Название: Applications of Advanced Optimization Techniques in Industrial Engineering
Автор: Abhinav Goel, Anand Chauhan, A.K. Malik
Издательство: CRC Press
Год: 2022
Страниц: 243
Язык: английский
Формат: pdf (true)
Размер: 14.2 MB

This book provides different approaches used to analyze, draw attention, and provide an understanding of the advancements in the optimization field across the globe. It brings all of the latest methodologies, tools, and techniques related to optimization and industrial engineering into a single volume to build insights towards the latest advancements in various domains.

Applications of Advanced Optimization Techniques in Industrial Engineering includes the basic concept of optimization, techniques, and applications related to industrial engineering. Concepts are introduced in a sequential way along with explanations, illustrations, and solved examples. The book goes on to explore applications of operations research and covers empirical properties of a variety of engineering disciplines. It presents network scheduling, production planning, industrial and manufacturing system issues, and their implications in the real world.

Evolutionary approaches form a subset of modern artificial algorithms, which are inspired by nature. Evolutionary approaches are an efficient and effective choice in order to solve complex, continuous and discrete, convex and nonconvex, nonlinear and linear, optimization problems. Evolutionary algorithms are also known as metaheuristic optimization techniques. They have operations such as selection, mutation, reproduction, etc., all of which help to find out the best solutions to optimization problems.

Clustering is the approach of dividing a given data sets into a finite number of groups, which are known as data clusters. The clusters are created in such as a way that similar data points fall in the same cluster and dissimilar data points fall in different clusters. This is a long- standing problem and researchers have been trying to solve it in different ways. In machine learning, clustering can be done using unsupervised learning, wherein based on some similarity measure, groups of data are formed. There exist many clustering approaches, like k- means, c- means (a fuzzy version of k- means), mean shift clustering, DBSCAN, agglomerative clustering, expectation maximization clustering, evolutionary clustering etc.

In Linear Fractional Programming (LFP), the objective function is a ratio of two linear functions instead of a single linear function (as is the case with Linear Programming). Thus, instead of computing the best outcome such as maximum profit or lowest cost, the highest ratio of outcome to cost is computed. This ratio represents the highest efficiency. Fractional Linear Programming (FLP) problem is a special kind of non- linear programming problem which is quite commonly used in finance, industries, health care and production planning. Both linear programming and linear-fractional programming represent optimization problems using linear equations and linear inequalities, which, for each problem- instance, define a feasible set. Fractional linear programs have a more well- appointed set of objective functions.

The book caters to academicians, researchers, professionals in inventory analytics, business analytics, investment managers, finance firms, storage-related managers, and engineers working in engineering industries and data management fields.




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