Integrating production planning and maintenance: an iterative method

Abstract

Purpose – Making decisions on production and maintenance separately, as is often done in practice and research literature, may not result in overall optimization. This paper aims to propose a joint method that better integrates production planning and maintenance at the tactical level. The potential of improving the performance of the classic planning method is also explored. Design/methodology/approach – An integrated production planning and maintenance model is proposed. The production capacity losses resulted from both preventive and corrective maintenance activities are considered. Meanwhile, the reliability deterioration of the machine is considered to be operation dependent. An iterative approach is presented to find a solution for the nonlinear model through iteratively solving a sequence of mixed integer linear programming instances, accompanied by modification of some parameters prior to each iteration. Computational experiments are conducted to evaluate the performance of the proposed method compared with three other methods, including two methods based on separate planning and one integrated model. Findings – The superiority of the proposed method compared with all the other three methods is demonstrated. Thus, the values of both integrated planning and considering operation-dependent failures are testified. The advantage of the proposed method is highlighted in the cases of high capacity utilization, long maintenance durations and low maintenance costs. The performance of the two methods based on separate planning is sensitive to the system utilization, and when utilization is high, the one with an availability-sensitive objective function defined for the maintenance problem performs better. Originality/value – Few studies have been carried out to integrate decisions on production lot and maintenance. Their considerations are either incomplete or not realistic enough. A more comprehensive and realistic integrated model is proposed in this paper, along with an iterative solution algorithm for it. A potential way to improve the performance of the classic planning method with its simplicity preserved is also presented.