Loading [MathJax]/extensions/MathZoom.js
Energy-efficient optimization of Flexible Job Shop Scheduling and Preventive Maintenance | IEEE Conference Publication | IEEE Xplore

Energy-efficient optimization of Flexible Job Shop Scheduling and Preventive Maintenance


Abstract:

In recent years, there has been growing concern on energy efficiency in the manufacturing enterprises. Since scheduling problem has a direct impact on energy consumption,...Show More

Abstract:

In recent years, there has been growing concern on energy efficiency in the manufacturing enterprises. Since scheduling problem has a direct impact on energy consumption, developing the effective production scheduling is among the priorities in industries. Moreover, in practice, production and maintenance operations have been viewed as major source of energy consumption in industrial system. In this paper, we propose a stochastic mathematical model for a joint production and maintenance operations scheduling problem in a flexible job shop industrial environment in which both traditional and energy efficient aspects are modeled. The objective of this research is to minimize the expected makespan in the scheduling problem focusing on C02 emissions reduction in an actual workshop which breakdowns can happen at any moment and make machines unavailable for processing operations. In fact, energy usage associated with the C02 emissions of the industrial shop floor are formulated in the constraints with respect to different states of operation and idle. To address this problem effectively, the Genetic Algorithm (GA) is applied for the proposed stochastic model to minimize the expected makespan. From an operation management viewpoint, the proposed model provides a scientific and helpful guideline for manufacturing system to plan production and maintenance simultaneously, with both economic and environmental benefits.
Date of Conference: 28-31 January 2019
Date Added to IEEE Xplore: 25 July 2019
ISBN Information:

ISSN Information:

Conference Location: Orlando, FL, USA

Summary & Conclusions

In recent years, there has been growing concern on energy efficiency in the manufacturing enterprises. Since scheduling problem has a direct impact on energy consumption, developing the effective production scheduling is among the priorities in industries. Moreover, in practice, production and maintenance operations have been viewed as major source of energy consumption in industrial system. In this paper, we propose a stochastic mathematical model for a joint production and maintenance operations scheduling problem in a flexible job shop industrial environment in which both traditional and energy efficient aspects are modeled. The objective of this research is to minimize the expected makespan in the scheduling problem focusing on C02 emissions reduction in an actual workshop which breakdowns can happen at any moment and make machines unavailable for processing operations. In fact, energy usage associated with the C02 emissions of the industrial shop floor are formulated in the constraints with respect to different states of operation and idle. To address this problem effectively, the Genetic Algorithm (GA) is applied for the proposed stochastic model to minimize the expected makespan. From an operation management viewpoint, the proposed model provides a scientific and helpful guideline for manufacturing system to plan production and maintenance simultaneously, with both economic and environmental benefits.

References

References is not available for this document.