Method of supply chain optimization in E-commerce

Rapid development of technologies and their penetration into all sectors generates a wide range of streamlining of production and trade processes. Electronic commerce is the area in which information and communication technology (ICT) is an essential and indispensable element. It is based on the use of e-commerce systems. An e-commerce system combines several parts consisting of customers, suppliers (sellers, dealers, producers, businessmen, etc.), the web server (web interface), the information system (ERP, CRM, the database system), the payment system, the dispatch system and the legislature itself. All these subsystems must be managed both at the operational level and in terms of the whole e-commerce system. E-commerce systems are tools meant to support the supply chain (SC), the quality of which as well as other parts of the e-commerce system largely depend on management processes representing supply chain management (SCM). The optimal way to ensure the success of SCM is to use the methods of modelling and simulation based on appropriate models and mathematical representation of a real SC. Such models are constructed with the use of process and value-chain oriented approaches or based on the concept of multi-agent systems. Different types of models in conjunction with a suitable mathematical representation allow us to perform the simulation process which outputs can help managers make suitable decisions. The paper aims at presenting contemporary approaches to the supply chain modelling within e-commerce systems. Moreover, the case study emphasized hereby is oriented to present the sample simulation approach in order to find the optimal allocation of resources which are meant to minimize shipping costs.e-commerce system, supply chain, supply chain management, warehouse, allocation of resources

Point process modeling of wildfire hazard in Los Angeles County, California

The Burning Index (BI) produced daily by the United States government's National Fire Danger Rating System is commonly used in forecasting the hazard of wildfire activity in the United States. However, recent evaluations have shown the BI to be less effective at predicting wildfires in Los Angeles County, compared to simple point process models incorporating similar meteorological information. Here, we explore the forecasting power of a suite of more complex point process models that use seasonal wildfire trends, daily and lagged weather variables, and historical spatial burn patterns as covariates, and that interpolate the records from different weather stations. Results are compared with models using only the BI. The performance of each model is compared by Akaike Information Criterion (AIC), as well as by the power in predicting wildfires in the historical data set and residual analysis. We find that multiplicative models that directly use weather variables offer substantial improvement in fit compared to models using only the BI, and, in particular, models where a distinct spatial bandwidth parameter is estimated for each weather station appear to offer substantially improved fit.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS401 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Development methodologies quality management iron products

The system of performance management quality steel products with the use of mathematical models with elements of fuzzy logic, which describes the real situation taking into account the incompleteness and vagueness of the original information was offered. As a measure of the uncertainty of technological information is an entropy criterion. It shows the use of entropy in multioperational process metallurgy

Mathematical modelling and control of a robotic manipulator : a thesis presented in partial fulfilment of the requirements of the degree of Masters of Technology at Massey University

Control system engineering strives to alter a systems performance to suit the objectives of the user. This requires pre-requisite knowledge of the system behaviour. This is often in the form of mathematical models of the system. These models can then be used to simulate the system and obtain a sound understanding of the systems operation, these can then be used in controller design. Every real world physical system has its own unique characteristics. These must be modelled to develop a simulation of the system. The uniqueness of a real world system necessitates the use of experimental practices and procedures to obtain information about the system. This information is then used to form models representing the system. A simulation of the system can then be based on these models. In this project a robotic system comprising of a link structure, a pneumatic driving system and a valve regulating system, is investigated. Mathematical models describing each component of the robotic system are investigated. Mathematical models describing the dynamic interactions of the link structure are developed and implemented in a fashion to facilitate control of the robot mechanism. The equations are in an explicit form which do not require the use of a numerical method for development of state space equations used in controller development. The pneumatic muscle used as the desired actuator for the robot structure is analysed. Analytical models obtained from the available literature are examined and new models are developed to describe the characteristics of the pneumatic muscle. A proto-type valve specially developed for supplying air to the pneumatic muscle is investigated. Experiments are conducted on this valve to characterise the valves behaviour. A model of the valves behaviour is then developed. A selection of controllers are then applied to the valve pneumatic muscle system. The investigation of alternative actuation systems proposes a new rotary pneumatic muscle design. Analytical models for the rotary pneumatic muscle are developed, a prototype is constructed as part of a feasibility study

