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This paper intends to discover, analyze and construct a model that may be used to measure the interactions between major factors which are identified by expert opinion and literature review for sustainable maintenance specific to manufacturing industries using the total interpretive structural modeling (TISM) approach.

In total, 12 factors were acknowledged from the literature review and the opinions of experts from manufacturing industries. Scheduled interviews with the employees were conducted by using the questionnaire which is developed from the identified 12 factors in order to find the interrelationships among these factors. The TISM approach is used for analyzing factors' interrelationships. The Matrice d'Impacts Croises Multiplication Appliques a un Classement (MICMAC) approach is used to identify the key factors which influence sustainable maintenance.

This paper found 12 factors that have ascendancy over the sustainable maintenance practices in the industry by reviewing the literature and consulting industry experts to realize the linkage between the factors. The results found that availability rate, adopting government policies, training and education are key factors that influence sustainable maintenance.

The proposed model would be valuable for experts to understand the factors influencing sustainable maintenance in the industry. This model can be used by an organization's maintenance managers to implement sustainable maintenance practices in their plants.

This study analyzes the interrelationship between factors influencing sustainable maintenance in manufacturing industries, which is a new effort in this domain of practice.

This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.

A mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.

It was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.

The proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.

Maintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.

This work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.