Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Exact and heuristic formulae to compute the geodetic height from the ellipse equation

The conversion of the cartesian coordinates of a point to its geodetic equivalent coordinates in reference to the geodetic ellipsoid is one of the main challenges in geodesy.The ellipse equation in the meridian plane significantly influences the value of the geodetic coordinates.This research analyzes this influence and how it can contribute to their solutions.The study investigates the mathematical relation between them and presents an exact formula relating to the geodetic height and the ellipse equation.In addition,a heuristic formula for the relation between the geodetic height and the ellipse equation is proposed,which is independent of the geodetic latitude and has a relative accuracy better than 99.9 %.The calculation is stable,and the cost is low.

Research on the Generation Path of Heuristic Teaching Language Based on Positive Psychology Theory

As the essence of traditional Chinese educational thought,heuristic teaching has gradually enriched and devel-oped its ideas through the continuous efforts of educational researchers of the past dynasties.In front-line teach-ing,heuristic teaching,as a teaching principle that can help teachers and students to interact and learn,undoubtedly plays an important role in students’acquisition of knowledge and scientific thinking activities.But teachers’understanding of heuristic teaching is not the same.In actual teaching,there are obvious gaps in language ability among different teachers.This research aims to enrich the heuristic teaching theory based on the perspective of psychology,and provide a certain theoretical basis for teacher training.Through the research and analysis of the generation path of the heuristic teaching language,this paper helps more teachers realize the advantages of the heuristic teaching language,improve their own heuristic teaching language level,and provide reference for the scientific teaching evaluation standard.We apply the heuristic instruction language approach proposed in this study and apply it to real classrooms.In the application,it can be clearly found that the students’thinking and participation have been strengthened,and the teaching effect has been significantly improved com-pared with the past,which indicates that the heuristic teaching language generation path in this study has certain practical application value.

Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm(OCA) with that of a heuristic rules-based algorithm(HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that(i) capacity under the HRA(denoted as C_(H)) is smaller than capacity under the OCA;(ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA;(iii) when the inflow rate is smaller than C_(H), the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost;(iv) when the inflow rate is larger than C_(H), the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.

Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches——A Systematic Literature Review and Mapping Study

The exponential use of artificial intelligence(AI)to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed.While AI is a powerfulmeans to discover interesting patterns and obtain predictive models,the use of these algorithms comes with a great responsibility,as an incomplete or unbalanced set of training data or an unproper interpretation of the models’outcomes could result in misleading conclusions that ultimately could become very dangerous.For these reasons,it is important to rely on expert knowledge when applying these methods.However,not every user can count on this specific expertise;non-AIexpert users could also benefit from applying these powerful algorithms to their domain problems,but they need basic guidelines to obtain themost out of AI models.The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features.The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering.As a result,9 papers that tackle AI algorithmrecommendation through tangible and traceable rules and heuristics were collected.The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms.

SA-MSVM:Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics.Bigdata is created from social websites like Facebook,WhatsApp,Twitter,etc.Opinions about products,persons,initiatives,political issues,research achievements,and entertainment are discussed on social websites.The unique data analytics method cannot be applied to various social websites since the data formats are different.Several approaches,techniques,and tools have been used for big data analytics,opinion mining,or sentiment analysis,but the accuracy is yet to be improved.The proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine(SA-MSVM)approach.SA-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing(NLP)process applied on tweets extracted from the Twitter dataset.A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers criticize.SA-MSVM is implemented,experimented with MATLAB,and the results are verified.The results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine(SVM)approach.SA-MSVM has obtained 96.34%accuracy in classifying the product review compared with the existing systems.

Application of Heuristic Method to the Clinical Teaching of Internal Medicine

There is an old saying,“Give a man a fish,and he will eat for a day.Teach a man to fish,and he will eat for the rest of his life.”In clinical teaching,students should not only be taught about diseases,but their intelligence should also be cultivated,along with their analytical,comprehension,and independent learning skills.The ability to solve problems enables students to think independently and acquire knowledge.This is known as the heuristic method of teaching.In this study,we mainly analyze the application value of the heuristic method in the clinical teaching of internal medicine.

HEURISTIC PARTICLE SWARM OPTIMIZATION ALGORITHM FOR AIR COMBAT DECISION-MAKING ON CMTA

Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.

