NS) to resolve the problem of scheduling coursetimetables at Al-Ahlyyia Amman University. However, the experimental resultsshow that the proposed techniques outperformed the standard VNS tested on university course timetabling benchmark dataset ITC2007-Track3. Meanwhile, theapproach was tested using datasets collected from the faculty of information technology at Al-Ahlyyia Amman University (Jordan). Where the results showed that,the proposed technique could help educational institutes to resume their regularoperations while complying with the social distancing guidelines.

MIP Formulations and Metaheuristics for Multi-Item Capacitated Lot-Sizing Problem with Non-Customer Specific Production Time Windows and Setup Times

Our research focuses on the development of two cooperative approaches for resolution of the multi-item capacitated lot-sizing problems with time windows and setup times (MICLSP-TW-ST). In this paper we combine variable neighborhood search and accurate mixed integer programming (VNS-MIP) to solve MICLSP-TW-ST. It concerns so a particularly important and difficult problem in production planning. This problem is NP-hard in the strong sense. Moreover, it is very difficult to solve with an exact method;it is for that reason we have made use of the approximate methods. We improved the variable neighborhood search (VNS) algorithm, which is efficient for solving hard combinatorial optimization problems. This problem can be viewed as an optimization problem with mixed variables (binary variables and real variables). The new VNS algorithm was tested against 540 benchmark problems. The performance of most of our approaches was satisfactory and performed better than the algorithms already proposed in the literature.

A Two-phase Solution Algorithm for the Inventory Routing Problem with Time Windows

The main objective of this paper is to propose a two-phase solution algorithm for solving the Inventory Routing Problem with Time Windows （IRPTW）, which has not been excessively researched in the literature. The solution approach is based on （a） a simple simulation for the planning phase （Phase I） and （b） the Variable Neighborhood Search Algorithm （VNS） for the routing phase （Phase II）. Testing instances are established to investigate algorithmic performance, and the computational results are then reported. The computational study underscores the importance of integrating the inventory and vehicle routing decisions. Graphical presentation formats are provided to convey meaningful insights into the problem.

Optimization of fixed aviation support resource station configuration for aircraft carrier based on aircraft dispatch mission scheduling

Before the dispatch of the carrier-based aircraft,a series of pre-flight preparation operations need to be completed on the flight deck.Flight deck fixed aviation support resource station configuration has an important impact on operation efficiency and sortie rate.However,the resource station configuration is determined during the aircraft carrier design phase and is rarely modified as required,which may not be suitable for some pre-flight preparation missions.In order to solve the above defects,the joint optimization of flight deck resource station configuration and aircraft carrier pre-flight preparation scheduling is studied in this paper,which is formulated as a two-tier optimization decision-making framework.An improved variable neighborhood search algorithm with four original neighborhood structures is presented.Dispatch mission experiment and algorithm performance comparison experiment are carried out in the computational experiment section.The correlation between the pre-flight preparation time(makespan)and flight deck cabin occupancy percentage is given,and advantages of the proposed algorithm in solving the mathematical model are verified.

A hybrid genetic algorithm to optimize device allocation in industrial Ethernet networks with real-time constraints

With the advance of automation technology,the scale of industrial communication networks at field level is growing.Guaranteeing real-time performance of these networks is therefore becoming an increasingly difficult task.This paper addresses the optimization of device allocation in industrial Ethernet networks with real-time constraints (DAIEN-RC).Considering the inherent diversity of real-time requirements of typical industrial applications,a novel optimization criterion based on relative delay is proposed.A hybrid genetic algorithm incorporating a reduced variable neighborhood search (GA-rVNS) is developed for DAIEN-RC.Experimental results show that the proposed novel scheme achieves a superior performance compared to existing schemes,especially for large scale industrial networks.

Vehicle flow formulation for two-echelon time-constrained vehicle routing problem

Two-echelon routing problems,including variants such as the two-echelon vehicle routing problem(2E-VRP)and the two-echelon location routing problem(2E-LRP),involve assignment and location decisions.However,the two-echelon time-constrained vehicle routing problem(2E-TVRP)that caters to from-linehaul-to-delivery practices does not involve assignment decisions.This routing problem variant for networks with two eche-lons has not yet attracted enough research interest.Localized or long-distance services suffer from the lack of the assignment decisions between satellites and customers.Therefore,the 2E-TVRP,rather than using assignment decisions,adopts time constraints to decide the routes on each of the two interacting echelons:large-capacity vehicles trans-port cargoes among satellites on the first echelon,and small-capacity vehicles deliver cargoes from satellites to customers on the second echelon.This study introduces a mixed integer linear programming model for the 2E-TVRP and proposes a heuristic algorithm that incorporates the savings algorithm followed by a variable neighborhood search phase.Illustrative examples are used to test the mathematical formulation and the heuristic and a case study is used to demonstrate that the heuristic can effectively solve realistic-size instances of the 2E-TVRP.

A hybrid particle swarm optimization algorithm for the capacitated location routing problem

Purpose–The purpose of this paper is to solve the capacitated location routing problem(CLRP),which is an NP-hard problem that involves making strategic decisions as well as tactical and operational decisions,using a hybrid particle swarm optimization(PSO)algorithm.Design/methodology/approach–PSO,which is a population-based metaheuristic,is combined with a variable neighborhood strategy variable neighborhood search to solve the CLRP.Findings–The algorithm is tested on a set of instances available in the literature and gave good quality solutions,results are compared to those obtained by other metaheuristic,evolutionary and PSO algorithms.Originality/value–Local search is a time consuming phase in hybrid PSO algorithms,a set of neighborhood structures suitable for the solution representation used in the PSO algorithm is proposed in the VNS phase,moves are applied directly to particles,a clear decoding method is adopted to evaluate a particle(solution)and there is no need to re-encode solutions in the form of particles after applying local search.

Free-floating bike-sharing green relocation problem considering greenhouse gas emissions

This paper introduces the problem of green bike relocation considering greenhouse gas emissions in free-floating bike-sharing systems(FFBSSs)and establishes a mathematical model of the problem.This model minimizes the total imbalance degree of bikes in the FFBSS and the greenhouse gas emissions generated by relocation in the FFBSS.Before the relocation phase,the FFBSS is divided into multiple relocation areas using a two-layer clustering method to reduce the scale of the relocation problem.In the relocation phase,the relocation route problem is converted into a pickup and delivery vehicle-routing problem.Then,an adaptive variable neighbourhood tabu search algorithm with a three-dimensional tabu list is proposed,which can simultaneously solve the relocation problem and the routing problem.A computational study based on the actual FFBSS used in Shanghai shows that this method can effectively solve the green relocation problem of FFBSSs.