[EN] Manufacturing industries are faced with environmental challenges, so their industrial processes must be
optimized in terms of both profitability and sustainability. Since most of these processes are dynamic, the
previously obtained solutions cannot be valid after disruptions. This paper focuses on recovery in dynamic
job-shop scheduling problems where machines can work at different rates. Machine speed scaling
is an alternative framework to the on/off control framework for production scheduling. Thus, given a
disruption, the main goal is to recover the original solution by rescheduling the minimum number of
tasks. To this end, a new match-up technique is developed to determine the rescheduling zone and a
feasible reschedule. Then, a memetic algorithm is proposed for finding a schedule that minimizes the
energy consumption within the rescheduling zone but that also maintains the makespan constraint. An
extensive study is carried out to analyze the behavior of our algorithms to recover the original solution
and minimize the energy reduction in different benchmarks, which are taken from the OR-Library. The
energy consumption and processing time of the tasks involved in the rescheduling zone will play an
important role in determining the best match-up point and the optimized rescheduling. Upon a
disruption, different rescheduling solutions can be obtained, all of which comply with the requirements
but that have different values of energy consumption. The results proposed in this paper may be useful
for application in real industries for energy-efficient production rescheduling.This research has been supported by the Seventh Framework Programme under the research project TETRACOM-GA609491 and the Spanish Government under research projects TIN2013-46511-C2-1, TIN2015-65515-C4-1-R and TIN2016-80856-R. The authors wish to thank reviewers and editors for their positive comments to improve the quality of the paper.Salido Gregorio, MA.; Escamilla Fuster, J.; Barber Sanchís, F.; Giret Boggino, AS. (2017). Rescheduling in job-shop problems for sustainable manufacturing systems. Journal of Cleaner Production. 162(20):121-132. https://doi.org/10.1016/j.jclepro.2016.11.002S1211321622
