Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization

Waste collection is an important part of waste management system.Transportation costs and carbon emissions can be greatly reduced by proper vehicle routing.Meanwhile,each vehicle can work again after achieving its capacity limit and unloading the waste.For this,an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection,which incorporates practical factors like the limited capacity,maximum working hours,and multiple trips of each vehicle.Considering both economy and environment,fixed costs,fuel costs,and carbon emission costs are minimized together.To solve the formulated model effectively,contribution-based adaptive particle swarm optimization is proposed.Four strategies named greedy learning,multi-operator learning,exploring learning,and exploiting learning are specifically designed with their own searching priorities.By assessing the contribution of each learning strategy during the process of evolution,an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the algorithm.Moreover,an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are improved.Performance of the proposed algorithm is tested on ten waste collection instances,which include one real-world case derived from the Green Ring Company of Jiangbei New District,Nanjing,China,and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark datasets.Comparisons with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.

Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles

Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines,and the rationality of their routes plays the direct impact on operation safety and energy consumption.Rich studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil,however,less works are for electric trackless rubber-tyred vehicles.Furthermore,energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving,especially the limited cruising ability of electric trackless rubber-tyred vehichles(TRVs).To address this issue,an energy consumption model of an electric trackless rubber-tyred vehicle is formulated,in which the effects from total mass,speed profiles,slope of roadways,and energy management mode are all considered.Following that,a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance,allowable load,and endurance power.As a problem-solver,an improved artificial bee colony algorithm is put forward.More especially,an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific space.In order to assign onlookers to some promising food sources reasonably,their selection probability is adaptively adjusted.For a stagnant food source,a knowledge-driven initialization is developed to generate a feasible substitute.The experimental results on four real-world instances indicate that improved artificial bee colony algorithm(IABC)outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.

Variable Reduction Strategy Integrated Variable Neighborhood Search and NSGA-II Hybrid Algorithm for Emergency Material Scheduling

Developing a reasonable and efficient emergency material scheduling plan is of great significance to decreasing casualties and property losses.Real-world emergency material scheduling(EMS)problems are typically large-scale and possess complex constraints.An evolutionary algorithm(EA)is one of the effective methods for solving EMS problems.However,the existing EAs still face great challenges when dealing with large-scale EMS problems or EMS problems with equality constraints.To handle the above challenges,we apply the idea of a variable reduction strategy(VRS)to an EMS problem,which can accelerate the optimization process of the used EAs and obtain better solutions by simplifying the corresponding EMS problems.Firstly,we define an emergency material allocation and route scheduling model,and a variable neighborhood search and NSGA-II hybrid algorithm(VNS-NSGAII)is designed to solve the model.Secondly,we utilize VRS to simplify the proposed EMS model to enable a lower dimension and fewer equality constraints.Furthermore,we integrate VRS with VNS-NSGAII to solve the reduced EMS model.To prove the effectiveness of VRS on VNS-NSAGII,we construct two test cases,where one case is based on a multi-depot vehicle routing problem and the other case is combined with the initial 5∙12 Wenchuan earthquake emergency material support situation.Experimental results show that VRS can improve the performance of the standard VNS-NSGAII,enabling better optimization efficiency and a higher-quality solution.

Load Optimization Scheduling of Chip Mounter Based on Hybrid Adaptive Optimization

A chip mounter is the core equipment in the production line of the surface-mount technology,which is responsible for finishing the mount operation.It is the most complex and time-consuming stage in the production process.Therefore,it is of great significance to optimize the load balance and mounting efficiency of the chip mounter and improve the mounting efficiency of the production line.In this study,according to the specific type of chip mounter in the actual production line of a company,a maximum and minimum model is established to minimize the maximum cycle time of the chip mounter in the production line.The production efficiency of the production line can be improved by optimizing the workload scheduling of each chip mounter.On this basis,a hybrid adaptive optimization algorithm is proposed to solve the load scheduling problem of the mounter.The hybrid algorithm is a hybrid of an adaptive genetic algorithm and the improved ant colony algorithm.It combines the advantages of the two algorithms and improves their global search ability and convergence speed.The experimental results show that the proposed hybrid optimization algorithm has a good optimization effect and convergence in the load scheduling problem of chip mounters.

Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms

Due to their advantages in flexibility,scalability,survivability,and cost-effectiveness,drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields.This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms.Given a set of available air defense systems,the problem determines the location of each air defense system in a predetermined region,such that the cost for enemy drones to pass through the region would be maximized.The cost is calculated based on a counterpart drone path planning problem.To solve this adversarial problem,we first propose an exact iterative search algorithm for small-size problem instances,and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances.We implement the evolutionary framework with six popular evolutionary algorithms.Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms.

I_(ϵ+)LGEA:A Learning-Guided Evolutionary Algorithm Based on I_(ϵ+) Indicator for Portfolio Optimization

Portfolio optimization is a classical and important problem in the field of asset management,which aims to achieve a trade-off between profit and risk.Previous portfolio optimization models use traditional risk measurements such as variance,which symmetrically delineate both positive and negative sides and are not practical and stable.In this paper,a new model with cardinality constraints is first proposed,in which the idiosyncratic volatility factor is used to replace traditional risk measurements and can capture the risks of the portfolio in a more accurate way.The new model has practical constraints which involve the sparsity and irregularity of variables and make it challenging to be solved by traditional Multi-Objective Evolutionary Algorithms(MOEAs).To solve the model,a Learning-Guided Evolutionary Algorithm based on I_(ϵ+)indicator(I_(ϵ+)LGEA)is developed.In I_(ϵ+)LGEA,the I_(ϵ+)indicator is incorporated into the initialization and genetic operators to guarantee the sparsity of solutions and can help improve the convergence of the algorithm.And a new constraint-handling method based on I_(ϵ+)indicator is also adopted to ensure the feasibility of solutions.The experimental results on five portfolio trading datasets including up to 1226 assets show that I_(ϵ+)LGEA outperforms some state-of-the-art MOEAs in most cases.

A Multi-Objective Scheduling and Routing Problem for Home Health Care Services via Brain Storm Optimization

At present,home health care(HHC)has been accepted as an effective method for handling the healthcare problems of the elderly.The HHC scheduling and routing problem(HHCSRP)attracts wide concentration from academia and industrial communities.This work proposes an HHCSRP considering several care centers,where a group of customers(i.e.,patients and the elderly)require being assigned to care centers.Then,various kinds of services are provided by caregivers for customers in different regions.By considering the skill matching,customers’appointment time,and caregivers’workload balancing,this article formulates an optimization model with multiple objectives to achieve minimal service cost and minimal delay cost.To handle it,we then introduce a brain storm optimization method with particular multi-objective search mechanisms(MOBSO)via combining with the features of the investigated HHCSRP.Moreover,we perform experiments to test the effectiveness of the designed method.Via comparing the MOBSO with two excellent optimizers,the results confirm that the developed method has significant superiority in addressing the considered HHCSRP.

Simulation of COVID-19 Outbreak in Nanjing Lukou Airport Based on Complex Dynamical Networks

The Corona Virus Disease 2019(COVID-19)pandemic is still imposing a devastating impact on public health,the economy,and society.Predicting the development of epidemics and exploring the effects of various mitigation strategies have been a research focus in recent years.However,the spread simulation of COVID-19 in the dynamic social system is relatively unexplored.To address this issue,considering the outbreak of COVID-19 at Nanjing Lukou Airport in 2021,we constructed an artificial society of Nanjing Lukou Airport based on the Artificial societies,Computational experiments,and Parallel execution(ACP)approach.Specifically,the artificial society includes an environmental model,population model,contact networks model,disease spread model,and intervention strategy model.To reveal the dynamic variation of individuals in the airport,we first modeled the movement of passengers and designed an algorithm to generate the moving traces.Then,the mobile contact networks were constructed and aggregated with the static networks of staff and passengers.Finally,the complex dynamical network of contacts between individuals was generated.Based on the artificial society,we conducted large-scale computational experiments to study the spread characteristics of COVID-19 in an airport and to investigate the effects of different intervention strategies.Learned from the reproduction of the outbreak,it is found that the increase in cumulative incidence exhibits a linear growth mode,different from that(an exponential growth mode)in a static network.In terms of mitigation measures,promoting unmanned security checks and boarding in an airport is recommended,as to reduce contact behaviors between individuals and staff.

