AGV Scheduling Optimization of Automated Port Based on Disruption Management

The recent rapid development of China’s foreign trade has led to the significant increase in waterway transportation and automated container ports. Automated terminals can significantly improve the loading and unloading efficiency of container terminals. These terminals can also increase the port’s transportation volume while ensuring the quality of cargo loading and unloading, which has become an inevitable trend in the future development of ports. However, the continuous growth of the port’s transportation volume has increased the horizontal transportation pressure on the automated terminal, and the problems of route conflicts and road locks faced by automated guided vehicles (AGV) have become increasingly prominent. Accordingly, this work takes Xiamen Yuanhai automated container terminal as an example. This work focuses on analyzing the interference problem of path conflict in its horizontal transportation AGV scheduling. Results show that path conflict, the most prominent interference factor, will cause AGV scheduling to be unable to execute the original plan. Consequently, the disruption management was used to establish a disturbance recovery model, and the Dijkstra algorithm for combining with time windows is adopted to plan a conflict-free path. Based on the comparison with the rescheduling method, the research obtains that the deviation of the transportation path and the deviation degree of the transportation path under the disruption management method are much lower than those of the rescheduling method. The transportation path deviation degree of the disruption management method is only 5.56%. Meanwhile, the deviation degree of the transportation path under the rescheduling method is 44.44%.

Manual Order Picking Route Optimization in Distribution Warehouse of Chain Furniture Retail Enterprise

Due to the effects of the COVID-19 pandemic and the rise of online shopping, the offline sales of IKEA Fuzhou have been declining since 2020. Because the cost of distribution warehouse is a major expense for offline chain furniture retailers, and the picking process is a key activity in distribution warehouse operations. To reduce the cost of distribution warehouse and alleviate the survival pressure of the offline chain furniture retailers, this paper focuses on optimizing the picking route of the IKEA Fuzhou distribution warehouse. It starts by creating a two-dimensional coordinate system for the storage location of the distribution warehouse using the traditional S-type picking strategy to calculate the distance and time of the sorting route. Then, the problem of optimizing the picking route is then transformed into the travelling salesman problem (TSP), and picking route optimization model is developed using a genetic algorithm to analyze the sorting efficiency and picking route optimization. Results show that the order-picking route using the genetic algorithm strategy is significantly better than the traditional S-type picking strategy, which can improve overall sorting efficiency and operations, reduce costs, and increase efficiency. Thus, this establishes an implementation process for the order-picking path based on genetic algorithm optimization to improve overall sorting efficiency and operations, reduce costs, increase efficiency, and alleviate the survival pressure of pandemic-affected enterprises.

On Problems of Multicomponent System Maintenance Modelling

We present an overview of some recent developments in the area of mathematical modeling of maintenance decisions for multi-unit systems. The emphasis is on three main groups of multicomponent maintenance optimization models： the block replacement models, group maintenance models, and opportunistic maintenance models. Moreover, an example of a two-unit system maintenance process is provided in order to compare various maintenance policies.

Numerical Model of Internal Solitary Wave Evolution on ImpermeableVariable Seabed in A Stratified Two-Layer Fluid System

In the present study a numerical model developed by Lynett and Liu （2002） is modified to include density difference in a stratified two-layer fluid in a three-dimensional internal wave domain. The internal solitary wave （ISW） in the model is assumed to be weakly nonlinear and weakly dispersive, and the viscosity effects at all boundaries are ignored. The governing equations based on the Navier-Stokes and Euler equations are solved for internal solitary wave propagation over variable seabed topography. Theoretical formulations are established, from which analytical solutions are obtained, in addition to numerical results. Wave profiles from previous experimental studies are compared with the numerical results from the present analytical solutions. Numerical models developed on the basis of the present analytical solutions are better than those developed by Lynett and Liu （2002）. The results of numerical modeling agree well with the experimental data.

Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and privacy.Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT.In this view,this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine(ACOMKSVM)with Elliptical Curve cryptosystem(ECC)for secure and reliable IoT data sharing.This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data,collected from various data providers.Then,ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process.In this study,the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers,where IoT data is encrypted and recorded in a distributed ledger.The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts.To examine the performance of the proposed method,it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set(BCWD)and Heart Disease Data Set(HDD)from UCI AI repository.The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.

