deterioration, its management is a critical issue, for current and future generation. During the past three decades, many groundwater management models were developed by several researchers by linking groundwater flow/transport simulation model with optimization model (Shamir et al.,1984; Ahlfeld et al., 1986; Lefkoff and Gorelick, 1986; Willis and Finney, 1988; Finney and Samsuhadi, 1992; Emch and Yeh, 1998; Zheng and Wang, 2002,Wu and Zhu, 2006; Ayvaz,2009;Gaur et al.,2011a; Gaur et al.,2011b; Ghandour
Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems Introduction 1.1 Multi-objective optimization: Optimization techniques are reflected as one of the finest techniques for finding optimal design using machines. Multi-objective optimization “The main focus of this work” deals with finding solutions for problems having more than one objectives. And obviously there is more than one solution for such problems due to the nature of multi-objective
There are 10 guid... ... middle of paper ... ... type of software like CPLEX, XPRESS, OSL and GUROBI that can used solve MIP problems but not limited to MIP problems. LINGO is a simples and powerful software that can be used to solve MIP optimization problems. This software can handle tens of thousands of variables and constraints with up to few thousand integer variables (Schrage, 2006). Wong et al. (2010) and Easa and Hossain (2008) used this software to solve MIP problems to find the global
The system learns to infer a function from a collection of labeled training data. The training dataset contains a set of input features and several instance values for respective features. The predictive performance accuracy of a machine learning algorithm depends on the supervised learning scheme [8]. The aim of the inferred function may be to solve a regression or classification problem. There are several metrics used in the measurement of the learning task like accuracy, sensitivity, specificity
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
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).
One great barrier that has stood in front of computer programmers is that of finally realizing a dream of building a computer system that realistically models human thinking. The ethics of realizing such a dream are widely debated. Many believe it would be an extremely dangerous thing to accomplish, but that hasn’t stopped many from trying. The two main systems that have been developed so far that come closest to accomplishing this goal are neural networks and fuzzy logic control systems. This
both space and time. This paper investigates Smooth Partially Observable Value Approximation (SPOVA) [2], which approximates belief values by a differentiable function and then use gradient descent to update belief values. This POMDP approximation 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.
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
any given time is defined as: Where pmax is the power consumption at peak load and pmin is the power consumption at minimum load. (b) Task Consolidation Algorithm: - Task consolidation is the process of allocating tasks to resources without violating time factors thus maximize resource utilization. There are two energy task consolidation algorithms i.e. ECTC (Energy Consolidation and Task Consolidation) and MaxUtil (Maximum Utilization). Both are same but their cost function is different. The cost
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
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
STATEMENT OF PURPOSE “They say the entire value of the postage stamp consist in its ability to stick to something until it gets there. Be like the postage stamp; finish the race that you’ve started!” - Albert Einstein ------------------------------------------------------------------------------------------------------------------------------------------------ To explore knowledge and achieve excellence has been my inspiration in life. From my early years at high school to my current job at Computer
A. Background A. 1.The facts about food waste and hunger in Australia Australia is a great food-producing county and is truly lucky enough to feed 60 million people [1] which is almost twice as the current estimated population of about 36.24 million people.[2] However recent research shows that more than 4 million tonnes of food are disposed to landfill each year, of which food retailing accounts for 1.38 million tonnes and 2.6 million tonnes come from Australian household.[3] Every year Australian
The classical job-shop scheduling problem (JSP) is a combinatorial optimization problem, which is among the most complicated problems in the scheduling area. The JSP has been proven to be NP-hard (Zhang et al., 2008). Flexible job-shop scheduling problem (FJSP) is a generalization of the classical JSP. It takes shape when alternative production routing is allowed in the classical job-shop (Al-Hinai, 2011). FJSP is NP-hard due to; (a) assignment decisions of operations to a subset of machines and
Chapter 3 OPTIMIZATION 3.1 Introduction Optimization is a chronic and natural process usually witnessed in our daily life events. In various disciplines such as engineering designs, manufacturing systems, agricultural sciences, physical sciences, economics, pattern recognition etc. optimization is observed. Optimization is, thus a process of making best, effective and functional solution out of possible choices no way differs from the structural optimization which is being conceived in the present
being used. Integrated Decision Support Corporation (IDSC) is a company that provides decision support software to the truckload transportation industry. IDSC focuses on providing superior decision making 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
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
Extrimi liernong Mechoni (ELM) [1] os e songli hoddin leyir fiid furwerd nitwurk (SLFN) ontrudacid by G. B. Haeng on 2006. In ELM, thi wioghts bitwiin onpat end hoddin niaruns end thi boes fur iech hoddin niarun eri essognid rendumly. Thi wioght bitwiin uatpat niaruns end hoddin niaruns eri giniretid asong thi Muuri Pinrusi Ginirelozid Invirsi [18]. Thos mekis ELM e fest liernong clessofoir. It sarmuants verouas tredotounel gredoint besid liernong elgurothms [1] sach es Beck Prupegetoun (BP) end
Mathematics has always been a necessary component in modern warfare. During the World War II era, mathematicians Alan Turing and John von Neumann were responsible for some of the technological and scientific developments which contributed Allied victory. After considering their accomplishments before the war, their contributions during the war, and how they were recognized after the war, you will see that each mathematician is remembered very differently for their contributions. Turing is barely