Complexity Science and Cyber Operations: A Literature Survey

Complexity science is an interdisciplinary scientific field that analyzes systems as holistic entities consisting of characteristics beyond the sum of a system’s individual elements.This paper presents current research across the literature promoting cyber security as a complex adaptive system.We introduce complex systems concepts and fields of study,and deliver historical context,main themes,and current research relevant to cyber operations.Examples of cyber operations research leveraging agent-based modeling demonstrate the power of computational modeling grounded in complex systems principles.We discuss cyber operations as a scientific field,define current shortfalls for scientific rigor,and provide examples of how a complexity science foundation can further research and practice across a variety of cyber-based efforts.We propose standard definitions applicable to complex systems for cyber professionals and conclude with recommendations for future cyber operations research.

Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Harmony Search(HS)algorithm is highly effective in solving a wide range of real-world engineering optimization problems.However,it still has the problems such as being prone to local optima,low optimization accuracy,and low search efficiency.To address the limitations of the HS algorithm,a novel approach called the Dual-Memory Dynamic Search Harmony Search(DMDS-HS)algorithm is introduced.The main innovations of this algorithm are as follows:Firstly,a dual-memory structure is introduced to rank and hierarchically organize the harmonies in the harmony memory,creating an effective and selectable trust region to reduce approach blind searching.Furthermore,the trust region is dynamically adjusted to improve the convergence of the algorithm while maintaining its global search capability.Secondly,to boost the algorithm’s convergence speed,a phased dynamic convergence domain concept is introduced to strategically devise a global random search strategy.Lastly,the algorithm constructs an adaptive parameter adjustment strategy to adjust the usage probability of the algorithm’s search strategies,which aim to rationalize the abilities of exploration and exploitation of the algorithm.The results tested on the Computational Experiment Competition on 2017(CEC2017)test function set show that DMDS-HS outperforms the other nine HS algorithms and the other four state-of-the-art algorithms in terms of diversity,freedom from local optima,and solution accuracy.In addition,applying DMDS-HS to data clustering problems,the results show that it exhibits clustering performance that exceeds the other seven classical clustering algorithms,which verifies the effectiveness and reliability of DMDS-HS in solving complex data clustering problems.

Modeling and Simulation of Cooperative Exploration Strategies in Unknown Environments

Cooperative spatial exploration in initially unknown surroundings is a common embodied task in various applications and requires satisfactory coordination among the agents.Unlike many other research questions,there is a lack of simulation platforms for the cooperative exploration problem to perform and statistically evaluate different methods before they are deployed in practical scenarios.To this end,this paper designs a simulation framework to run different models,which features efficient event scheduling and data sharing.On top of such a framework,we propose and implement two different cooperative exploration strategies,i.e.,the synchronous and asynchronous ones.While the coordination in the former approach is conducted after gathering the perceptive information from all agents in each round,the latter enables an ad-hoc coordination.Accordingly,they exploit different principles for assigning target points for the agents.Extensive experiments on different types of environments and settings not only validate the scheduling efficiency of our simulation engine,but also demonstrate the respective advantages of the two strategies on different metrics.

Evolutionary Experience-Driven Particle Swarm Optimization with Dynamic Searching

Particle swarm optimization(PSO)algorithms have been successfully used for various complex optimization problems.However,balancing the diversity and convergence is still a problem that requires continuous research.Therefore,an evolutionary experience-driven particle swarm optimization with dynamic searching(EEDSPSO)is proposed in this paper.For purpose of extracting the effective information during population evolution,an adaptive framework of evolutionary experience is presented.And based on this framework,an experience-based neighborhood topology adjustment(ENT)is used to control the size of the neighborhood range,thereby effectively keeping the diversity of population.Meanwhile,experience-based elite archive mechanism(EEA)adjusts the weights of elite particles in the late evolutionary stage,thus enhancing the convergence of the algorithm.In addition,a Gaussian crisscross learning strategy(GCL)adopts cross-learning method to further balance the diversity and convergence.Finally,extensive experiments use the CEC2013 and CEC2017.The experiment results show that EEDSPSO outperforms current excellent PSO variants.