模糊时间预测系统的Heuristic模型的改进

Song和Chissom首先提出了模糊时间系统的定义、时间的不变量和许多应用模糊时间系统的例子 .其中 ,对Alabama校入学人数的预测是应用模糊时间系统最好的例子 .但在预测过程中对Heuristic的指标有待研究 .针对Heuristic的指标进行改进 ,引入了新的α 指标和Heuristic2 函数 ,使预测更符合人们的正常思维方式 ,更有机会贴近实际 ,从而争取客观地做出更好的预测结果 .

Resource Allocation Using Timed Petri Nets and Heuristic Search

Traditional models for project management have not adequately incorporated a number of factors that are important for resource allocation. This paper proposed a unified timed Petri net model in which scheduling and planning were collectively carried out to take full advantages of the flexibility of the FMS. Through the lens of system theory, two types of resources were distinguished: major role and auxiliary role, and the major role was used to construct the FMS' Petri net. The method simplified the Petri net's construction and gave a clear flow chart for scheduling. Hence, the auxiliary resource allocation could be easily carried out according to the schedule, which was proposed by heuristic search algorithm. At last, the efficacy of the Petri net model for online scheduling in a resource constrained environment was discussed.

用于编制热轧生产流程的新型Meta-heuristic算法

带钢热轧具有特殊的生产工艺约束,其生产流程的编制是钢铁企业生产的关键,因此提出采用并行策略的基于多旅行商问题(MTSP)热轧轧制模型.该模型不但考虑了板坯在宽度、厚度和硬度跳变时的约束,还考虑了同一轧制单元内轧制板坯数量的约束.并设计了新的Meta-heuristics算法求解此模型.通过对某热轧带钢厂生产数据的仿真实验,表明模型和算法能有效地给出满意的排产结果,并且具有较高的执行效率.

Data-Driven Heuristic Assisted Memetic Algorithm for Efficient Inter-Satellite Link Scheduling in the BeiDou Navigation Satellite System

Inter-satellite link(ISL)scheduling is required by the BeiDou Navigation Satellite System(BDS)to guarantee the system ranging and communication performance.In the BDS,a great number of ISL scheduling instances must be addressed every day,which will certainly spend a lot of time via normal metaheuristics and hardly meet the quick-response requirements that often occur in real-world applications.To address the dual requirements of normal and quick-response ISL schedulings,a data-driven heuristic assisted memetic algorithm(DHMA)is proposed in this paper,which includes a high-performance memetic algorithm(MA)and a data-driven heuristic.In normal situations,the high-performance MA that hybridizes parallelism,competition,and evolution strategies is performed for high-quality ISL scheduling solutions over time.When in quick-response situations,the data-driven heuristic is performed to quickly schedule high-probability ISLs according to a prediction model,which is trained from the high-quality MA solutions.The main idea of the DHMA is to address normal and quick-response schedulings separately,while high-quality normal scheduling data are trained for quick-response use.In addition,this paper also presents an easy-to-understand ISL scheduling model and its NP-completeness.A seven-day experimental study with 10080 one-minute ISL scheduling instances shows the efficient performance of the DHMA in addressing the ISL scheduling in normal(in 84 hours)and quick-response(in 0.62 hour)situations,which can well meet the dual scheduling requirements in real-world BDS applications.

HEURISTIC STUDY OF FLOWSHOP SCHEDULING TO MINIMIZE MEAN FLOW TIME WITH LOT TRANSFER CONSIDERED

Lot scheduling problem with idle time transfer between processes to minimize mean flow time is very important because to minimize mean flow time is to minimize work in process. But the problem is NP hard and no polynomial algorithm exists to guarantee optimal solution. Based the analysis the mathematical structure of the problem, the paper presents a new heuristic algorithm. Computer simulation shows that the proposed heuristic algorithm performs well in terms of both quality of solution and execution speed.

A new heuristic for task scheduling in heterogeneous computing environment

Heterogeneous computing (HC) environment utilizes diverse resources with different computational capabilities to solve computing-intensive applications having diverse computational requirements and constraints. The task assignment problem in HC environment can be formally defined as for a given set of tasks and machines, assigning tasks to machines to achieve the minimum makespan. In this paper we propose a new task scheduling heuristic, high standard deviation first (HSTDF), which considers the standard deviation of the expected execution time of a task as a selection criterion. Standard deviation of the ex- pected execution time of a task represents the amount of variation in task execution time on different machines. Our conclusion is that tasks having high standard deviation must be assigned first for scheduling. A large number of experiments were carried out to check the effectiveness of the proposed heuristic in different scenarios, and the comparison with the existing heuristics (Max-min, Sufferage, Segmented Min-average, Segmented Min-min, and Segmented Max-min) clearly reveals that the proposed heuristic outperforms all existing heuristics in terms of average makespan.

