Job shop scheduling using hybrid Meta heuristic approach

Abstract

Schedule optimization plays an important role in well designed and newlineefficient manufacturing systems to fulfill global business needs Intelligent newlineutilization of resources to improve efficiency of the manufacturing system is a newlinecomplex combinatorial job shop scheduling problem For each job in a job newlineshop scheduling problem there is a sequence of operations which needs to be newlineprocessed without interruption on a given machine for a given period of time newlineMore than on operation of the same job cannot be processed concurrently newlineThe objective of job shop scheduling problem is to find a feasible schedule newlinethat minimizes the makespan completion time required to get the finished newlineproduct Job shop scheduling problem is one of the hard combinatorial newlineoptimization problems Hence techniques to efficiently solve job shop newlinescheduling problems are an important area of research newlineIn recent years much attention has been given to solve newlinecombinatorial optimization problems like job shop scheduling using metaheuristic newlineapproaches and in most domains no single meta heuristic method newlinedominates Hence it is desirable to gain the collective power of a set of metaheuristics newlinelike ant colony optimization and tabu search This thesis proposes newlinealgorithms based on ant colony optimization tabu search and a hybrid newlinealgorithm comprising the abilities of the first two algorithms newline