Effective scheduling is challenging in Component Maintenance, Repair, and Overhaul (CMRO) operations due to the complexity of dynamically allocating resources across multiple jobs with varying priorities and technical constraints. Current industry practices typically rely on static, manual scheduling, resulting in suboptimal resource allocation and insufficient adaptability to operational disruptions. Most existing studies approach specific job shop problems by incorporating individual features, such as job prioritization or resource constraints, without considering the combined operational complexities of CMRO shops. Therefore, this research presents a scheduling model using the Flexible Job Shop Scheduling Problem (FJSSP), tailored to dynamic CMRO environments. The model uses Mixed Integer Linear Programming (MILP) to simultaneously schedule technicians and machines while accounting for skill requirements, resource constraints, and job prioritization. The approach balances multiple objectives, including tardiness and earliness, to enhance shop performance metrics such as Turnaround Time (TAT) and On Time Delivery (OTD) rates. Outcomes from a case study applied to real-world data from CMRO shops demonstrate significant operational improvements, achieving a reduction in TAT of up to 34% and an improvement in OTD by approximately 23% relative to historical shop performance. Furthermore, the model incorporates schedule robustness measures, minimizing deviations from planned schedules, despite operational uncertainties. Additionally, comparative analysis with a traditional heuristic dispatching rule model confirms the superior performance of the proposed optimization framework. This framework can be broadly applied to improve scheduling efficiency and stability in CMRO shops and similar workshop environments.

Social and political pressure has made emissions an increasingly important subject for companies. The extension of the sustainability reporting mandate by the EU has increased the necessity for carbon transparency for companies in Europe. Warehousing and distribution account for 13\% global supply chain emissions and can be an important factor in reducing company emissions. The traditional trade off in warehousing storage systems between cost, service level and emissions has mostly been in favor of cost and service level. Now however, emissions are becoming just as important to companies. This paper investigates the emission levels of manual and automated storage system, specified to aviation standard parts. Aviation standard parts are chosen as these are commonly stored using automated systems, due to the high degree of accuracy needed and the quantities involved in this sector. Current knowledge does not provide insight into the environmental footprint of automated and manual storage systems for small parts. This paper aims to fill this gap by combining literature research and expert consulting to gain insight into the carbon footprint of storage systems and modelling to gain insight into operational energy consumption. Finally, a case study is performed at an aviation parts dealer for empirical evidence on storage system emissions.