Autopilot Control Synthesis for Path Tracking Maneuvers of Underwater Vehicles

This paper is concerned with the robust control synthesis of autonomous underwater vehicle （AUV） for general path following maneuvers. First, we present maneuvering kinematics and vehicle dynamics in a unified framework. Based on H∞ loop-shaping procedure, the 2-DOF autopilot controller has been presented to enhance stability and path tracking. By use of model reduction, the high-order control system is reduced to one with reasonable order, and further the scaled low-order controller has been analyzed in both the frequency and the time domains. Finally, it is shown that the autopilot control system provides robust performance and stability against prescribed levels of uncertainty.

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.

Profile Evolution and Energy Dissipation for Internal SolitonTransmitting over Different Submarine Ridges

Fundamental experiments were carried out in a wave flume on internal solitary wave （ISW） of depression-type propagating over a submerged ridge. The seabed ridge included either triangular or semicircular shape - regarded as topographic obstacles. Influenced by the submarine ridge, the transmitted waves were found to always consist of a leading pulse （a solitary wave） followed by a dispersive wave train. The wave profile propagating over a triangular ridge was similar to that caused by a semicircular obstacle. Apparently, the smooth face of a semicircular ridge produced time lag of wave propagation. From experimental results available, the reduction in wave energy induced by a semicircular ridge was larger than that by a triangular one. The events of wave distortion, strong breaking, internal bolus, and stratification mixing happened in case that the crest of an ISW was great enough to interact with the topographic obstacle. The reduction in wave energy was induced by strong breaking, and it depended on the ridge height rather than the geometric shape of the ridge.

Port Logistics in West and Central Africa: A Strategic Development under Globalization

The main idea of this study is to provide help to West and Central Africa port logistics authorities in making, evaluating and realizing their decisions on port development/management, all in an effort to guaranty port efficiency and operational brilliance to profit the different ports, their hinterlands and their related clients. Consequently, an examination is made as far as port sector is concerned, with an ever more demanding enhancement in operational efficiency. Moreover, a past research on West and Central Africa Ports, as well as an overview on port logistics was made, making it possible to determine what has been done (prospects) and what is still to be done (ineffectiveness), in prospective of a more reformed and efficient port. One of the principles of this paper is also to confer all the aspects that operate as obstacles to the WCA port’s improvement and development;using thus the evaluation model called EWCA (Evaluation of West and Central Africa Ports), which gives a descriptive analysis of the present situation of the latter’s port logistics, including the aid of multiple regression model to determine an impact of explanatory variables on the study probability. Applying the method of strategic development, this paper thus deals with exploring typical problems and quandaries of West and Central Africa ports. The findings suggested how to fight back or overcome the constrictions and strangulations;enabling West and Central Africa port logistics attain their desired strategic development aims, despite the ineffectiveness. Tremendous efforts were made, giving a vital shoot on African economy and development;even though there is still a lot to do, for the data and variables showed how the port logistics augment and descend frequently without a long period of stability.

Synthesis of Cu Nano-particle in Toluene Used for Conductive Ink with a Binder of Polyurethane

Copper nanoparticles with a size of about 150 nm were prepared in toluene using oleic acid as protecting agent. The nanoparticles were used to prepare conductive Cu ink with a polyurethane binder. Oleic acid was used to prevent the nanoparticles from oxidization and agglomeration. The prepared Cu nanoparticles were characterized by X-ray diffraction （XRD） and transmission electron microscopy （TEM）. The resistivity of the copper film on glass substrate that was prepared using Cu nanoparticle ink reached about 1.5× 10-4 2. cm-1 after it was annealed to 120 ~C. Both the nanoparticle ink and the films were characterized by XRD, fourier transform infrared （FT-IR）, and the thermogravimetry-differential scanning calorimetry instrument （TG- DSC）.

Accuracy Evaluation of A Diagnostic Test by Detecting Outliers and Influential Observations

Logit regression analysis is widely applied in scientific studies and laboratory experiments, where skewed observations on a data set are often encountered. A number of problems with this method, for example, oudiers and influential observations, can cause overdispersion when a model is fitted. In this study a systematic statistical approach, including the plotting of several indices is used to diagnose the lack-of-fit of a logistic regression model. The outliers and influential observations on data from laboratory experiments are then detected. Specifically we take account of the interaction of an internal sohtary wave （ISW） with an obstacle, i.e., an underwater ridge, and also analyze the effects of the ridge height, the lower layer water depth, and the potential energy on the amplitude-based transmission rate of the ISW. As concluded, the goodness-of-fit of the revised logit regression model is better than that of the model without this approach.

