These concepts of Lagrangian and Hamiltonian Mechanics use the Kinetic and Potential Energy of the system. We first determine the equations of motion for the inverted double pendulum system using the concepts of Hamiltonian and Lagrangian Approach and then we determine the state space for the system and we simulate the system in MATLAB to observe the non-linear characteristics of the system. These observations can be used further to design an efficient control system for the inverted double pendulum system from the energy based approach. But this paper is restricted only to the study of the non-linearity of the system and simulation of the system in
This method can be very effective when dealing with new product or new technologies 2. Time series: Time series forecasting method is done by using historical demand information. The basic concept of the time series method is that the past demand is good indicator for doing forecast of future demand. This method is very effective for the products with steady demand and when the demand pattern does not vary that much from one year to the next year. These are one of the simplest methods to implement in practical field and can provide good starting point to do forecasting (Chopra & Meindl 2007, 186-190) (Emmett & Granville 2007,
CHAPTER 3 – Theoretical and Numerical Computational Solution of the Schrodinger Equation 3.1 Theoretical Solution The theoretical solution of the time independent and the time dependent Schrodinger equation is analysed. Solution to Time Dependent Schrodinger Equation: method of separation of variables  TDSE: EΨ(t,x)= (〖-ħ〗^2/2m d^2/(dx^2 ) + U(x))Ψ(t,x)-→ EΨ(t,x) = ĤΨ The potential energy in the Hamiltonian is time independent: U = U(x). Assuming: Ψ(t,x) = Ψ(x)f(t) So TDSE is re-written as: iħΨ(x) df(t)/dt = 〖-ħ〗^2/2m (d^2 Ψ(x))/(dx^2 ) f(t)+ UΨ(x)f(t) Multiply through by (1/Ψ(x)f(t)) to make the R.H.S exclusively t-dependent and the L.H.S exclusively x-dependent TDSE is re-written as: iħ 1/(f(t))
The forecasting process in many fashion industries is characterised by a number of features that can make it especially challenging. As highlighted by Thomassey (2014), these features include strong seasonal patterns, very short product life-cycles, coupled with a huge product variety, and short planning horizons (generally a few weeks) to manage replenishments of stocks at retail outlets. In addition, several exogenous variables, some of which cannot be directly controlled by manufacturing companies, can have a strong influence on sales. These include macro-economic conditions, marketing strategies, retailing strategies and fashion trends. As a result of these factors, coupled with the absence of long sales time-series for most products, the
Lifecycle Forecasting should be used since a new product will tend to have less predictable demand pattern than the existing product. Comparisons with the launch of the previous car and the use of casual modeling to anticipate sector demand is crucial. The use of probability is as important as the Lifecycle Forecasting. The launch of a new product will tend to have less predictable demand patterns than the existing product but the new product should generate an increase demand over a product it replaces. In order to reduce all these unknown factors and uncertainties comparison with the launch of previous similar cars is key.
Planning is essential for proper and effective management, and forecasting is an important subset of the planning function (Choi, 1999). Rahmlow and Klimberg (2002) identified some of the most important decision areas as well as the impact that forecasting has on these areas within an organization, the results are displayed in Table 1. In the food retail industry, a major contributing factor to the successful operation and optimal stock management is forecasting (Arunraj and Ahrens, 2015). Kokkinou (2013) states that, as restaurant operators deal with highly perishable products, overestimation of sales can lead to unnecessary labor costs and stock wastage. Underestimation of sales can lead to unsatisfactory customer service and loss of revenue
SBD has to determine the current market and future demand levels of its products in order to determine its process design. Determining the market requirements enables SBD identify material requirements and the most efficient way to produce products in order to make expected profits. In understanding the nature of demand of its products, SBD can effectively determination the variations in volume as a result of price
They are targeting predictive modelling and spatial and temporal analysis. Spatial – temporal modelling means when data is collected both in time and space axis. By analysing time series and correlating it with external variables like weather, festivals and competition, predictions are made to understand various metrics like sale volumes, fast-selling items, visitor profiles, and so on. Jabong is analysing behaviour of the customer based on the purchases and lifestyle and correlate it with the location to optimize its business. Using this model they would able to predict the inventory level and type of inventory to keep for a particular location, which offer or product to give priority based in customer choice