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In recent years there has been increased concern on energy efficiency of industries. Since the scheduling problems in the shop floors are directly related to the energy consumption, an appropriate way to improve energy efficiency in industrial plants is to develop effective scheduling strategies. Hence, the aim of this paper is to design an energy-efficient scheduling in a shop floor industrial environment, i.e., flexible job-shop scheduling problem (FJSP). To this end, a multi-objective optimization model is developed with three objective functions: (i) minimizing total completion time, (ii) maximizing the total availability of the system, and (iii) minimizing total energy cost of both production and maintenance operations in the FJSP. To cope with this multi-objective optimization problem, an enhanced evolutionary algorithm incorporated with the global criterion, as a multi-objective handling technique, is proposed and then performance evaluation is performed based on an extensive numerical analysis. [ABSTRACT FROM AUTHOR]

In many modern buildings, different monitoring and control systems are used, but majority of them are not directly connected with the energy consumption optimisation. This paper presents a concept of an integrated performance monitoring system for promotion of energy awareness in buildings. The reference architecture of the envisioned system evolved from the traditional management structure, plan-do-check-act model. Traditional plan-do-check-act model has been upgraded with the integration of prediction engine, knowledge repository and modern scientific approaches for supporting energy awareness including early warning, optimisation and supervision. The proposed concept is constituted upon three modules: (1) data acquisition and processing, (2) knowledge repository, (3) performance monitoring system including prediction engine, consumption modelling, emissions calculation and decision support. The concept of three modules evolved from the idea to enrich existing solutions with the awareness about possible future abnormal situations and to enable preventive actions. Presented case study represents a repeatable application of the energy cost centres based modelling of energy consumption in educational and research buildings by using neuro-fuzzy modelling approach. Initial testing results are indicating the potential of electricity savings up to 15% enabled by visualisation and awareness about electricity consumption. [ABSTRACT FROM AUTHOR]