{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T12:04:44Z","timestamp":1777291484377,"version":"3.51.4"},"reference-count":16,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>A problem of minimizing the total weighted tardiness in the preemptive single machine scheduling for discrete manufacturing is considered. A hyper-heuristic is presented, which is composed of 24 various heuristics, to find an approximately optimal schedule whenever finding the exact solution is practically intractable. The three heuristics are based on the well-known rules, whereas the 21 heuristics are introduced first. Therefore, the hyper-heuristic selects the best heuristic schedule among 24 schedule versions, whose total weighted tardiness is minimal. Each of the 24 heuristics can solely produce a schedule which is the best one for a given scheduling problem. Despite the percentage of zero gap instances decreases as the greater number of jobs is scheduled, the average and maximal gaps decrease as well. In particular, the percentage is not less than 80 % when up to 10 jobs are scheduled. The average gap calculated over nonzero gaps does not exceed 4 % in the case of scheduling 7 jobs. When manufacturing consists of hundreds of jobs, the hyper-heuristic is made an online scheduling algorithm by applying it only to a starting part of the manufacturing process.<\/jats:p>","DOI":"10.2478\/acss-2022-0001","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T06:00:42Z","timestamp":1661320842000},"page":"1-12","source":"Crossref","is-referenced-by-count":2,"title":["A Hyper-Heuristic for the Preemptive Single Machine Scheduling Problem to Minimize the Total Weighted Tardiness"],"prefix":"10.2478","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3543-3087","authenticated-orcid":false,"given":"Vadim","family":"Romanuke","sequence":"first","affiliation":[{"name":"Polish Naval Academy , Gdynia , Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"2026042709092784432_j_acss-2022-0001_ref_001","doi-asserted-by":"crossref","unstructured":"[1] Z. Wang and C. Lu, \u201cAn integrated job shop scheduling and assembly sequence planning approach for discrete manufacturing,\u201d Journal of Manufacturing Systems, vol. 61, pp. 27\u201344, Oct. 2021. https:\/\/doi.org\/10.1016\/j.jmsy.2021.08.003","DOI":"10.1016\/j.jmsy.2021.08.003"},{"key":"2026042709092784432_j_acss-2022-0001_ref_002","doi-asserted-by":"crossref","unstructured":"[2] J. C. Serrano-Ruiz, J. Mula, and R. Poler, \u201cSmart manufacturing scheduling: A literature review,\u201d Journal of Manufacturing Systems, vol. 61, pp. 265\u2013287, Oct. 2021. https:\/\/doi.org\/10.1016\/j.jmsy.2021.09.011","DOI":"10.1016\/j.jmsy.2021.09.011"},{"key":"2026042709092784432_j_acss-2022-0001_ref_003","doi-asserted-by":"crossref","unstructured":"[3] M. L. Pinedo, Scheduling: Theory, Algorithms, and Systems. Springer, 2016. https:\/\/doi.org\/10.1007\/978-3-319-26580-3","DOI":"10.1007\/978-3-319-26580-3"},{"key":"2026042709092784432_j_acss-2022-0001_ref_004","doi-asserted-by":"crossref","unstructured":"[4] R. L. Graham, E. L. Lawler, J. K. Lenstra, and A. H. G. Rinnooy Kan, \u201cOptimization and approximation in deterministic sequencing and scheduling: A survey,\u201d Annals of Discrete Mathematics, vol. 5, pp. 287\u2013326, 1979. https:\/\/doi.org\/10.1016\/S0167-5060(08)70356-X","DOI":"10.1016\/S0167-5060(08)70356-X"},{"key":"2026042709092784432_j_acss-2022-0001_ref_005","doi-asserted-by":"crossref","unstructured":"[5] J. Du and J. Y. T. Leung, \u201cMinimizing total tardiness on one machine is NP-hard,\u201d Mathematics of Operations Research, vol. 15, no. 3, pp. 483\u2013495, Aug. 1990. https:\/\/doi.org\/10.1287\/moor.15.3.483","DOI":"10.1287\/moor.15.3.483"},{"key":"2026042709092784432_j_acss-2022-0001_ref_006","doi-asserted-by":"crossref","unstructured":"[6] W. Y. Ku and J. C. Beck, \u201cMixed Integer Programming models for job shop scheduling: A computational analysis,\u201d Computers & Operations Research, vol. 73, pp. 165\u2013173, Sep. 2016. https:\/\/doi.org\/10.1016\/j.cor.2016.04.006","DOI":"10.1016\/j.cor.2016.04.006"},{"key":"2026042709092784432_j_acss-2022-0001_ref_007","doi-asserted-by":"crossref","unstructured":"[7] V. V. Romanuke, \u201cMinimal total weighted tardiness in tight-tardy single machine preemptive idling-free scheduling,\u201d Applied