@article{10902/32752,
year = {2023},
month = {12},
url = {https://hdl.handle.net/10902/32752},
abstract = {Uncertainty pervades real life and supposes a challenge for all industrial processes as it makes it difficult to predict the outcome of otherwise risk-free activities. In particular, time deviation from projected objectives is one of the main sources of economic losses in manufacturing, not only for the delay in production but also for the energy consumed by the equipment during the additional unexpected time they have to work to complete their labour. In this work we deal with uncertainty in the flexible job shop, one of the foremost scheduling problems due to its practical applications. We show the importance of a good model to avoid introducing unwanted imprecision and producing artificially pessimistic solutions. In our model, the total energy is decomposed into the energy required by resources when they are actively processing an operation and the energy consumed by these resources simply for being switched on. We propose a set of metrics and carry out an extensive experimental analysis that compares our proposal with the more straightforward alternative that directly translates the deterministic model. We also define a local search neighbourhood and prove that it can reach an optimal solution starting from any other solution. Results show the superiority of the new model and the good performance of the new neighbourhood.},
organization = {Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has been supported by the Spanish Government under research Grants PID2019-106263RB-I00 and TED2021-131938B-I00 and by Universidad de Cantabria and the Government of Cantabria under Grant Concepción Arenal UC-20-20.},
publisher = {Springer},
publisher = {Natural Computing, 2023, 22(4), 685-704},
title = {Neighbourhood search for energy minimisation in flexible job shops under fuzziness},
author = {García Gómez, Pablo and Vela, Camino R. and González Rodríguez, Inés},
}