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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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Problem instances for scheduling jobs with time windows on unrelated parallel machines

Authors: Tadumadze, Giorgi; Emde, Simon; Diefenbach, Heiko;

Problem instances for scheduling jobs with time windows on unrelated parallel machines

Abstract

The following dataset contains problem instances for the unrelated machine scheduling problem with job release dates and deadlines, which are used in the article "Tadumadze, G., Emde, S. & Diefenbach, H. Exact and heuristic algorithms for scheduling jobs with time windows on unrelated parallel machines. OR Spectrum 42, 461–497 (2020). https://doi.org/10.1007/s00291-020-00586-w". The problem instances are stored in table “instances”, where the columns of the table can be interpreted as follows: Problem_ID: <Autonumber> n: <number of jobs>; m: <number of machines>; w: <vector with n elements: the j-th element corresponds to the weight of job j>; r: <vector with n elements: the j-th element corresponds to the release date of job j>; d: <vector with n elements: the j-th element corresponds to the deadline of job j>; p: <n*m matrix: each entry in j-th column and i-th row corresponds to the processing time of job j on machine i>; The first 80 entries (Problem_ID between 1-80), contain discrete Berth-allocation problem instances, provided by “Jean-François Cordeau, Gilbert Laporte, Pasquale Legato, Luigi Moccia, (2005) Models and Tabu Search Heuristics for the Berth-Allocation Problem. Transportation Science 39(4):526-538. https://doi.org/10.1287/trsc.1050.0120” and additionally contain machine availability times, which are stored in the following columns: s: <vector with m elements: the i-th element corresponds to the start availability time of machine i>; e: <vector with m elements: the i-th element corresponds to the end availability time of machine i>; The following 270 entries (Problem_ID between 81-270) contain newly generated random problem instances with the instance generation scheme proposed by “Nicholas G. Hall, Marc E. Posner, (2001) Generating Experimental Data for Computational Testing with Machine Scheduling Applications. Operations Research 49(6):854-865. https://doi.org/10.1287/opre.49.6.854.10014”. The first 10 instances (Problem_ID between 81-90) are used for parameter tuning tests and the next 180 (Problem_ID between 91-270) instances for computational performance comparison. The last 30 entries (Problem_ID between 271-300) contain integrated truck and workforce scheduling problem instances with fixed workforce at each door, provided by “Giorgi Tadumadze, Nils Boysen, Simon Emde, Felix Weidinger (2019) Integrated truck and workforce scheduling to accelerate the unloading of trucks. European Journal of Operational Research 278(1):343-362. https://doi.org/10.1016/j.ejor.2019.04.024”. The detailed computational results for each instance, approach and objective function are reported in tables which are named with the following convention: "results_<approach>_<objective value>”.

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Keywords

Machine scheduling; Unrelated parallel machines; Time windows

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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