Intelligent Optimization Under Multiple Factories: Hybrid Flow Shop Scheduling Problem with Blocking Constraints Using an Advanced Iterated Greedy Algorithm

The distributed hybrid flow shop scheduling problem(DHFSP),which integrates distributed manufacturing models with parallel machines,has gained significant attention.However,in actual scheduling,some adjacent machines do not have buffers between them,resulting in blocking.This paper focuses on addressing the DHFSP with blocking constraints(DBHFSP)based on the actual production conditions.To solve DBHFSP,we construct a mixed integer linear programming(MILP)model for DBHFSP and validate its correctness using the Gurobi solver.Then,an advanced iterated greedy(AIG)algorithm is designed to minimize the makespan,in which we modify the Nawaz,Enscore,and Ham(NEH)heuristic to solve blocking constraints.To balance the global and local search capabilities of AIG,two effective inter-factory neighborhood search strategies and a swap-based local search strategy are designed.Additionally,each factory is mutually independent,and the movement within one factory does not affect the others.In view of this,we specifically designed a memory-based decoding method for insertion operations to reduce the computation time of the objective.Finally,two shaking strategies are incorporated into the algorithm to mitigate premature convergence.Five advanced algorithms are used to conduct comparative experiments with AIG on 80 test instances,and experimental results illustrate that the makespan and the relative percentage increase(RPI)obtained by AIG are 1.0%and 86.1%,respectively,better than the comparative algorithms.

Towards Efficient Robotic Software Development by Reusing Behavior Tree Structures for Task Planning Paradigms

interaction pipelines while maintaining interfaces for task-specific customization.The Structural-BT framework supports the modular design of structure functionalities and allows easy extensibility of the inner planning flows between BT components.With the Structural-BT framework,software engineers can develop robotic software by flexibly composing BT structures to formulate the skeleton software architecture and implement task-specific algorithms when necessary.In the experiment,this paper develops robotic software for diverse task scenarios and selects the baseline approaches of Robot Operating System(ROS)and classical BT development frameworks for comparison.By quantitatively measuring the reuse frequencies and ratios of BT structures,the Structural-BT framework has been shown to be more efficient than the baseline approaches for robotic software development.

Modeling Common-Mode Current Due to Asymmetric Aging of Machine Winding Insulation

Machine stator winding insulation degradation is one of the main results of machine aging.It is non-negligible once this degradation process becomes asymmetric between phases.The traditional way to determine the insulation state of health is a partial discharge test.However,this method requires the system offline,which causes production loss and extra administrative burden.This paper presents an idea for better characterizing the insulation machine’s state of health using common-mode(CM)behavior in the machine-drive system.With the help of circuit decomposition methods and modeling tools,the CM quantities due to asymmetric aging show a unique characteristic that distinguishes itself from other differential-mode(DM)quantities in the equivalent circuit.It is shown effective to represent the asymmetric aging effect from the detection of system leakage current.This paper provides an analytical method to quantify this characteristic from mathematical approaches,and a proper approximation has been made on the CM equivalent model(CEM)such that the CM behavior is accurately characterized.The proposed method will serve the purpose of predicting machine abnormal behavior using the simple RLC circuit.Researchers can adapt this method to quantify and characterize the machine insulation state of health(SOH).

Simulating Temporally and Spatially Correlated Wind Speed Time Series by Spectral Representation Method

In this paper,it aims to model wind speed time series at multiple sites.The five-parameter Johnson mdistribution is deployed to relate the wind speed at each site to a Gaussian time series,and the resultant-Z(t)dimensional Gaussian stochastic vector process is employed to model the temporal-spatial correlation of mwind speeds at different sites.In general,it is computationally tedious to obtain the autocorrelation functions Z(t)(ACFs)and cross-correlation functions(CCFs)of Z(t),which are different to those of wind speed times series.In order to circumvent this correlation distortion problem,the rank ACF and rank CCF are introduced to Z(t)characterize the temporal-spatial correlation of wind speeds,whereby the ACFs and CCFs of can be analytically obtained.Then,Fourier transformation is implemented to establish the cross-spectral density matrix Z(t)mof,and an analytical approach is proposed to generate samples of wind speeds at different sites.Finally,simulation experiments are performed to check the proposed methods,and the results verify that the five-parameter Johnson distribution can accurately match distribution functions of wind speeds,and the spectral representation method can well reproduce the temporal-spatial correlation of wind speeds.

