Reliability Optimization for Manufacturing Industries Using Variant of Heuristic and Meta Heuristic Techniques

Abstract

In fast growing industrialization, industries are competing to manufacture the best quality newlinegoods to meet global standards. In order to manufacture needed goods in a stipulated time newlineframe without affecting manpower and maintaining profit goals, it is required for a system newlineto perform well. Here the term reliability has its importance; reliability is the probability newlineof successful performances of a system in a specific given time duration. Reliability newlineoptimization is a way to achieve desired levels of system reliability by employing several newlineapproaches. Redundancy allocation is one of those approaches that work on the tradition of newlineproviding additional components to the system for achieving the goals of reliability newlineoptimization within the given constraints. Redundancies allocated to the system work as newlinebackups that get activated on requirement or component/system failure and extend the newlinesystem s life. Among the reliability optimization problems, the redundancy allocation newlineproblem aims to identify the number of redundant components along with the component s newlinereliability in efforts to maximize system reliability within the given constraints. newlineThe research work presented in this thesis focuses on reliability optimization of newlinemanufacturing systems using variants of heuristic and meta-heuristic techniques. The newlinereliability optimization problems considered in the present research work are of three newlinemanufacturing industries: one is Yaris Pharmaceuticals, from which considered two newlinesystems are quotliquid medicine manufacturing systemsquot and quottablet medicine manufacturing newlinesystemquot and the other two manufacturing industries are taken from existing literature newlineknown as quotNeel Metal Products Limited 1A (NMPL-1A)quot (Devi and Garg, 2020) and newlinequotPharmaceutical Plantquot (Garg et al., 2010).