Productivity Improvement Using Heuristics Algorithm in Scheduling a Flow-Shop Manufacturing

In manufacturing industries, meeting the dynamically changing need of customers and delivery times is the key to stay at the apex of global or national competitions. Manufacturing system consists and integrates entities such as machines, jobs with different operations to be processed in the corresponding machine, input materials, human operators, and all the things that facilitate the production system of a manufacturing industry so that enabling the firm to generate good wealth and to cope the dynamically changing market demand. The problem under study is a textile garment manufacturing industry of a flow shop-manufacturing. Even, giving high priority to the first arrival jobs and seems fair to customers and jobs, however, does not consider other customer and job characteristics such as production cost, idle time, make-span, and tardiness of jobs. In this flow shop type of scheduling problem, “n” jobs considered to process on “m” machines and preemption of jobs not allowed. In addition, assumed the machines could process only one job at a time. The study conducted with the main aim of productivity improvement by minimizing the idle time of machines to control criteria or parameters such as make-span, resource utilization, and production cost for the case company by finding the most optimal sequence of jobs under the study. To carry out the study and find the best and efficient sequence of jobs heuristics algorithms such as NEH, CDS, palmers and EDD rules in the flow shop-manufacturing used, and the NEH resulted in the best sequence of jobs. In the proposed sequence of jobs with a 3.6% utilization improvement, the productivity improved by 17.2% than the existing schedule.