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Many countries are actively trying to cope with recent effects of climate change and the energy crisis. At the same time, efficient energy use and the reduction of greenhouse gas emissions are very important, not only to reduce energy costs but also to contribute to an environment-friendly, sustainable lifestyle. Currently, manufacturing industries in some countries pay stratified electricity rates that depend on the time of the day (i.e. peak load, mid-load and off-peak load). Hence, the production scheduling process, which considers time-dependent and machine-dependent electricity costs, enables these industries to minimise energy expenses. Additionally, the emerging Smart Grid is supposed to require industries to pay real-time hourly electricity costs. More energy-efficient, intelligent production scheduling is thus a major goal. This paper deals with minimising the total production cost of the flexible job-shop scheduling problem. Our method allows each decision-maker in the manufacturing industry to seek a compromise solution for total production costs, by considering electricity costs with distributed energy resources and energy storage. We use constraint programming and mixed-integer programming approaches to solve this problem, and compare our proposed models with the classical computational method.
Moon et al. (Mon,) studied this question.