Supplementary data for the article: Time/sequence-dependent scheduling: the design and evaluation of a general purpose tabu-based adaptive large neighbourhood search algorithm

  • L. He (Creator)
  • M.M. de Weerdt (Creator)
  • N. Yorke-Smith (Creator)
  • El-Houssaine Aghezzaf (Creator)
  • B. Cesaret (Creator)
  • Ceyda Oguz (Creator)
  • Sibel Salman (Creator)
  • Pieter Vansteenwegen (Creator)
  • Cedric Verbeeck (Creator)

Dataset

Description

In intelligent manufacturing, it is important to schedule orders from customers efficiently. Make-to-order companies may have to reject or postpone orders when the production capacity does not meet the demand. Many such real-world scheduling problems are characterised by processing times being dependent on the start time (time dependency) or on the preceding orders (sequence dependency), and typically have an earliest and latest possible start time. We introduce and analyze four algorithmic ideas for this class of time/sequence-dependent over-subscribed scheduling problems with time windows: a novel hybridization of adaptive large neighbourhood search (ALNS) and tabu search (TS), a new randomization strategy for neighbourhood operators, a partial sequence dominance heuristic, and a fast insertion strategy.
This dataset contains the benchmark data for three domains to test the proposed algorithm—an order acceptance and scheduling problem, a real-world multi-orbit agile Earth observation satellite scheduling problem, and a time-dependent orienteering problem with time windows. The source codes of proposed algorithm are available at: https://doi.org/10.4121/uuid:3a23b216-3762-4a61-ba2c-d3df6dc53268.
If you use this data, please cite the following paper: He L , De Weerdt M , Yorke-Smith N. Time/sequence-dependent scheduling: the design and evaluation of a general purpose tabu-based adaptive large neighbourhood search algorithm[J]. Journal of Intelligent Manufacturing, 2019, DOI: 10.1007/s10845-019-01518-4.
Date made available2019
PublisherTU Delft - 4TU.ResearchData
Date of data production2019

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