Computer Systems, vol. 24, no. 2, pp. 150\u2013160, Dec. 2019. https:\/\/doi.org\/10.2478\/acss-2019-0019","DOI":"10.2478\/acss-2019-0019"},{"key":"2026042709092784432_j_acss-2022-0001_ref_008","doi-asserted-by":"crossref","unstructured":"[8] P. Brucker, Scheduling Algorithms, 5th ed. Springer-Verlag Berlin Heidelberg, 2007. https:\/\/doi.org\/10.1007\/978-3-540-69516-5","DOI":"10.1007\/978-3-540-69516-5"},{"key":"2026042709092784432_j_acss-2022-0001_ref_009","doi-asserted-by":"crossref","unstructured":"[9] M. Batsyn, B. Goldengorin, P. Pardalos, and P. Sukhov, \u201cOnline heuristic for the preemptive single machine scheduling problem of minimizing the total weighted completion time,\u201d Optimization Methods & Software, vol. 29, no. 5, pp. 955\u2013963, 2014. https:\/\/doi.org\/10.1080\/10556788.2013.854360","DOI":"10.1080\/10556788.2013.854360"},{"key":"2026042709092784432_j_acss-2022-0001_ref_010","doi-asserted-by":"crossref","unstructured":"[10] S. Haruhiko and S. Hiroaki, Online Scheduling in Manufacturing: A Cumulative Delay Approach. Springer-Verlag London, 2013. https:\/\/doi.org\/10.1007\/978-1-4471-4561-5","DOI":"10.1007\/978-1-4471-4561-5"},{"key":"2026042709092784432_j_acss-2022-0001_ref_011","doi-asserted-by":"crossref","unstructured":"[11] M. C. Georgiadis, A. A. Levis, P. Tsiakis, I. Sanidiotis, C. C. Pantelides, and L. G. Papageorgiou, \u201cOptimisation-based scheduling: A discrete manufacturing case study,\u201d Computers & Industrial Engineering, vol. 49, no. 1, pp. 118\u2013145, Aug. 2005. https:\/\/doi.org\/10.1016\/j.cie.2005.02.004","DOI":"10.1016\/j.cie.2005.02.004"},{"key":"2026042709092784432_j_acss-2022-0001_ref_012","doi-asserted-by":"crossref","unstructured":"[12] M. Aicardi, A. Di Febbraro, and R. Minciardi, \u201cCombined scheduling and routing in discrete manufacturing systems,\u201d IFAC Proceedings Volumes, vol. 23, no. 3, pp. 671\u2013676, Sep. 1990. https:\/\/doi.org\/10.1016\/S1474-6670(17)52637-3","DOI":"10.1016\/S1474-6670(17)52637-3"},{"key":"2026042709092784432_j_acss-2022-0001_ref_013","doi-asserted-by":"crossref","unstructured":"[13] F. Jaramillo and M. Erkoc, \u201cMinimizing total weighted tardiness and overtime costs for single machine preemptive scheduling,\u201d Computers & Industrial Engineering, vol. 107, pp. 109\u2013119, May 2017. https:\/\/doi.org\/10.1016\/j.cie.2017.03.012","DOI":"10.1016\/j.cie.2017.03.012"},{"key":"2026042709092784432_j_acss-2022-0001_ref_014","doi-asserted-by":"crossref","unstructured":"[14] B. Goldengorin and V. Romanuke, \u201cOnline heuristic for the preemptive single machine scheduling problem to minimize the total weighted tardiness,\u201d Computers & Industrial Engineering, vol. 155, May 2021, Art no. 107090. https:\/\/doi.org\/10.1016\/j.cie.2020.107090","DOI":"10.1016\/j.cie.2020.107090"},{"key":"2026042709092784432_j_acss-2022-0001_ref_015","doi-asserted-by":"crossref","unstructured":"[15] R. Panneerselvam, \u201cSimple heuristic to minimize total tardiness in a single machine scheduling problem,\u201d The International Journal of Advanced Manufacturing Technology, vol. 30, no. 7\u20138, pp. 722\u2013726, 2006. https:\/\/doi.org\/10.1007\/s00170-005-0102-1","DOI":"10.1007\/s00170-005-0102-1"},{"key":"2026042709092784432_j_acss-2022-0001_ref_016","doi-asserted-by":"crossref","unstructured":"[16] B. Goldengorin and V. Romanuke, \u201cExperimental analysis of tardiness in preemptive single machine scheduling,\u201d Expert Systems with Applications, vol. 186, Dec. 2021, Art no. 114947. https:\/\/doi.org\/10.1016\/j.eswa.2021.114947","DOI":"10.1016\/j.eswa.2021.114947"}],"container-title":["Applied Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/reference-global.com\/pdf\/10.2478\/acss-2022-0001","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T11:27:26Z","timestamp":1777289246000},"score":1,"resource":{"primary":{"URL":"https:\/\/reference-global.com\/article\/10.2478\/acss-2022-0001"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,1]]},"references-count":16,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,8,23]]},"published-print":{"date-parts":[[2022,6,1]]}},"alternative-id":["10.2478\/acss-2022-0001"],"URL":"https:\/\/doi.org\/10.2478\/acss-2022-0001","relation":{},"ISSN":["2255-8691"],"issn-type":[{"value":"2255-8691","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,1]]}}}