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A Genetic Algorithm Approach for Minimization of Mean Flow Time in Job Shop Scheduling

Author(s):

Sunil Singh , VINDYA INSTITUTE OF TECHNOLOGY & SCIENCE SATNA (M.P.); Narendra Jaiswal, VINDYA INSTITUTE OF TECHNOLOGY & SCIENCE SATNA (M.P.)

Keywords:

Job Shop Scheduling, Genetic Algorithm Approach

Abstract

This dissertation is motivated by a scheduling problem that is commonly observed in today’s manufacturing world. It contributes to the theoretical and practical aspects of scheduling research. It is dedicated to the analysis of scheduling a set of jobs on multiple machines when the jobs have uncertain processing times and conformance to the due date is the performance objective. Efficient scheduling can improve productivity through reduced work-in-process and finished goods inventories. The objective of the research is to efficiently solve the adaptive job shop scheduling problem and to determine best job schedule for the problem using genetic algorithm that minimizes the mean flow time and mean tardiness. In this research, we are developing a method to optimize the scheduling process that improves the customer satisfaction through better adherence to deliver dates and simultaneously minimizing the resource usage.

Other Details

Paper ID: IJSRDV4I50275
Published in: Volume : 4, Issue : 5
Publication Date: 01/08/2016
Page(s): 607-612

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