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.

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.

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.

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.

Meta-heuristic算法研究进展

对模拟退火、遗传算法和禁忌搜索法等代表性 meta-heuristic算法在理论与应用方面的研究进行综述 ,探讨算法结构和研究体系上的统一性 ,并归纳指出其发展方向。

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.

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.

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 .

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.

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.

Heuristic Computing Methods for Contact Plan Design in the Spatial-Node-Based Internet of Everything

To satisfy the increasing demands of high-speed transmission, high-efficiency computing, and real-time communications in the high-dynamic and heterogeneous networks, the Contact Plan Design(CPD) has attracted continuous attention in recent years, especially for the spatial-node-based Internet of Everything(IoE). In this paper, we study the NP-hardness of contact scheduling and the attenuation of atmospheric precipitation in the spatial-node-based IoE. Two heuristic computing methods for contact plan design are proposed by comprehensively considering the time-varying topology, the intermittent connectivity, and the adaptive transmission in different weather conditions, which are named Contact Plan Design-Particle Swarm Optimization(CPD-PSO) and Contact Plan Design-Greedy algorithm with the Minimum Delivery Time(CPD-GMDT) separately. For the population-based algorithm, CPD-PSO not only solves the CPD problem with a limited-resource condition, but also dynamically adjusts the search scope to ensure the continuous searching capability of the algorithm. For the CPD-GMDT that makes CP decisions based on the current state, the algorithm uses the idea of greedy algorithm to schedule Satellite-Platform Links(SPLs) and Inter Satellite Links(ISLs) respectively using the strategies of optimal matching and load balancing. The simulation results show that the proposed CPD-PSO outperforms Contact Plan Design-Genetic Algorithm(CPD-GA) in terms of fitness and delivery time, and CPD-GMDT presents better overall delay than Fair Contact Plan(FCP).

Heuristic algorithm for RCPSP with the objective of minimizing activities' cost

Resource-constrained project scheduling problem（RCPSP） is an important problem in research on project management. But there has been little attention paid to the objective of minimizing activities＇ cost with the resource constraints that is a critical sub-problem in partner selection of construction supply chain management because the capacities of the renewable resources supplied by the partners will effect on the project scheduling. Its mathematic model is presented firstly, and analysis on the characteristic of the problem shows that the objective function is non-regular and the problem is NP-complete following which the basic idea for solution is clarified. Based on a definition of preposing activity cost matrix, a heuristic algorithm is brought forward. Analyses on the complexity of the heuristics and the result of numerical studies show that the heuristic algorithm is feasible and relatively effective.

Improved methods for scheduling flexible manufacturing systems based on Petri nets and heuristic search

This paper proposes and evaluates two improved Petri net （PN）-based hybrid search strategies and their applications to flexible manufacturing system （FMS） scheduling. The algorithms proposed in some previous papers, which combine PN simulation capabilities with A＊ heuristic search within the PN reachability graph,may not find an optimum solution even with an admissible heuristic function. To remedy the defects an improved heuristic search strategy is proposed, which adopts a different method for selecting the promising markings and reserves the admissibility of the algorithm. To speed up the search process, another algorithm is also proposed which invokes faster termination conditions and still guarantees that the solution found is optimum. The scheduling results are compared through a simple FMS between our algorithms and the previous methods. They are also applied and evaluated in a set of randomly-generated FMSs with such characteristics as multiple resources and alternative routes.

A Heuristic Algorithm for the Fabric Spreading and Cutting Problem in Apparel Factories

We study the fabric spreading and cutting problem in apparel factories.For the sake of saving the material costs,the cutting requirement should be met exactly without producing additional garment components.For reducing the production costs,the number of lays that corresponds to the frequency of using the cutting beds should be minimized.We propose an iterated greedy algorithm for solving the fabric spreading and cutting problem.This algorithm contains a constructive procedure and an improving loop.Firstly the constructive procedure creates a set of lays in sequence,and then the improving loop tries to pick each lay from the lay set and rearrange the remaining lays into a smaller lay set.The improving loop will run until it cannot obtain any smaller lay set or the time limit is due.The experiment results on 500 cases show that the proposed algorithm is effective and efficient.

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 Construction Heuristic for the Split Delivery Vehicle Routing Problem

The Split Delivery Vehicle Routing Problem (SDVRP) is a relaxation of the Capacitated Vehicle Routing Problem (CVRP) where customers may be assigned to multiple routes. A new construction heuristic is developed for the SDVRP and computational results are given for thirty-two data sets from previous literature. With respect to the total travel distance, the construction heuristic compares favorably versus a column generation method and a two-phase method. In addition, the construction heuristic is computationally faster than both previous methods. This construction heuristic could be useful in developing initial solutions, very quickly, for a heuristic, algorithm, or exact procedure.

A heuristic approach for petrochemical plant layout considering steam pipeline length

Plant layout design affects both investment and performance of a factory. To maximize the economic benefits of a petrochemical factory, a large number of factors must be considered simultaneously, such as material flow, heat flow and safety. However, conventional principles for plant layout design and optimization do not involve the heat flow, resulting in higher construction investment. To solve this problem, a new heuristic approach is proposed in this paper based on the current layout design principles. Both material flow(pipelines for process streams) and heat flow(pipelines for steam) are considered. Three optimization methods with different objective functions are used to optimize the layout. The application of proposed approach is illustrated with a case study. The optimal scheme and pipeline networks can be obtained, and the pipeline length is reduced significantly.

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 web service composition method based on domain ontology

In order to realize automatic web service composition, a heuristic web service composition method based on domain ontology is proposed. First, this method integrates the domain ontology and the artificial intelligence （AI） planning algorithm. Then, it uses the domain ontology and its reasoning capability to infer the semantic relationship among parameters. Finally, it transforms the web service composition problem into the planning problem based on the AI planning heuristic algorithm. The preliminary experimental results show that the above method compensates for the lack of semantics in the previous AI planning method and it can satisfy the requirements of quality and efficiency of composition, thus generating composite web services according to customer requirements automatically and efficiently.