Assembly Line Design: The Balancing of Mixed-Model Hybrid - download pdf or read online

By Brahim Rekiek

ISBN-10: 1846281121

ISBN-13: 9781846281129

Effective meeting line layout is an issue of substantial business significance. meeting Line layout might be acquired by way of technical team of workers operating in layout, making plans and creation departments in in addition to managers in who are looking to study extra approximately concurrent engineering. This e-book can also be bought through researchers and postgraduate scholars in mechanical, production or micro-engineering.

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Read or Download Assembly Line Design: The Balancing of Mixed-Model Hybrid Assembly Lines with Genetic Algorithms (Springer Series in Advanced Manufacturing) PDF

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Extra resources for Assembly Line Design: The Balancing of Mixed-Model Hybrid Assembly Lines with Genetic Algorithms (Springer Series in Advanced Manufacturing)

Sample text

In order to install safe, non-expensive and stable ALs, designers have to think about the stability of the product being assembled. 13. 13(a), task 1 must only precede task 5. Thus, the acceptable solution consists of the first station which contains task 1, the second tasks {3, 4} and the third one, task 5. 13(c) depicts a valid solution where task 1 must directly precede task 5. Indeed, 24 Assembly Line Design task 1 is directly followed by task 5. 13(b) shows a non-valid solution in which stations containing tasks 1 and 5 are separated by a station which contains tasks {3, 4}.

To and Korn [163] presented a multiobjective evolution strategy (MOB-ES) method for solving MOPs subjected to linear and nonlinear constraints. The method is based on the concept of C-(constraints), F-(objective function) and N-(niche) fitness, which allows one to handle constraints and (in)feasible individuals. The authors provided new ideas for handling (in)feasible individuals, since some niche infeasible individuals can be better than some feasible ones. Strength Pareto Evolutionary Algorithm.

It is preferable to use PROMETHEE because it is simpler to use than ELECTRE and easier to understand and manipulate. Given a set of n potential alternatives (a1 , a2 , ... ai , ... an ) and k evaluation criteria (f1 , f2 , ... fj , ... fk ), each evaluation fj (ai ) is a real number. 1. In the case of a GA, each potential alternative ai is an individual of the population and the evaluation criteria fj (ai ) is the value of objective j for individual ai . The ranking of a given population starts when the evaluation matrix is available.

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Assembly Line Design: The Balancing of Mixed-Model Hybrid Assembly Lines with Genetic Algorithms (Springer Series in Advanced Manufacturing) by Brahim Rekiek


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