Joint optimization of production scheduling and group preventive maintenance planning in multi-machine systems

Mishra, Aseem K.; Shrivastava, Divya; Tarasia, Devesh; Rahim, Abdur

doi:10.1007/s10479-021-04362-z

Cited by 14 publications

(2 citation statements)

References 53 publications

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“…The use of AI for complex production and/or maintenance scheduling is not a new concept, as there are already many AI algorithms explored in the literature. Within the scope of the present paper's problem, the most promising AI algorithms used in the literature are Particle Swarm Optimization (PSO) [20], Linear Programming (LP) [21], Simulated Annealing (SA) [22], Reinforcement Learning (RL) [23], and the most popular being the Genetic Algorithm (GA) [24]- [28]. Accordingly, in the present paper, a GA was chosen to be implemented due to being a well-documented algorithm in the literature for task/load scheduling, much faster when compared to more linear mathematical approaches, and being able to find solutions in large complex solution spaces.…”

Section: A Literature Reviewmentioning

confidence: 99%

Joint Production and Maintenance Scheduling for Total Cost and Machine Overload Reduction in Manufacturing: A Genetic Algorithm Approach

Mota,

Faria,

Ramos

2023

IEEE Access

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The European manufactory sector has been greatly impacted in recent times by the COVID-19 pandemic and the Russo-Ukrainian conflict, which have made energy prices soar to all-time highs reducing European companies' competitiveness in the global market. To remain competitive in these crises, manufacturing companies need to start optimizing not only their energy costs but also maintenance-derived costs, by better planning maintenance activities, through joint production and maintenance scheduling, and by improving machine longevity by way of reducing overload in single machines. Accordingly, the premise of the present paper is to propose an intelligent joint production and maintenance scheduling system to minimize total costs, that is, energy and maintenance costs, as well as minimize single-machine overload by balancing tasks between machines, while also allowing for imposed constraints in the production schedule. This is achieved through a Genetic Algorithm to solve the scheduling problem in flexible job shop manufacturing layouts. To reduce total costs, retailer energy price volatility, generated renewable energy resources availability and surplus selling, and maintenance stipulated hours prices are considered and benefited from as much as possible. Overload in single machines is reduced by minimizing the machine occupation rate standard deviation in the production schedule. A baseline scenario with real-production data from a work in the literature is used to validate the proposed scheduling system. Obtained results show that the proposed system is able to reliably reduce energy costs by 11.3% up to 15.4%, and single machine overload by 32.3% up to 52.7%.

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Section: A Literature Reviewmentioning

confidence: 99%

Joint Production and Maintenance Scheduling for Total Cost and Machine Overload Reduction in Manufacturing: A Genetic Algorithm Approach

Mota,

Faria,

Ramos

2023

IEEE Access

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“…The system will, however, be completely shut down during preventive or corrective maintenance activities. For a multimachine system with multiple components, Mishra et al (2021) developed a method for integrating production scheduling and group maintenance planning. In this approach to preventive maintenance, the goal is to identify the number of components that can be maintained simultaneously, reducing the total expected maintenance cost.…”

Section: Scheduling For Group Maintenancementioning

confidence: 99%

Failure Mode Structured Preventive Maintenance Scheduling With Changing Failure Rates in Industry 4.0 Environment

Alamri

2022

Front. Manuf. Technol.

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Critical manufacturing systems consist of many different components working together. Each component is important to the operation and performance of the whole system. It is crucial to have a sound maintenance plan to ensure continuous production output. The strategy of current research is to focus on the failure mode modelling of the manufacturing system and divide it according to failure mode in production blocks. The preventive replacement intervals are determined using mean time to failure values of components, with group replacements of components within the production blocks to prevent unexpected functional breakdown failures. With the implementation of Industry 4.0 infrastructure, the mean time to failure values of components are constantly updated. The multi-mode mathematical model developed in this paper is able to adjust the preventive replacement schedule dynamically to suit changing system conditions. The results indicate that preventive replacement of components can achieve consistent minimum system reliability of 90% while minimizing system costs due to maintenance and downtime. Novelty of the approach lies in developing a holistic preventive maintenance schedule using failure mode and effects analysis for a complete system. The approach not only improves maintainability and reliability, lowers the cost of maintenance, but also keeps continuity of production.

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A decision framework for incorporating the coordination and behavioural issues in sustainable supply chains in digital economy

et al. 2022

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Joint optimization of production scheduling and group preventive maintenance planning in multi-machine systems

Cited by 14 publications

References 53 publications

Joint Production and Maintenance Scheduling for Total Cost and Machine Overload Reduction in Manufacturing: A Genetic Algorithm Approach

Joint Production and Maintenance Scheduling for Total Cost and Machine Overload Reduction in Manufacturing: A Genetic Algorithm Approach

Failure Mode Structured Preventive Maintenance Scheduling With Changing Failure Rates in Industry 4.0 Environment

A decision framework for incorporating the coordination and behavioural issues in sustainable supply chains in digital economy

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