Applying Genetic Algorithms to the U
... The traditional (linear) assembly line balancing problem is known to be NP-hard [9]. If there are m tasks and r ordering constraints then there are m!/2’ possible tasks sequences [2]. With such a vast search space it is nearly impossible to obtain an efficient solution using a deterministic algorith ...
... The traditional (linear) assembly line balancing problem is known to be NP-hard [9]. If there are m tasks and r ordering constraints then there are m!/2’ possible tasks sequences [2]. With such a vast search space it is nearly impossible to obtain an efficient solution using a deterministic algorith ...
Resources
... (Production of the first sample on the first machine do not efficiently - c11 = 0, the second estimated efficiency c12 = 10, ..., manufacturing efficiency quadruple sample estimated in the fourth machine c44 = 20). Create the most efficient plan for product manufacturing. Task 16. According to the p ...
... (Production of the first sample on the first machine do not efficiently - c11 = 0, the second estimated efficiency c12 = 10, ..., manufacturing efficiency quadruple sample estimated in the fourth machine c44 = 20). Create the most efficient plan for product manufacturing. Task 16. According to the p ...
Solved Problems - McMaster University > ECE
... 5.8.2 We notice that the two inputs to the loop settle to constant values (the disturbance will converge to -3). Then all signals in the loop should converge to constant values, unless the loop is unstable. If the loop is unstable, signals in the loop will not settle and the concept of steady state ...
... 5.8.2 We notice that the two inputs to the loop settle to constant values (the disturbance will converge to -3). Then all signals in the loop should converge to constant values, unless the loop is unstable. If the loop is unstable, signals in the loop will not settle and the concept of steady state ...
Solving the Assignment Problem with the Improved
... The assignment problem is concerned with assigning n entities to n slots for achieving minimum cost or maximum profit. It is known to be a polynomial combinatorial optimization problem. Its applications cover pattern classification, machine learning, operations research and so on. For solving the assi ...
... The assignment problem is concerned with assigning n entities to n slots for achieving minimum cost or maximum profit. It is known to be a polynomial combinatorial optimization problem. Its applications cover pattern classification, machine learning, operations research and so on. For solving the assi ...
MatLab - Systems of Differential Equations
... This final section shows how to create two dimensional phase portraits and direction fields. You begin by downloading the MatLab files for pplane and dfield by John Polking from Rice University. The current version is pplane8, which is invoked by having this m-file in your current directory and typi ...
... This final section shows how to create two dimensional phase portraits and direction fields. You begin by downloading the MatLab files for pplane and dfield by John Polking from Rice University. The current version is pplane8, which is invoked by having this m-file in your current directory and typi ...
Multiple-criteria decision analysis
Multiple-criteria decision-making or multiple-criteria decision analysis (MCDA) is a sub-discipline of operations research that explicitly considers multiple criteria in decision-making environments. Whether in our daily lives or in professional settings, there are typically multiple conflicting criteria that need to be evaluated in making decisions. Cost or price is usually one of the main criteria. Some measure of quality is typically another criterion that is in conflict with the cost. In purchasing a car, cost, comfort, safety, and fuel economy may be some of the main criteria we consider. It is unusual that the cheapest car is the most comfortable and the safest one. In portfolio management, we are interested in getting high returns but at the same time reducing our risks. Again, the stocks that have the potential of bringing high returns typically also carry high risks of losing money. In a service industry, customer satisfaction and the cost of providing service are two conflicting criteria that would be useful to consider.In our daily lives, we usually weigh multiple criteria implicitly and we may be comfortable with the consequences of such decisions that are made based on only intuition. On the other hand, when stakes are high, it is important to properly structure the problem and explicitly evaluate multiple criteria. In making the decision of whether to build a nuclear power plant or not, and where to build it, there are not only very complex issues involving multiple criteria, but there are also multiple parties who are deeply affected from the consequences.Structuring complex problems well and considering multiple criteria explicitly leads to more informed and better decisions. There have been important advances in this field since the start of the modern multiple-criteria decision-making discipline in the early 1960s. A variety of approaches and methods, many implemented by specialized decision-making software, have been developed for their application in an array of disciplines, ranging from politics and business to the environment and energy.