An integrated production scheduling and workforce capacity planning model for the maintenance and repair operations in airline industry

Abstract

Maintenance, repair, and overhaul (MRO) activities for the airline sector are generally subject to some regulations to ensure the safety and the continuity of flights. The critical equipment on aircraft must go through MRO at regulated intervals for the continuing permission of use. Thus, the strict deadlines constraint overhaul activities. Several systems on aircrafts are of so-called rotable module type. These expensive rotable modules are overhauled by MRO companies and used repeatedly. MRO companies usually perform exchange programs with customer airlines regarding the expensive rotable modules. When an airplane comes for an MRO service involving rotable module, a ready-to-use module from the inventory of MRO company is exchanged with the rotable module extracted from the airplane so that the service time for the aircraft is minimized. The extracted module is overhauled in the MRO shop with a limited workforce capacity and the overhauled module is rotated back to the inventory for a future exchange. We tackle the overhaul and exchange scheduling problem together with the workforce planning for MRO companies with an expedited overhaul option. We propose a mixed integer programming formulation of the problem as a finite planning horizon model where we assume that there are multiple types of rotables handled by the MRO company, and we minimize the sum of inventory holding and workforce-related costs. The salient features of the model are that we allow exchanges to be carried out earlier than their due dates, if it makes sense cost wise, up to a certain earliness limit, and we assume that there is an expedited overhaul option. The model uses two types of time buckets, small and big, for overhaul scheduling and workforce planning, respectively. Both the problem and its model are new in the literature. We show that this planning problem is NP-Hard. We provide extensive numerical tests on a set of randomly generated problems and propose some managerial insights based on the results obtained.