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Abstract
In recent decades, workshop scheduling has excessively focused on time-related indicators, while ignoring environmental metrics. With the advent of sustainable manufacturing, the energy-aware scheduling problem has been attracting more and more attention from scholars and researchers. In this study, we investigate an energy-aware flexible job shop scheduling problem to reduce the total energy consumption in the workshop. For the considered problem, the energy consumption model is first built to formulate the energy consumption, such as processing energy consumption, idle energy consumption, setup energy consumption and common energy consumption. Then, a mathematical model is established with the criterion to minimize the total energy consumption. Secondly, a modified migrating birds optimization (MMBO) algorithm is proposed to solve the model. In the proposed MMBO, a population initialization scheme is presented to ensure the initial solutions with a certain quality and diversity. Five neighborhood structures are employed to create neighborhood solutions according to the characteristics of the problem. Furthermore, both a local search method and an aging-based re-initialization mechanism are developed to avoid premature convergence. Finally, the experimental results validate that the proposed algorithm is effective for the problem under study.
Highlights
Nowadays, with the increasing emphasis on the environmental protection and sustainable development, manufacturing enterprises are facing economic pressure and environmental challenges
Several different directions are being pursued by researchers in academia and industry area, which concentrate on the machine level, the product level and the management level
To implement the proposed modified migrating birds optimization (MMBO), some items are designed according to the characteristics of the problem, such as encoding, population initialization, neighborhood structure, aging-based re-initialization and local search
Summary
With the increasing emphasis on the environmental protection and sustainable development, manufacturing enterprises are facing economic pressure and environmental challenges. It is very important to take some measurements to control energy consumption. Several different directions are being pursued by researchers in academia and industry area, which concentrate on the machine level, the product level and the management level. Production scheduling is one of the most important factors in production management, which allocates limited resources to tasks in order to reach expected targets during the whole manufacturing process. Production scheduling has proved to be an effective way of reducing energy consumption [1,2,3]. There has been an increasing number of studies on energy-aware scheduling problems
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