PRODUCTIVITY ENHANCEMENT FOR JOB SHOP SCHEDULING PROBLEM USING GREY WOLF OPTIMIZATION (GWO) ALGORITHM: (CASE STUDY OF AKAKI BASIC METAL INDUSTRY)

Abstract

Ethiopian metal manufacturing industries are struggling with a long manufacturing cycle, which results in lower efficiency. Because of issues with improper job allocation on the given machines and machine failure, Akaki BMI has performed significantly worse than other major metal industries in Ethiopia. The necessary data was gathered from company reports and questionnaires in order to investigate existing issues that impede industry productivity. The sample size considered is 107, of which 98 completed and returned the questionnaires on time, yielding a response rate of approximately 91.6%. It was determined that the major issues affecting Akaki BMI's productivity are related to machinery failure and scheduling. The industry employs a First Come First Serve (FCFS) scheduling method. Thus, the main concern of this research was to pinpoint the causes of the aforementioned problems and to look for different alternative mechanisms/techniques to come up with the best solution. The solution to scheduling problems is presented and analyzed using the LEKIN and MATLAB software. From the study analysis, GWO based scheduling has better to increase productivity by reducing the makespan, total flow time, total tardiness of the jobs and energy consumption of each machine in the machine shop during both operation time and idle time . Finally, the findings of the study using FCFS and GWO algorithms showed that the makespan 385 & 225 minutes respectively, which is reduced by a 41.56% improvement, total flow time 5010 & 3840 minutes respectively, which is reduced by a 23.35% improvement, total tardiness 2778 & 1553 minutes respectively, which is reduced by a 44.1% improvement, total energy consumption during operation time of the machines shop 1314.53 & 883.9 kWh respectively, which is reduced by a 32.76% improvement, total energy consumption during idle time of the machines 888.625 & 449 kWh respectively, which is reduced by a 49.47% improvement, and the machine productivity could also be improved by 41.55% per machine/minutes improvement