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

In current days,the domain of Internet of Things(IoT)and Wireless Sensor Networks(WSN)are combined for enhancing the sensor related data transmission in the forthcoming networking applications.Clustering and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT networks.In this view,this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing(EMOQoSCMR)Protocol for IoT assisted WSN.The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy,throughput,delay,and lifetime.The proposed model involves two stage processes namely clustering and routing.Firstly,the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering(CEROAC)technique to select an optimal set of cluster heads(CHs)and construct clusters.Besides,oppositional chaos game optimization based routing(OCGOR)technique is employed for the optimal set of routes in the IoT assisted WSN.The proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy,distance to sink node,etc.The proposed EMOQoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH,PSO-ECHS,E-OEERP,and iCSHS methods have resulted in a lesser NAN of 2,10,42,and 51 rounds.The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

Study on Vitalizing Port Operation at Ulsan Port： Focused on Main Port of Ulsan

Ulsan port is the hugest industry-oriented port in South Korea. It has the third highest traffic volume of cargo, and also it has the most liquid bulk in Korea. However, on account of thoughtless development, there are other cases like Ulsan port that liquid cargo and general cargo are simultaneously handled. The demurrage ratio takes up to 12.5% of probability on average. In 2010, it has less room at berths for liquid bulk than general cargo and it totally has 107% of processing ratio （throughput）. This indicates that Ulsan port concentrates on liquid cargo more than general cargo depending on the characteristics of the port. This research suggests the effects as alternatives for efficiency and effectivity at both ports, Ulsan port and Pohang port in the case that the general cargo handled in Ulsan port is relocated to Pohang port in terms of the characteristics of products which Pohang port and its hinterland the most focused on. Namely, we estimate volume of relocated volume suitable for relocation in assumption that volume handled in Ulsan port is relocated to Pohang port scenario by scenario, analyze the effects of relocated volume, and suggest alternatives for vitalizing Ulsan port.

Stability and stabilization of discrete T-S fuzzy time-delay system based on maximal overlapped-rules group

The problems of stability and stabilization for the discrete Takagi-Sugeno（T-S） fuzzy time-delay system are investigated.By constructing a discrete piecewise Lyapunov-Krasovskii function（PLKF） in each maximal overlapped-rules group（MORG）,a new sufficient stability condition for the open-loop discrete T-S fuzzy time-delay system is proposed and proved.Then the systematic design of the fuzzy controller is investigated via the parallel distributed compensation control scheme,and a new stabilization condition for the closed-loop discrete T-S fuzzy time-delay system is proposed.The above two sufficient conditions only require finding common matrices in each MORG.Compared with the common Lyapunov-Krasovskii function（CLKF） approach and the fuzzy Lyapunov-Krasovskii function（FLKF） approach,these proposed sufficient conditions can not only overcome the defect of finding common matrices in the whole feasible region but also largely reduce the number of linear matrix inequalities to be solved.Finally,simulation examples show that the proposed PLKF approach is effective.

Fuzzy Analysis and Evaluation of Public Transport Service Quality: A Case Study of Dar es Salaam City, Tanzania

Urban public transport plays a critical role in stimulating economic development of any nation since most of the revenues come from cities. The majority of the city dwellers of any country use public transport. The evaluation of public transport service quality provides a valuable feedback to commuter operators to ensure continuous improvement of level of service and to the government to take appropriate measures for enhancing the quality of public transport service. This paper analyses and evaluates service quality of Road Public Transport (RPT) (i.e. minibuses and buses) and Urban Rail Transport (URT) in Dar es Salaam City, Tanzania. Since service quality and its attributes are intangible and vague, a fuzzy evaluation model is developed and applied. The formulated model is composed of Fuzzy Entropy Method (FEM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The overall evaluation procedure is as follows: initially, an intensive literature search and experts’ opinions are employed to establish criteria for evaluating the service quality of public transport in Dar es Salaam City. The developed FEM is used to obtain criteria weight. Lastly, the formulated TOPSIS is used to provide an overall ranking of urban public transport service quality. The overall evaluation shows that urban rail transport outperforms road public transport in terms of service quality. Nevertheless, the urban rail transport service in Dar es Salaam City is currently not well developed as it is provided on very limited routes. Thus, the Tanzania government, the rail transport operator i.e. Tanzania Railway Limited (TRL) and the agency responsible for the provision of rail infrastructure i.e. Reli Assets Holding Company (RAHCO) are advised to design and employ Public-Private-Partnership (PPP) schemes i.e. concession contracts to invest more in rolling stocks, locomotives and rail wagons so that the rail transport service is available on many routes of the road public transport to bring fair competition between the two operators.

