The routing optimization of yard trailers in container terminal is an important issue for vehicle dispatching. Whether the vehicle dispatch is reasonable will determine the circulation efficiency of the container terminal. In order to better optimize the yard trailers routing under stochastic demand, a two-stage optimization model is established in this paper considered the constraint of vehicle capacity and travel distance, which aims at planning and determining the travel routing of the trailers and the operating sequence. To solve the two-stage optimization model, a particle swarm optimization algorithms (PSO) algorithm with exponentially decreasing weight strategy is introduced to search for a satisfied solution to ensure the feasibility of distribution plan and vehicle routing. And the optimal results of the simulation experimental reveal that satisfied solutions can be obtained by employing the two-stage optimization model constructed in this paper, which further verified the feasibility and validity of the optimization model and algorithm. The two-stage optimization model provides a practical and effective method to solve the trailers routing dispatching in container terminals under stochastic demand.

党的二十大报告中强调,我国需要着力提升产业链、供应链韧性和安全水平,着力推动经济实现质的有效提升和量的合理增长.这是因为过去企业之间的竞争焦点在于如何扩张销售规模、优化商品质量、降低生产成本,而今该竞争方式已逐步被供应链之间的竞争所取代.尤其是在电商领域,供应链的竞争力在很大程度上决定着电商企业的生存能力与盈利水平,不断加强供应链集成能力成为电商企业巩固自身竞争优势的重要途径.同时,电商企业的战略导向决定着其活动规则与日常运作,是影响绩效的重要因素,极有可能对供应链集成能力与电商企业绩效的关系产生调节效应.基于此,本文提出了供应链集成能力、战略导向与电商企业绩效三者关系的假设,并通过结构方程模型进行验证.本文将供应链集成能力用自身集成能力、消费者集成能力和商品集成能力进行衡量;将战略导向通过市场导向和技术导向进行衡量.研究结果表明:自身集成能力、消费者集成能力、商品集成能力均能够促进企业绩效;市场导向能够提升自身集成能力、消费者集成能力对企业绩效的促进作用,无法提升商品集成能力对企业绩效的促进作用;技术导向能够提升自身集成能力、商品集成能力对企业绩效的促进作用,无法提升消费者集成能力对企业绩效的促进作用.

This paper proposes a prediction-driven sequential optimization methodology for joint decision-making problems of production-sales-stock in refined oil enterprises. In the proposed prediction-driven sequential optimization methodology, three dynamic nonlinear programming models are first constructed to model the production-sales-stock decision-making problems in refined oil enterprises. Then, the analytical solutions to sequential optimization for production-sales-stock decision-making issues are presented by using the inverse inference method in dynamic programming. Finally, the impact of price and demand prediction of refined oil products on sequential optimization for production-sales-stock decision-making are analyzed using a numerical analysis method. Numerical results demonstrated the significant impact of forecasting results of price and demand of refined oil products on sequential optimization decision-making, indicating that the prediction-driven sequential optimization methodology can be used as an effective tool for joint decision-making of production-sales-stock.

In order to make reasonable production-sales-stock decision-making for gasoline production enterprises, it is necessary to make an accurate prediction of the gasoline demand. However, gasoline demand is often affected by many factors, which makes it very difficult to predict. Therefore, this paper tries to construct a trend- and periodicity-trait-driven decomposition-ensemble forecasting model in terms of trend and periodicity characteristics of gasoline demand data. In order to verify the effectiveness of the proposed model, the demand data of a typical gasoline product-93# gasoline in China, is used. The empirical results show that the proposed trend- and periodicity-trait-driven decomposition-ensemble forecasting model can achieve better prediction results than the single models, indicating that the proposed methodology can be used as a feasible solution to predict the gasoline demand series with trend and periodicity traits.

