Optimization problem After having defined the structure of the extractive distillation process, the optimal values of the design and operating variables can be determined based on an optimization problem whose details will be discussed as follows. Decision variables Due to the fact that the parameters specified as the (design/operating) degrees of freedom can be used as the decision variables, the decision variables can be divided into two categories including: design and operating decision variables
1991). We can classify the site layout problems into two different aspects - Static problems and Dynamic problems. According to Ning et al. (2010), a site layout planning without changing the available locations and the facilities in different stages of construction are classified as static problem. While for dynamic problems, it involves the changing of required facilities and the available locations in different construction stages, it is called dynamic problems. According to Cheng and O’Connor, (1996)
I Wish to Pursue an MS Degree in Electrical Engineering During my senior year at Purdue University, I made a decision that has impacted the entire course of my education. While my classmates were making definite decisions about their career paths, I chose to implement a five-year plan of development and growth for myself. I designed this plan in order to examine various careers that I thought might interest me, as well as to expand upon my abilities at the time. As I was attaining a BS degree
to extend the intrinsic lifetime of piece of very expensive equipment to be systematic replaced. The objective of preventive interventions is to either reduce the effect of the system wear-out or delay the onset of these effects. Deterministic optimization models have been proposed by various authors. Yao et al (2001) presented a model with two-layer hierarchical structure that optimizes the preventive maintenance scheduling for operations in semiconductor manufacturing industry. For the higher level
In the literature review we explain the modeling process first before discussing different methods of simulation and modelling and interpreting the methods in enterprise-wide modeling. I. Modelling Process: The powerful technique, which allows researchers from diverse backgrounds to analyze and study complex phenomena is Modelling. In general model is ‘A (small) finite description of an infinitely complex reality, constructed for the purpose of answering particular questions’ (Kuipers 1994). Even
(2013) gave a mixed integer programming formulation that lets us to make efficient, almost orthogonal, almost balanced designs for mixed factor problems. These problems call nearly orthogonal-and-balanced (NOAB) designs (Vieira et al., 2013). Generic nonlinear programming (NLP) problems hold continuous or integer variables, but mechanical design optimizations usually include continuous, binary, discrete and integer variables (Garg, 2014).
This project involves optimization of materials procurement, transportation in construction projects. With the thought of operations research, designed a objective function and constrained conditions for a materials procurement and transportation optimization model. According to data, simulates the cost and the method provided can be used to analyze the rule of materials procurement and transportation cost and make a correct decision and to solve the problem of analyzing and forecasting procurement
Portfolio theory deals with the problem of constructing a collection of assets that reflect the individual needs. When a portfolio is constructed a variety of parameters can be taken into account, such as value, average, the riskiness of the asset. The financial objectives of the investor determines what types of assets to be used. In this paper a quantitative approach of choosing the portfolio will be discussed. Modern Portfolio Theory as introduced by Markowitz (1952) frames the time dimension
JP Molasses The analysis is divided into three sections: Part I: description of the optimization model Part II: solution to the present problem Part III: recommendations on future improvements to increase profits Part I Objective function: J.P. Molasses' goal is to maximize the profit generated from the refining of raw sugar into molasses and its byproducts and then shipping those products to customers. Decision variables: a. The amount of raw sugar shipped from eight suppliers to two
Car-like Vehicle Models A car-like vehicle resembles completely an automobile. It consists of four wheels for locomotion and is capable of being steered from one place to another. Car-like vehicles model can be classified as rear-wheel, front-wheel and four-wheel driving ground vehicles. For a rear wheel drive vehicle, the rear tires handle the engine dynamics while the front only needs to handle the steering forces. Figure 2, depicts the vehicle model schematic for a rear drive vehicle. The states
supervisors, specialist staffs -More organization levels, autocratic style: unilateral goal setting, assignment of workers -Frequent alienation: “It’s only a job” -Less individual development opportunity and employment security STS -Joint optimization of systems -People as complements to machines -Optimal task grouping, multiple, broad skills -Internal controls: self-regulating subsystems -Fewer levels, participative style: Bilateral goal setting -Commitment: “It’s my job, group, and
realistic results. Namely, to determine the customers' preferences we use a linear additive model of part-worth utilities; than we specify utility function for each of the identified segments. We formulate the product line optimization problem as a nonlinear integer programming problem, employing the Nash equilibrium concept to model competitive reactions. In the proposed model, we assume profit maximizing firms. We tested the performance of our new model and compared it with the performance of the scenario
algorithm is applied on pole-balancing problem with regulation. Simulation results turn out this regulated approach is capable of estimating state transition probabilities and improving its policy simultaneously. Keywords – POMDP; SPOVA; Pole-balancing. Introduction Markov Decision Process (MDP) has proven to be a useful framework for solving a variety of problems in areas, including robotics, economics, and manufacturing. Unfortunately, many real-world problems cannot be modeled as MDP, especially
Aggressive technology scaling has resulted in increasing process variations and statistical diversity in manufacturing. Process variations result in varying in path lengths, and thus a possibly different set of critical paths for different process corners, necessitating consideration of process variation in delay test methods. Also, process variation adds some of near-critical paths to the critical/longest paths set [4]. Therefore, to maintain the reliability of circuits, testing methodologies need
software by creating state of the art optimization algorithms. IDSC released a product called NETWISE 3.0 in response to shippers requesting packaged and conditional bids, carriers having a difficult time selecting the lanes that compliment their current network, and determining the dedicated opportunities within a bid. NETWISE 3.0 are a decision support tool used in the analysis of profitability, pricing, and network balance. Companies were having a problem of having their carriers spend countless
important scientific advances of the mid 20th century. Its impact since the 1950’s has been extraordinary. Today it is a standard tool used by some companies (around 56%) of even moderate size. Linear programming uses a mathematical model to describe the problem of concern. Linear programming involves the planning of activities to obtain an optimal result, i.e., a result that reaches the specified goal best (according to the mathematical model) among all feasible alternatives. Linear Programming as seen
Over the past two years, I have accumulated a good knowledge of Finance. I was introduced to Bayesian Statistics, GARCH processes, and other topics of time series analysis. I also learned how to price volatility swaps and categorize different optimization tasks. While I never intended to focus solely on the practical side of finance, nearly all of my work revolves around it. For example, I have done research that forecasted assets' expected returns as well as research on a better way to execute
a nonlinear difference equation, into a linear equation and we determine its explicit solution. This result able us to study the behavior of this solution and check the results known in stability theory. 2 | P a g e 1. Introduction Many types of problems are naturally described by recurrence relations said difference equations [2, 3], which usually
The Canny edge detection algorithm is commonly known as the optimal edge detector. During his research work, Canny's main intentions were to enhance the edge detectors which were already out at that time. Canny was successful in his objective and published a paper entitled "A Computational Approach to Edge Detection" in which he mentions a list of criteria which could improve current methods of edge detection. According to him, low error rate was one of the important criteria. Secondly, the edges
Minimization (PCM), under which payment costs are minimized directly. In order to solve the PCM in the dual space, the Lagrangian relaxation and surrogate optimization approach is frequently used. When standard optimization methods, such as branch-and-cut, become ineffective due to the large size of a problem, the Lagrangian relaxation and surrogate optimization approach provides a good feasible solution within a reasonable CPU time. The convergence of the standard Lagrangian relaxation and surrogate subgradient