To enhance the electrical power contract capacity by leveling production schedule based on data-driven

EasyChair Preprint no. 1478

Abstract

The scarcity and potential shortage of major energy resources currently used in manufacturing has become a focus of attention (Sun A. 2013). Severe carbon dioxide emissions cause global warming. The main source of carbon dioxide emissions is the production of fossil fuels and the burning of coal, most of which is used to provide electricity. In the past few decades, 75% of anthropogenic greenhouse gas emissions have been produced by burning fossil fuels, accounting for 38% of fossil fuels using fossil fuels (Boden et al., 2010). The object of this paper is based on the foundry industry. According to previous studies, industry planning in the form of production in line with orders is generally based on the lack of production capacity to account for the level of power required for different orders resulting in wasted power and high demand for instantaneous power. This study invested the leveling production concept of TPS (Toyota Production System) and applied power saving as a resource for scheduling production plans. It will provide better resource planning and results, reduce contractual capacity under the same production conditions and meet product requirements. This study contributed to the literatures by proposing a methodology to even out peak power consumption and developed optimal contract capacity which can reduce carbon emissions by 5,431 tons in one year. The methodology proposed by this study provided a simple way to execute the planned production orders.

Keyphrases: Contract capacity, Electricity planning, energy saving, Green Management, Planning schedule, power management

@Booklet{EasyChair:1478,
  author = {Ping Yu Hsu and Hong Tsuen Lei and Ming-Shien Cheng and Willie Kaiping Huang and Yi-Hao Chen and Ying Chia Peng},
  title = {To enhance the electrical power contract capacity by leveling production schedule based on data-driven},
  howpublished = {EasyChair Preprint no. 1478},

  year = {EasyChair, 2019}}