research-article Predict Risk Assessment in Financial Market System Based on Machine Learning Algorithm Share on Authors: Jiannan Yu Harbin Engineering University, China Harbin Engineering University, ChinaView Profile , Jinlou Zhao Harbin Engineering University, China Harbin Engineering University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 1998–2001https://doi.org/10.1145/3482632.3484085Online:22 November 2021Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Purpose Long-term contract is an important developing direction of China's coal industry coordination. This paper aims to discuss how to use contract for difference (CFD) to avoid risk and effectively increase the benefit of both coal and thermal power plants in the coal-electricity supply chain. Design/methodology/approach Based on prospect theory, this paper takes the risks and benefits of the coal and coal-fired power plants in the coal supply chain under CFD into balanced consideration to construct the contract coordination mechanism. In this mechanism, the coal demand in the coal supply chain equilibrium under centralized decision-making is regarded as the total annual volume of transactions needed to design the contract coordination mechanism and solve double marginalization. Then, based on prospect theory, in the construction of CFD, this paper takes the income of power and coal enterprises when they are in equilibrium under Stackelberg non-cooperative game as the reference point. In addition, considering that coal demand is a random variable, the CFD with a one-year trading session can be designed. Findings The research derives the coal price of the contract for difference, contract trading volume and its proportion of the total trading volume. A numerical example shows that the model above can be used to effectively avoid the risk of both coal and electricity sides. Originality/value To solve the conflict between coal enterprises and thermal power plants, let the coal-electricity supply chain be converted from non-cooperative game to cooperative game. Based on the prospect theory, this paper takes the income of the non-cooperative game of coal and thermal power plants as a reference point and considers how to design the coordination mechanism, the contract for difference, so as to make the two parties cooperate to solve the double marginal utility of the non-cooperative game in a chain supply. The main innovation of the work lies in the following: first, the coal demand when the coal-electrical supply chain is in balance under centralized decision-making is taken as the total annual trading volume needed to design the contract coordination mechanism and solve double marginalization. Second, based on prospect theory, in the construction of CFD, the benefits of coal-fired power plants and coal enterprises when both sides are in equilibrium under the Stackelberg non-cooperative game are taken as the reference points, and coal demand is taken as a random variable to design the CFD with a one-year transaction period. The price of coal that is not traded through CFD is calculated according to the daily market price. Third, this paper proposes the prospect M-V criterion of the risk-benefit equilibrium of both power and coal enterprises, which means that the risk-benefit equilibrium of both sides is the prospect variance effect of both sides relative to the reference point benefit divided by the prospect expectation effect.

With the rapid development of information technology, the value contained in big data has attracted more and more attention. Conducting efficient analysis of big data has become an important topic. Machine learning is one of the commonly used methods for data analysis. The traditional machine learning algorithm is often designed as the way of offline batch training. However, this method is not suitable for data sets with massive scale and continuous growth in the big data environment. Transforming the traditional machine learning algorithm so that it can better apply to the big data environment has become a research hotspot. With the rapid development of economy and society and the continuous progress of science and technology, the public's understanding of ocean functions is gradually deepened, the demand for marine products and services is increasing every day, and the economic and social benefits of the ocean are continuously rising. In this context, great importance should be given to the examination of marine resources and environment, the development and utilization of work, the constant adjusting of the ocean economic development policy, and the variety of comprehensive marine management measures to ensure the sustainable development of implementation of marine programs. The current mainstream of machine learning technology based on the marine economy of systemic risk early warning system for research is proposed here.

在两级物流服务供应链任务分配中,物流服务集成商在给功能型物流服务提供商分配任务的同时,常用奖金激励其更快完成任务.据此,本文提出了考虑时效激励机制的物流服务供应链任务分配设计方案.首先,考虑到物流订单任务量的随机性,建立了基于时效激励机制的客户满意度模型;其次,结合此模型构建了物流服务成本最小化模型;最后,采用NSGAⅡ算法,对两个目标规划模型进行求解,进而获得两级物流服务供应链任务分配方案.