Modelling Cell Cycle using Different Levels of Representation

Understanding the behaviour of biological systems requires a complex setting of in vitro and in vivo experiments, which attracts high costs in terms of time and resources. The use of mathematical models allows researchers to perform computerised simulations of biological systems, which are called in silico experiments, to attain important insights and predictions about the system behaviour with a considerably lower cost. Computer visualisation is an important part of this approach, since it provides a realistic representation of the system behaviour. We define a formal methodology to model biological systems using different levels of representation: a purely formal representation, which we call molecular level, models the biochemical dynamics of the system; visualisation-oriented representations, which we call visual levels, provide views of the biological system at a higher level of organisation and are equipped with the necessary spatial information to generate the appropriate visualisation. We choose Spatial CLS, a formal language belonging to the class of Calculi of Looping Sequences, as the formalism for modelling all representation levels. We illustrate our approach using the budding yeast cell cycle as a case study

User's guide to an early warning system for macroeconomic vulnerability in Latin American countries

The authors develop an early warning system for macroeconomic vulnerability for several Latin American countries, drawing on the work of Kaminsky, Lizondo, and Reinhart (1997) and Kaminsky (1988). They build a composite leading indicator that signals macroeconomic vulnerability, showing that, historically, crises tend to happen in certain"vulnerable"situations. Interested mainly in providing an operational tool, the authors use a different approach to the problem than Kaminsky did. First, they use fewer variables to generate the signals. Then, after the variables are aggregated, a signal is issued, depending on the behavior of the composite index. (Kaminsky's procedure was to generate signals with each variable and then aggregate them.) Their results are satisfactory both statistically and operationally. Statistically, Type I and II errors are smaller than those reported in previous papers. Operationally, this system of leading indicators is less costly to maintain, given fewer variables-which are widely available and reported with timeliness. The authors tested the models'out-of-sample predictive ability on crises that occurred after the first stage of their project was finished: Colombia (September 1998), Brazil (January 1999), and Ecuador (February 1999). In all cases the models correctly anticipated the speculative attacks. Moreover, Mexico's models, estimated with information available two years before the 1994 crisis, show that these signaling devices would have been useful for signaling the macroeconomic vulnerability before December 1994.Statistical&Mathematical Sciences,Economic Theory&Research,Environmental Economics&Policies,Educational Technology and Distance Education,Scientific Research&Science Parks,Environmental Economics&Policies,Economic Theory&Research,Educational Technology and Distance Education,Statistical&Mathematical Sciences,Geographical Information Systems

Comparison between two different cardiovascular models during a hemorrhagic shock scenario

Hemorrhagic shock is a form of hypovolemic shock determined by rapid and large loss of intravascular blood volume and represents the first cause of death in the world, whether on the battlefield or in civilian traumatology. For this, the ability to prevent hemorrhagic shock remains one of the greatest challenges in the medical and engineering fields. The use of mathematical models of the cardiocirculatory system has improved the capacity, on one hand, to predict the risk of hemorrhagic shock and, on the other, to determine efficient treatment strategies. In this paper, a comparison between two mathematical models that simulate several hemorrhagic scenarios is presented. The models considered are the Guyton and the Zenker model. In the vast panorama of existing cardiovascular mathematical models, we decided to compare these two models because they seem to be at the extremes as regards the complexity and the detail of information that they analyze. The Guyton model is a complex and highly structured model that represents a milestone in the study of the cardiovascular system; the Zenker model is a more recent one, developed in 2007, that is relatively simple and easy to implement. The comparison between the two models offers new prospects for the improvement of mathematical models of the cardiovascular system that may prove more effective in the study of hemorrhagic shock