A Comparative Analysis of Job Scheduling for Optimum Performance of Parallel Machines by Considering the Energy Consumption



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Submitted Jul 25, 2018
Published Sep 18, 2018
10.24018/ejeng.2018.3.9.841

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In this study, a comparative analysis of scheduling of machines and jobs was conducted by proposing a method, which aims to optimize the performance of the job-shop environment. A wide range of objective functions including make-span, maximum tardiness, total flow time, total tardiness, total weighted flow time, and total weighted tardiness, and energy consumption. The decision variables of a manufacturing company including the job’s weight, stating and completion time, due dates, releasing date, and processing time were considered as inputs of the optimization model. Then, subject to the defined technical constraints of the system, a comparative analysis on the basis of eight scheduling methods was conducted to assess the performance indicators of the job-shop system. Results of the analysis revealed variations of different objective functions based on the proposed methods. The optimum make-span varied from minimum of 210 days estimated by the Earliest Due Date (EDD) method, up to 222 days estimated by the Critical Ratio (CR) method. Further recommendations were also made for optimum values of the involved decision variables in the considered job-shop environment. Results revealed that the maximum tardiness was best optimized using the EDD method, whilst the Shortest Processing Time (SPT) led to the best optimum value for the total flow time. Analysis of the energy evaluation of the machines showed that the optimum overall energy consumption has been observed with the value of 7,934 kWh for the first operating machine under the effect of the Minimum Slack method, whilst the optimum energy consumption for the second machine was observed with the value of 7,968 kWh using the First Come First Served (FCFS). Last stage of study recommended the optimum planning schedules and resource allocations of the jobs in the machines considering each scheduling method during the operation of the designed job-shop.

Keywords: Scheduling Performance Optimization Parallel Machines Jobs Processing Machines Manufacturing

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