Title: Task scheduling and management using genetic algorithms with application in production process optimisation

Authors: L.B. Gamage; C.W. de Silva

Addresses: Industrial Automation Laboratory, Department of Mechanical Engineering, The University of British Columbia, Vancouver, Canada ' Industrial Automation Laboratory, Department of Mechanical Engineering, The University of British Columbia, Vancouver, Canada

Abstract: This paper presents a methodology which uses Genetic Algorithms (GA) for task scheduling and management in an environment where multiple jobs compete for a limited number of resources. The primary objective of the developed system of task scheduling and management is to minimise the cost of resources using available resources while ensuring that the jobs are completed within stipulated timeframes while meeting the task specifications and performance criteria. Once the resources are allocated by the GA-based scheduling algorithm to complete a given set of jobs, it is necessary to continuously monitor the progress of jobs and changes in the environment and in the event of such situations as performance degradation and machine breakdowns, to plan, allocate and rearrange the resources to achieve the system objective. In this paper, a technique is developed to accommodate machine breakdowns and unavailability of machines due to prior assignment or maintenance. Another feature of the developed algorithm is the use of domain knowledge about the process to expedite the evolution process. In the present paper, methodology is also developed to accommodate high priority jobs that may be introduced after the initial scheduling. The developed methodology is applied to plan the activation and post activation processes in an activated carbon manufacturing plant and to schedule and manage the resources such as kilns, crushers, blenders, washers and dryers in the plant. The results demonstrate the effectiveness of the developed approach.

Keywords: task scheduling; task management; process optimisation; genetic algorithms; activated carbon manufacturing.

DOI: 10.1504/IJMR.2012.048697

International Journal of Manufacturing Research, 2012 Vol.7 No.3, pp.273 - 289

Available online: 23 Aug 2012 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article