A sensitivity analysis of an optimal Gantt charting maintenance scheduling model

• Published in: The International journal of quality & reliability management 2006-01, Vol.23 (2), p.197-229
• Main Authors: Oke, S.A, Charles-Owaba, O.E
• Format: Article
• Language: English
• Subjects: Arenas; Communities; Cost analysis; Gantt charts; Maintenance; Maintenance engineering; Maintenance management; Mathematical models; Optimization; Production scheduling; Scheduling; Sensitivity analysis; Studies
• ISSN: 0265-671X; 1758-6682
• DOI: 10.1108/02656710610640952

Purpose - The purpose of this paper is to work on an analytical approach to test sensitivity of a maintenance-scheduling model. Any model without sensitivity analysis is a "paper work" without advancing for wider applications. Thus, the simulation of simultaneous scheduling of maintenance and operation in a resource-constrained environment is very important in quality problem and especially in maintenance.Design methodology approach - The paper uses an existing model and presents a sensitivity analysis by utilising an optimal initial starting transportation tableau. This is used as input into the Gantt charting model employed in the traditional production scheduling system. The degree of responsiveness of the model parameters is tested.Findings - The paper concludes that some of these parameters and variables are sensitive to changes in values while others are not.Research limitations implications - The maintenance engineering community is exposed to various optimal models in the resource-constraint-based operational and maintenance arena. However, the models do lack the sensitivity analysis where the present authors have worked. The work seems significant since the parameters have the boundary values so the user knows where he can apply the model after considering the constraints therein.Originality value - The underlying quest for testing the sensitivity of the model parameters of a maintenance scheduling model in a multi-variable operation and maintenance environment with resource constraints is a novel approach. An optimal solution has to be tested for robustness, considering the complexity of the variables and criteria. The objective to test the model parameters is a rather new approach in maintenance engineering discipline. The work hopefully opens a wide gate of research opportunity for members of the maintenance scheduling community.