The increase of energy costs specially in manufacturing system encourages researchers to pay more attention to energy management in different ways. This paper investigates a non-preemptive single-machine manufacturing environment to reduce total energy costs of a production system. For this purpose, two new mathematical models are presented. The first contribution consists of an improvement of a mathematical formulation proposed in the literature which deals and deals with a scheduling problem at machine level to process the jobs in a predetermined order. The second model focuses on the generalisation of the previous one to deal simultaneously with the production scheduling at machine level as well as job level. So, the initial predetermined fixed sequence assumption is removed. Since this problem is NP-hard, an heuristic algorithm and a genetic algorithm based on the second model are developed to provide good solutions in reasonable computational time. Finally, the effectiveness of the proposed models and optimisation methods have been tested with different numerical experiments. In average, for small size instances which the mathematical model provides a solution in reasonable computational time, a gap of 2.2% for the heuristic and 1.82% for GA are achieved comparing to the exact method’s solution. These results demonstrate the accuracy and efficiency of both proposed algorithms.

By 2050, the European Union plans to make Europe the first carbon-neutral continent and a global leader in climate-green industries. Recently, many decisions have been taken in the world to ensure the energy transition from fossil fuel to renewable energy. The creation of renewable energy communities (REC) is among the solutions used to increase this transition. This study presents 16 different configurations for energy self-consumption in RECs containing different industrial factories. One mathematical model is proposed for each configuration, and they have been solved according to different criteria. The comparisons are made between these configurations according to economic, environmental, technical, and social criteria. Then, four multi-criteria decision-making (MCDM) methods are used to choose the best configurations considering all the criteria. For this purpose, the achieved results from the mathematical models are used as input for the MCDM methods. The findings demonstrate that the most effective configurations combine both individual and collective self-consumption. Furthermore, the inclusion of collective production results in multiple advantages, including a 64.71% rise in economic gains, a 26.95% decrease in CO₂ emissions, a 21.39% improvement in self-sufficiency, and a significant increase in job creation by 175.24%. In addition, incorporating storage enables a substantial rise in the degree of self-sufficiency, leading to reduced reliance on the power grid and consequent reduction in CO₂ emissions.