It is well known that the interbank market is able to effectively provide financial liquidity for the entire banking system and maintain the stability of the financial market. In this paper, we develop an innovative complex network approach to simulate an interbank network with systemic risk contagion that takes into account the balance sheet of each bank, from which we can identify if the financial institutions have sufficient capital reserves to prevent risk contagion. Cascading defaults are also generated in the simulation according to different crisis-triggering (targeted defaults) methods. We also use machine learning techniques to identify the synthetic features of the network. Our analysis shows that the topological factors and market factors in the interbank network have significant impacts on the risk spreading. Overall, this paper provides a scientific method for policy-makers to select the optimal management policy for handling systemic risk.

为降低船舶的维修成本,本文构建了基于Gamma过程的船舶关键部件性能退化模型,并在此基础上制定了视情维修策略.本文以气缸套的磨损过程为例,对模型及维修策略的有效性进行了验证.结果表明:该模型能够很好地刻画船舶关键部件的性能退化规律;相比于计划维修策略,该视情维修策略能够显著地降低船舶关键部件的维修成本.所提出的模型和维修策略适用于单调非减的性能退化过程,并可被推广到除船舶外的其他高可靠、长寿命的离岸设备.

The recent empirical studies showed that money center networks in interbank markets are more robust and stable. Therefore, the research on layered financial networks is a key part of the systemic risk management. Various methods have been proposed in prior studies to find optimal partitioning of interbank networks into core and periphery subsets. However, these methods that have been adopted with approximation methods, in general, do not guarantee optimal bipartition. In this paper, a genetic simulated annealing algorithm is presented to detect a hierarchical structure in interbank networks as a hybrid heuristic algorithm, while its effects are also analyzed. The optimization of the error score for the core-periphery model is mathematically developed firstly as an improved expression of the optimization function, which incorporates the genetic algorithm into a simulated annealing algorithm to guarantee the optimal bipartition and to jump from a local optimization. The results of this algorithm are finally verified by empirical analysis of interbank networks; and, through the immunity strategy under the risk diffusion model, the significance of core-periphery structure to risk management is verified.

Enhancing environmental sustainability in maritime shipping has emerged as an important topic for both firms in shipping-related industries and policy makers. Speed optimization has been proven to be one of the most effective operational measures to achieve this goal, as fuel consumption and greenhouse gas (GHG) emissions of a ship are very sensitive to its sailing speed. Existing research on ship speed optimization does not differentiate speed through water (STW) from speed over ground (SOG) when formulating the fuel consumption function and the sailing time function. Aiming to fill this research gap, we propose a speed optimization model for a fixed ship route to minimize the total fuel consumption over the whole voyage, in which the influence of ocean currents is taken into account. As the difference between STW and SOG is mainly due to ocean currents, the proposed model is capable of distinguishing STW from SOG. Thus, in the proposed model, the ship's fuel consumption and sailing time can be determined with the correct speed. A case study on a real voyage for an oil products tanker shows that: (a) the average relative error between the estimated SOG and the measured SOG can be reduced from 4.75% to 1.36% across sailing segments, if the influence of ocean currents is taken into account, and (b) the proposed model can enable the selected oil products tanker to save 2.20% of bunker fuel and reduce 26.12 MT of CO2 emissions for a 280-h voyage. The proposed model can be used as a practical and robust decision support tool for voyage planners/managers to reduce the fuel consumption and GHG emissions of a ship.

为有效解决船舶分段的空间调度问题,提出了一种基于优先规则的求解算法.首先利用优先规则和禁忌搜索算法产生可行的分段调度序列,再采用一种启发式定位策略——最下最左填满策略对产生的调度序列进行解码,以评估调度序列的优劣.算法不断迭代,最终可得到近似最优解.对船厂的实际生产数据进行了实证分析,并与现有的算法进行了对比,验证了所提出的算法在空间调度问题上的有效性和优越性.

群体特征提取是发现特定知识群体偏好,进而提供个性化服务的基础.TFIDF是进行特征提取的常用方法,然而传统TFIDF方法却未考虑到类间集中度和类内分散度的问题.针对该情况文章引进了CD因子,提出了新的TFIDF算法,以读者借阅数据为基础开展了图书馆知识群体特征提取研究.并以某高校建筑与城市规划群体为例,采用传统和改进两种TFIDF方法对群体特征进行提取.实证表叽改进TFIDF方法效果更佳.