Response of underground pipeline through fault fracture zone to random ground motion

It is assumed that a pipeline is laid through a vertical fault fracture zone, and is excited by seismic ground motion modelled as stationary stochastic process. For horizontal incidence of waves, the cross-PSD （Power Spectral Density） function is developed using wave propagation theory, while for vertical incidence of waves the cross-PSD function is composed by auto-PSD model, coherence model and site response model. As the seismic input, the eross-PSD function is used to calculate the the axial and lateral seismic responses of underground pipeline through the fracture zone. The results show that the incident directions of seismic waves, width and soil property of the fracture zone have great influence on underground pipeline. It is suggested that the flexible joints with appropriate stiffness should be added into the pipeline near the interfaces between the fracture zone and the surrounded media.

Clique-transversal number of graphs whose clique-graphs are trees

Given a graph G, a subgraph C is called a clique of G if C is a complete subgraph of G maximal under inclusion and ｜C｜ ≥2. A clique-transversal set S of G is a set of vertices of G such that S meets all cliques of G. The clique-transversal number, denoted as τC（G）, is the minimum cardinality of a clique-transversal set in G. The clique-graph of G, denoted as K（G）, is the graph obtained by taking the cliques of G as vertices, and two vertices are adjacent if and only if the corresponding cliques in G have nonempty intersection. Let F be a class of graphs G such that F = {G｜ K（G） is a tree}. In this paper the graphs in F having independent clique-transversal sets are shown and thus τC（G）/｜G｜ ≤ 1/2 for all G ∈F.

Implications of the EU’s Inclusion of Maritime Transport in the Emissions Trading System for Shipping Companies

1.Introduction Maritime transport is the backbone of international trade.The amount of total international maritime trade in million tonnes loaded was 8408 in 2012 and had increased to 11076 by 2019,for an average annual increase of 3.12%.In early 2020,the world fleet contained 98140 ships of 100 gross tonnes and above with 2.06 million dead weight tonnage of capacity[1].The greenhouse gas(GHG)emissions from shipping activities are not negligible.According to the fourth GHG study commissioned by the International Maritime Organization(IMO),in 2018,global shipping emitted a total of 1056 million tonnes of carbon dioxide(CO_(2)),accounting for around 2.89%of global anthropogenic CO_(2) emissions[2].Due to the international nature of shipping,efforts to control CO_(2) emissions from ships are absent from the Kyoto Protocol and the Paris Agreement.In an attempt to phase out carbon emissions from shipping entirely,the IMO formulated a strategy to cut the total annual GHG emissions from shipping by at least 50%from their 2008 levels by 2050[3];however,no mandatory rules have been promulgated since the release of this strategy.

Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain.In an Internet of Things(IoT)environment,accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain.As an example,this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit(the Shatian pomelo)in a comparative study.The root means square error(RMSE)values of regression analysis,exponential smoothing,grey prediction,grey neural network,support vector regression(SVR),and long short-term memory(LSTM)neural network methods were 53.715,6.707,18.440,1.580,and 1.436,respectively.Among these,the mean square error(MSE)values of the grey neural network,SVR,and LSTM neural network methods were 2.4979,31.652,and 2.0618,respectively;and theirRvalues were 0.99905,0.94,and 0.94501,respectively.The results demonstrated that the RMSE of the deep learning model is generally lower than that of a traditional prediction model,and the prediction results are more accurate.The prediction performance of the grey neural network was shown to be superior to that of SVR,and LSTM neural network,based on the comparison of parameters.

Application of the Conditional Inverse Matrix Theory for Estimation of Origin-Destination Trip Matrices from Link Traffic Flows

This study focuses on estimating O-D （origin-destination） trip demand from link traffic flows. Equality relationship among link traffic flow, path flow, and O-D trip matrices are used to establish a linear equation system. Solution characteristics are analyzed based on the relationship between the rank of the link/path incidence matrix and column variables. And under the solution framework of conditional inverse matrices, a column exchange method and a path flow proportion method have been developed. Network testing results verify that the proposed methods yield good results.