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A metaheuristic technique for energy-efficiency in job-shop scheduling

Published online by Cambridge University Press:  12 January 2017

Joan Escamilla ,
Miguel A. Salido ,
Adriana Giret  and
Federico Barber
Joan Escamilla
Affiliation:
Instituto de Automática e Informática Industrial, Universidad Politécnica de Valencia, Camino de vera s/n, 46022 Valencia, Spain e-mail: jescamilla@dsic.upv.es, msalido@dsic.upv.es, fbarber@dsic.upv.es
Miguel A. Salido
Affiliation:
Instituto de Automática e Informática Industrial, Universidad Politécnica de Valencia, Camino de vera s/n, 46022 Valencia, Spain e-mail: jescamilla@dsic.upv.es, msalido@dsic.upv.es, fbarber@dsic.upv.es
Adriana Giret
Affiliation:
Dpto. de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Camino de vera s/n, 46022 Valencia, Spain e-mail: agiret@dsic.upv.es
Federico Barber
Affiliation:
Instituto de Automática e Informática Industrial, Universidad Politécnica de Valencia, Camino de vera s/n, 46022 Valencia, Spain e-mail: jescamilla@dsic.upv.es, msalido@dsic.upv.es, fbarber@dsic.upv.es
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Abstract

Many real life problems can be modeled as a scheduling problem. The main objective of these problems is to obtain optimal solutions in terms of processing time, cost and quality. Nowadays, energy-efficiency is also taken into consideration. However, these problems are NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the classical job-shop scheduling problem. In the extended version, each operation has to be executed by one machine and this machine can work at different speed rates. The machines consume different amounts of energy to process tasks at different rates. The evaluation section shows that a powerful commercial tools for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality.

Type
Articles
Information
The Knowledge Engineering Review , Volume 31 , Special Issue 5: Constraint Satisfaction for Planning and Scheduling , November 2016 , pp. 475 - 485
Copyright
© Cambridge University Press, 2017 

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