Fierce competition between airlines has led to the need of minimising the operating costs while also ensuring quality of service. Given the large proportion of operating costs dedicated to aircraft maintenance, cooperation between airlines and their respective maintenance provider is paramount. In this research, we propose a framework to develop commercially viable and maintenance feasible flight and maintenance schedules. Such framework involves two multiobjective mixed integer linear programming (MMILP) formulations and an iterative algorithm. The first formulation, the airline fleet maintenance scheduling (AMS) with violations, minimises the number of maintenance regulation violations and the number of not airworthy aircraft; subject to limited workshop resources and current maintenance regulations on individual aircraft flying hours. The second formulation, the AMS with tail assignment (TA) allows aircraft to be assigned to different flights. In this case, subject to similar constraints as the first formulation, six lexicographically ordered objective functions are minimised. Namely, the number of violations, maximum resource level, number of tail re-assignments, number of maintenance interventions, overall resource usage, and number of not airworthy aircraft. The iterative algorithm ensures fast computational times while providing good quality solutions. Additionally, by tracking aircraft and using precise flying hours between maintenance opportunities we ensure that the aircraft are airworthy at all times. Computational tests on real flight schedules over a 30-day planning horizon show that even with multiple airlines (6457 flights, 1032 aircraft, 5 maintenance workshops) our solution approach can construct near optimal maintenance schedules within minutes.
During a single planning period, Kimberly-Clark Latin America manufactures dozens of stock-keeping units (SKUs) in varying quantities using a few machines. The same SKU can be manufactured on multiple machines, some of which are more efficient than others. In addition, the setup time for an SKU is sequence dependent, and its demand is stochastic between planning periods. The stochastic demand necessitates changing production plans each planning period; given the large number of SKUs and small number of machines, this leads to inefficiencies. This paper describes the formulation and corresponding solution approach of an integrated inventory, production-planning, and detailed scheduling model to address the inefficiencies in lot sizing, production scheduling, and inventory management. The paper's key contribution is the solution approach, which solves the resultant industry-size NP-hard problem in minutes. The solution quality and its implementation have been tested extensively, and the model has been successfully deployed in five countries. A reduction in finished product inventories of up to 45 percent, an increase in yield and uptime of 2 percent, and improvements in service levels of 2.4 percent are directly attributable to the model and the solution approach highlighted in the paper.
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