Aheuristic Algorithm for Berth Scheduling Problem in Container Ports

In this paper,the berth scheduling problem is transformed into a special two-dimensional packing problem with some constraints.A nonlinear programming model for the problem is established,and a heuristic algorithm is proposed to solve the model.Simulation results show that the algorithm can improve the utilization of berths on discrete berth scheduling in the container port.

An Investigation of Confucian Heuristic Teaching Elicitation Teaching Theory Theory and Socrates

Confucianism （especially its teaching theory） is the core of Chinese traditional culture with Confucius as its representative. It has played an indispensable role in the development of Chinese culture. It is the spiritual pillar of Chinese sustainable development. There are no others that can compare with its teaching method and art. The famous ＂heuristic teaching theory＂ is one of its greatest inventions. But many people think that it is Socrates invention. In fact, Socrates ＂elicitation teaching theory＂ was invented far later than that of Confucius. This paper aims at investigating of these two theories.

The 0/1 Multidimensional Knapsack Problem and Its Variants: A Survey of Practical Models and Heuristic Approaches

The 0/1 Multidimensional Knapsack Problem (0/1 MKP) is an interesting NP-hard combinatorial optimization problem that can model a number of challenging applications in logistics, finance, telecommunications and other fields. In the 0/1 MKP, a set of items is given, each with a size and value, which has to be placed into a knapsack that has a certain number of dimensions having each a limited capacity. The goal is to find a subset of items leading to the maximum total profit while respecting the capacity constraints. Even though the 0/1 MKP is well studied in the literature, we can just find a little number of recent review papers on this problem. Furthermore, the existing reviews focus particularly on some specific issues. This paper aims to give a general and comprehensive survey of the considered problem so that it can be useful for both researchers and practitioners. Indeed, we first describe the 0/1 MKP and its relevant variants. Then, we present the detailed models of some important real-world applications of this problem. Moreover, an important collection of recently published heuristics and metaheuristics is categorized and briefly reviewed. These approaches are then quantitatively compared through some indicative statistics. Finally, some synthetic remarks and research directions are highlighted in the conclusion.

Path Planning for Intelligent Robots Based on Deep Q-learning With Experience Replay and Heuristic Knowledge

Path planning and obstacle avoidance are two challenging problems in the study of intelligent robots. In this paper, we develop a new method to alleviate these problems based on deep Q-learning with experience replay and heuristic knowledge. In this method, a neural network has been used to resolve the "curse of dimensionality" issue of the Q-table in reinforcement learning. When a robot is walking in an unknown environment, it collects experience data which is used for training a neural network;such a process is called experience replay.Heuristic knowledge helps the robot avoid blind exploration and provides more effective data for training the neural network. The simulation results show that in comparison with the existing methods, our method can converge to an optimal action strategy with less time and can explore a path in an unknown environment with fewer steps and larger average reward.

Heuristic Solutions of Virtual Network Embedding： A Survey

Network virtualization(NV) is considered as an enabling tool to remove the gradual ossification of current Internet. In the network virtualization environment, a set of heterogeneous virtual networks(VNs), isolated from each other, share the underlying resources of one or multiple substrate networks(SNs) according to the resource allocation strategy. This kind of resource allocation strategy is commonly known as so called Virtual Network Embedding(VNE) algorithm in network virtualization. Owing to the common sense that VNE problem is NP-hard in nature, most of VNE algorithms proposed in the literature are heuristic. This paper surveys and analyzes a number of representative heuristic solutions in the literature. Apart from the analysis of representative heuristic solutions, a taxonomy of the heuristic solutions is also presented in the form of table. Future research directions of VNE, especially for the heuristics, are emphasized and highlighted at the end of this survey.

A metaheuristic method for the multireturn-to-depot petrol truck routing problem with time windows

The petrol truck routing problem is an important part of the petrol supply chain.This study focuses on determining routes for distributing petrol products from a depot to petrol stations with the objective of minimizing the total travel cost and the fixed cost required to use the trucks.We propose a mathematical model that considers petrol trucks returning to a depot multiple times and develop a heuristic algorithm based on a local branch-and-bound search with a tabu list and the Metropolis acceptance criterion.In addition,an approach that accelerates the solution process by adding several valid inequalities is presented.In this study,the trucks are homogeneous and have two compartments,and each truck can execute at most three tasks daily.The sales company arranges the transfer amount and the time windows for each station.The performance of the proposed algorithm is evaluated by comparing its results with the optimal results.In addition,a real-world case of routing petrol trucks in Beijing is studied to demonstrate the effectiveness of the proposed approach.