Manufacturing enterprises nowadays face the challenge of increasing energy prices and requirements to reduce their emissions. Most reported work on reducing manufacturing energy consumption today focuses on the need to improve the efficiency of resources (machines) largely ignoring the potential for energy reducing on the system-level where the operational method can be employed as the energy saving approach. The advantage is clearly that the scheduling and planning approach can also be applied across existing legacy systems and does not require large investment. Therefore, a multi-objective scheduling method is developed in this paper with reducing energy consumption as one of the objectives. This research focuses on classical job shop environment which is widely used in the manufacturing industry. A model for the bi-objectives problem that minimises total electricity consumption and total weighted tardiness is developed and the Non-dominant Sorting Genetic Algorithm is employed as the solution to obtain the Pareto front. A case study based on a modified 10 Â 10 job shop is presented to show the effectiveness of the algorithm and to prove the feasibility of the model.
Liu et al. REF developed a model for the bi-objectives problem that minimizes total electricity consumption and total weighted tardiness, where the non-dominant sorting genetic algorithm is employed to obtain the Pareto front.
An investigation into minimising total energy consumption and total weighted tardiness in job shops
Journal of Cleaner Production
Economics