Szczegóły
Tytuł artykułu
Evolutionary Algorithm for Minmax Regret Flow-Shop ProblemTytuł czasopisma
Management and Production Engineering ReviewRocznik
2015Numer
No 3Autorzy
Słowa kluczowe
Wydział IV Nauk TechnicznychWydział PAN
Wydział IV Nauk TechnicznychWydawca
Production Engineering Committee of the Polish Academy of Sciences, Polish Association for Production ManagementData
2015[2015.01.01 AD - 2015.12.31 AD]Identyfikator
DOI: 10.1515/mper-2015-0021 ; ISSN 2080-8208 ; eISSN 2082-1344Źródło
Management and Production Engineering Review; 2015; No 3Referencje
Nawaz (1983), A heuristic algorithm for the m - machine n - job flow - shop sequencing problem, The International Journal of Management Science, 11. ; Aissi (2009), Min - max and min - max regret versions of combinatorial optimization problems survey, European Journal of Operational Research, 197. ; Kasperski (2008), - approximation algorithm for interval data minmax regret sequencing problems with the total flow time criterion, Operations Research Letters, 42. ; Averbakh (2000), Minimax regret solutions for minimax optimization problems with uncertainty, European Journal of Operational Research, 27, 57. ; Lebedev (2006), Complexity of minimizing the total flow time with interval data and minmax regret criterion, Discrete Applied Mathematics, 154. ; Dutt (2013), Handling of Uncertainty - A Survey of Scientific and Research Publications, International Journal, 3. ; Conde (2010), - approximation for minmax regret problems via a mid - point scenario optimal solution, Operations Research Letters, 38, 326, doi.org/10.1016/j.orl.2010.03.002 ; Averbakh (2006), The minmax regret permutation flowshop problem with two jobs, Operations Research Letters, 69. ; Siepak (2014), Solution algorithms for unrelated machines minmax regret scheduling problem with interval processing times and the total flow time criterion, Annals of Operations Research, 222. ; Garey (1976), The complexity of flowshop and jobshop scheduling, Mathematics of Operations Research, 1.O czasopiśmie
MISSION STATEMENT Management and Production Engineering Review (MPER) is a peer-refereed, international, multidisciplinary journal covering a broad spectrum of topics in production engineering and management. Production engineering is a currently developing stream of science encompassing planning, design, implementation and management of production and logistic systems. Orientation towards human resources factor differentiates production engineering from other technical disciplines. The journal aims to advance the theoretical and applied knowledge of this rapidly evolving field, with a special focus on production management, organisation of production processes, manage- ment of production knowledge, computer integrated management of production flow, enterprise effectiveness, maintainability and sustainable manufacturing, productivity and organisation, forecasting, modelling and simu- lation, decision making systems, project management, innovation management and technology transfer, quality engineering and safety at work, supply chain optimization and logistics. Management and Production Engineering Review is published under the auspices of the Polish Academy of Sciences Committee on Production Engineering and Polish Association for Production Management. The main purpose of Management and Production Engineering Review is to publish the results of cutting- edge research advancing the concepts, theories and implementation of novel solutions in modern manufacturing. Papers presenting original research results related to production engineering and management education are also welcomed. We welcome original papers written in English. The Journal also publishes technical briefs, discussions of previously published papers, book reviews, and editorials. Letters to the Editor-in-Chief are highly encouraged.SUBMISSION Papers for submission should be prepared according to the Authors Instructions available at: www.journals.pan.pl/mper
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Indeksowanie w bazach
Index CopernicusWeb of Science - Clarivate (ESCI)
Scopus - Elsevier
SCIMAGO:
(CiteScore 2020 - 2.5
SJR 2020 - 0.332
SNIP 2020 - 1.061)