A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance

Ying Liu, Haibo Dong, Niels Lohse and Sanja Petrovic

International Journal of Production Economics, 2016, vol. 179, issue C, 259-272

Abstract: Increasing energy price and requirements to reduce emission are new challenges faced by manufacturing enterprises. A considerable amount of energy is wasted by machines due to their underutilisation. Consequently, energy saving can be achieved by turning off the machines when they lay idle for a comparatively long period. Otherwise, turning the machine off and back on will consume more energy than leave it stay idle. Thus, an effective way to reduce energy consumption at the system level is by employing intelligent scheduling techniques which are capable of integrating fragmented short idle periods on the machines into large ones. Such scheduling will create opportunities for switching off underutilised resources while at the same time maintaining the production performance. This paper introduces a model for the bi-objective optimisation problem that minimises the total non-processing electricity consumption and total weighted tardiness in a job shop. The Turn off/Turn on is applied as one of the electricity saving approaches. A novel multi-objective genetic algorithm based on NSGA-II is developed. Two new steps are introduced for the purpose of expanding the solution pool and then selecting the elite solutions. The research presented in this paper is focused on the classical job shop environment, which is widely used in the manufacturing industry and provides considerable opportunities for energy saving. The algorithm is validated on job shop problem instances to show its effectiveness.

Keywords: Energy efficient production planning; Sustainable manufacturing; Job shop scheduling; Multi-objective optimisation; Genetic algorithms (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:179:y:2016:i:c:p:259-272

DOI: 10.1016/j.ijpe.2016.06.019

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