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The purpose of this paper is to develop a stochastic detailed schedule for a preventive/scheduled/periodic maintenance program of a military aircraft, specifically a rotorcraft or helicopter.

The new model, entitled the military “periodic aviation maintenance stochastic schedule” (PAM-SS), develops a stochastic detailed schedule for a PUMA SA 330SM helicopter for the 50-h periodic inspection, using cyclic operation network (CYCLONE) and Monte Carlo simulation (MCS) techniques. The PAM-SS model identifies the different periodic inspection tasks of the maintenance schedule, allocates the resources required for each task, evaluates a stochastic duration of each inspection task, evaluates the probability of occurrence for each breakdown or repair, develops the CYCLONE model of the stochastic schedule and simulates the model using MCS.

The 50-h maintenance stochastic duration follows a normal probability distribution and has a mean value of 323 min and a standard deviation of 23.7 min. Also, the stochastic maintenance schedule lies between 299 and 306 min for a 99 per cent confidence level. Furthermore, except the pilot and the electrical team (approximately 90 per cent idle), all other teams are around 40 per cent idle. A sensitivity analysis is also performed and yielded that the PAM-SS model is not sensitive to the number of technicians in each team; however, it is highly sensitive to the probability of occurrence of the breakdowns/repairs.

The PAM-SS model is specifically developed for military rotorcrafts, to manage the different resources involved in the detailed planning and scheduling of the periodic/scheduled maintenance, mainly the 50-h inspection. It evaluates the resources utilization (idleness and queue), the stochastic maintenance duration and identifies backlogs and bottlenecks.

The PAM-SS tackles military aircraft planning and scheduling in a stochastic methodology, considering uncertainties in all inspection task durations and breakdown or repair durations. The PAM-SS, although developed for rotorcrafts can be further developed for any other type of military aircraft or any other scheduled maintenance program interval.

The construction industry today is one of the biggest industries in the world. As projects continue to grow in complexity, project management continues to evolve. Contractor selection is a difficult task that owners and project managers face. Although previously researchers have worked on the subject of contractor selection, a comprehensive decision support system for contractor selection has not yet been developed. Recent reports of major delays and cost overruns in mega projects highlight the need for a model that is able to be flexible and comprehensive becomes evident. The paper aims to discuss these issues.

The research focuses on obtaining insights from field experts using both quantitative and qualitative methods. Then, a model was developed in the light of the data collected. Accordingly, the model was tested on a case study.

This paper presents a model for contractor selection that is wholesome in its take on the topic. The model incorporates both managerial and technical aspects of the problem. The model was tested on a case study and it was proven to be feasible in real world applications. The contractor selection decision support system serves the needs of both academics and industry managers, as an integral part of project management.

The model presented in this paper is innovative in its take on the problems. MCDA tools have been uniquely modified in this paper to cater to the needs of the selection problem while accounting for the criteria hierarchy that incorporates aspects that are instrumental for proper evaluation of a contractor’s likelihood of success.