Multi-Agent System for Electric Vehicle Charging Scheduling in Parking Lots

As the number of electric vehicles(EVs)increases,massive numbers of EVs have started to gather in commercial parking lots to charge and discharge,which may significantly impact the operation of the grid.There may also be a deviation in the departure time of charged and discharged EVs in commercial parking lots.This deviation can lead to insufficient battery energy when the EVs leave the parking lot.This study uses the simulation software AnyLogic to build a commercial parking lot multi-agent simulation model,and the agent-based model can fully reflect the autonomy of individual EVs.Based on this simulation model,we propose an EV scheduling algorithm.The algorithm contains two main agents.The first is the power distribution center agent(PDCA),which is used to coordinate the energy output of photovoltaic(PV),energy storage system(ESS),and distribution station(DS)to solve the problem of grid overload.The second is the scheduling center agent(SCA),which is used to solve the insufficient battery energy problem due to EVs’random departures.The SCA includes two stages.In the first stage,a priority scheduling algorithm is proposed to emphasize the fairness of EV charging.In the second stage,a genetic algorithm is used to accurately determine the time interval between charging and discharging to ensure the maximum benefit of EV owner.Finally,simulation experiments are conducted in AnyLogic,and the results demonstrate the superiority of the algorithm over the classical algorithm.

Quantity Flexibility Contract Model for Emergency Procurement Considering Supply Disruption

Supply chain disruption risk usually poses a serious challenge to the management of emergency supplies procurement between the government and enterprises in cooperation.To research the impact of supply chain disruption on the supply and demand sides of emergency supplies for disaster relief,the emergency procurement model based on quantity flexibility contract is constructed.The model introduces a stockout disruption to measure the degree of supply chain disruption and uses per unit of material relief value to quantify government disaster relief benefits.Further,it analyzes the basic pricing strategy and the agreed order quantity between the government and enterprises,focusing on the negative impact of supply disruption on the government and enterprises.The model deduction and data analysis results show that supply disruption creates a“lose-lose”situation for governments and enterprises,reducing their benefits and willingness to cooperate.Finally,a sensitivity analysis is conducted on the case data to explain the decision-making changes in the contract price and flexibility parameters between the government and enterprises before and after the supply disruption.

Gaussian Process Based Modeling and Control of Affine Systems with Control Saturation Constraints

Model-based methods require an accurate dynamic model to design the controller.However,the hydraulic parameters of nonlinear systems,complex friction,or actuator dynamics make it challenging to obtain accurate models.In this case,using the input-output data of the system to learn a dynamic model is an alternative approach.Therefore,we propose a dynamic model based on the Gaussian process(GP)to construct systems with control constraints.Since GP provides a measure of model confidence,it can deal with uncertainty.Unfortunately,most GP-based literature considers model uncertainty but does not consider the effect of constraints on inputs in closed-loop systems.An auxiliary system is developed to deal with the influence of the saturation constraints of input.Meanwhile,we relax the nonsingular assumption of the control coefficients to construct the controller.Some numerical results verify the rationality of the proposed approach and compare it with similar methods.

Digital Twin Implementation of Autonomous Planning Arc Welding Robot System

Industrial robots are currently applied for ship sub-assembly welding to replace welding workers because of the intelligent production and cost savings.In order to improve the efficiency of the robot system,a digital twin system of welding path planning for the arc welding robot in ship sub-assembly welding is proposed in this manuscript to achieve autonomous planning and generation of the welding path.First,a five-dimensional digital twin model of the dual arc welding robot system is constructed.Then,the system kinematics analysis and calibration are studied for communication realization between the virtual and the actual system.Besides,a topology consisting of three bounding volume hierarchies(BVH)trees is proposed to construct digital twin virtual entities in this system.Based on this topology,algorithms for welding seam extraction and collision detection are presented.Finally,the genetic algorithm and the RRT-Connect algorithm combined with region partitioning(RRT-Connect-RP)are applied for the welding sequence global planning and local jump path planning,respectively.The digital twin system and its path planning application are tested in the actual application scenario.The results show that the system can not only simulate the actual welding operation of the arc welding robot but also realize path planning and real-time control of the robot.