Green manufacturing has been becoming more critical in response to changing a demand for manufacturing sustainability from lower carbonization to strict decarbonizaton. This paper extends our previous work on energy awareness in scheduling and investigates the relation between energy efficiency and productivity based on the physically measured data including power consumption and uncertainties that can be observed through the developed manufacturing physical simulator. It is also demonstrated, through physical scheduling simulations in a brute-force way, that our manufacturing simulator is capable of evaluating energy-efficient operations of manufacturing systems.

Energy savings and reduction in environmental burdens are necessitated to enhance sustainable manufacturing performances. Not only should energy consumption in the factory be visualized, but also a mechanism, by which in-process production and energy-related information measured in the shop floor are fed back into planning/scheduling decision-making, must be established to improve the energy efficiency during manufacturing execution. This study addresses the effect of scheduling on the improvement of energy efficiency in manufacturing by connecting a developed measurement and control platform with a real manufacturing system. The manufacturing system testbed utilized in this study forms a simple flow-type flexible manufacturing system composed of automated manufacturing cell with a CNC lathe, material-handling manipulator, and vertical machining center. We focus on the task scheduling of the material-handling manipulator, which yields a job sequence, and the effect of task scheduling of the manipulator on the energy efficiency and productivity of the entire manufacturing system.

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Manufacturing systems are affected by uncertainties, such as machine failure or tool breakage, which result in system downtime and productivity deterioration. In machining processes, system downtime must be reduces. This study aims to establish an automated scheduling technique that flexibly responds to unforeseen events, such as machine failure, based on adaptive operations of the handling manipulator instead of an operation schedule for the machine tools. We propose an “adaptive manipulation” procedure for establishing a reactive revision policy. The reactive revision policy modifies a portion of the manipulator operation sequence, followed by the machine operation sequence. We conduct a physical scheduling simulation on a material-handling manipulator system imitating a job-shop manufacturing system. Through simulations involving machine breakdown scenarios, the applicability of the reactive revision policy based on adaptive manipulation is demonstrated.

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