At present, the competition between enterprises is increasingly fierce. If the enterprise wants to obtain the competitive advantage, the innovation is an effective means; innovation is the basis of an enterprise's survival and development: all enterprises strive to improve their competitiveness through innovation; however, the effects of innovation diffusion will eventually restrict their development. This study, using a complex network approach, establishes a nonlinear technology innovation diffusion cascade model aimed at identifying the best seeds to foster the process of technology innovation diffusion by searching for the important nodes scattered throughout different communities to spread technology innovation and achieve the maximum diffusion effect. Through an analysis of simulated and real networks, we found that our method can both improve the speed of technology innovation diffusion and enhance the diffusion effect, which can provide advantages to enterprises and help promote innovation.

In order to enhance sustainability in maritime shipping, shipping companies spend good efforts in improving the operational energy efficiency of existing ships. Accurate fuel consumption prediction model is a prerequisite of such operational improvements. Existing grey-box models (GBMs) are found with significant performance potential for ship fuel consumption prediction, although having a limitation of separating weather directions. Aiming to overcome this limitation, we propose a novel genetic algorithm-based GBM (GA-based GBM), where ship fuel consumption is modelled in a procedure based on basic principles of ship propulsion and the unknown parameters in this model are estimated with a GA-based procedure. Real ship operation data from a crude oil tanker over a 7-year sailing period are used to demonstrate the accuracy and reliability of the proposed model. To highlight the contribution of this work, we compare the proposed model against the latest GBM. The results show that the fitting performance of the proposed model is remarkably better, especially for oblique weather directions. The proposed model can be employed as a basis of ship energy efficiency management programs to reduce fuel consumption and greenhouse gas (GHG) emissions of a ship. This is beneficial to achieve the goal of sustainable shipping.

银行间市场能够有效地提供整个银行系统的流动性,支撑金融市场的稳定运转.文章试图模拟一个银行间市场的网络去研究系统性风险的传染,并将网络中的每个银行的资产负债考虑进去.进而调查每家银行是否有足够的准备金作为缓冲,去防止系统性金融风险的蔓延.先基于复杂网络理论改进了一个银行间市场网络的模拟方法,并将其和每个银行的资产负债相结合去构建整个银行间市场;再在银行系统触发金融危机来模拟银行间网络的级联失效过程,以此研究如何调整准备金率以保证银行间市场的平稳,从而帮助政策制定者去掌握最好的系统性风险的管理政策.

In recent years, urban traffic congestion has become an important issue that besets the economic development of cities and brings inconvenience to people's productivity and lives. However, traffic congestion is inevitable due to certain factors, such as urban planning, population density, and traffic facilities. Thus, addressing traffic congestion, redistributing congestion load reasonably, and preventing large-scale cascade congestion offer practical research value. In this work, to solve urban traffic congestion, we set an initial load for every traffic network node and then determine whether the node fails or not according to the load threshold value. On the basis of the results, a nonlinear model for failure nodes' load redistribution is proposed. A condition in which the model triggers cascading failure is analyzed, and random and intentional attacks are executed on the Qiqihar urban traffic network. The cascading failures caused by the congestion nodes are simulated with MATLAB, and an understandable method of determining the destructive effect of the attacks is selected. Simulation results show that the model can achieve enhanced robustness against cascading failure by adjusting failure nodes' load redistribution reasonably, reducing the number of cascade failure nodes, and avoiding large-scale cascading failure. This study finds that intentional attacks are more destructive than random attacks when the capacity coefficient is small, but a supersized capacity coefficient is impossible in real life due to various constraints. Therefore, we need to properly adjust the redistribution coefficient of traffic load, redistribute congestion load, effectively reduce the number of failure nodes, and prevent large-scale cascading failure.