000 04212nam a22006135i 4500
001 978-3-031-71507-5
003 DE-He213
005 20251030113306.0
007 cr nn 008mamaa
008 241110s2024 sz | s |||| 0|eng d
020 _a9783031715075
_9978-3-031-71507-5
024 7 _a10.1007/978-3-031-71507-5
_2doi
050 4 _aTS155-194
072 7 _aKJMV
_2bicssc
072 7 _aBUS087000
_2bisacsh
072 7 _aKJMV
_2thema
100 1 _aVanhoucke, Mario.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987292
245 1 2 _aA Quest for Projects with Scarce Resources
_h[electronic resource] :
_bSeeking Schedule Intelligence Through Project Data Discovery /
_cby Mario Vanhoucke.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXXIII, 161 p. 40 illus., 29 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aBusiness Guides on the Go,
_x2731-4766
505 0 _aDo We Have Enough Project Data? (Part 1) -- Do the Projects Exist in Reality? -- Do We Have Good Schedules for the Projects? -- Can We Solve Every Project Instance? -- Why Is the RCPSP So Difficult? (Part 1) -- Can We Make the Projects Easier? (Part 1) -- Why Is the RCPSP So Difficult? (Part 2) -- Can We Make the Projects Easier? (Part 2) -- How to Make the Project Data Practically Relevant? -- Do We Have Enough Project Data? (Part 2).
520 _aBased on the shared journey of two researchers, this book explores enhancing algorithms for the resource-constrained project scheduling problem. It examines the search for and significance of project data from multiple, distinct perspectives. In the first part, the quest for project data is presented as a continuous exploration of the complexity of the resource-constrained project scheduling problem. This quest is pursued by solving this challenging problem with the aid of state-of-the-art algorithms from the literature, each time gaining a deeper understanding of its challenging nature. To provide insights into the problem’s complexity, project data is created, manipulated, and analyzed in depth to make current projects easier or harder to schedule. This challenging quest for project data has resulted in new project databases for academic research, new ways of testing future algorithms, and insights into how to improve future algorithms to solve this project scheduling problem with limited resources. In turn, the second part discusses the relevance of project data, demonstrating to the reader the importance of the academic research presented in the first part for the professional world. It shows how project data can be used to calibrate real project data, leading to improved decision-making, e.g. for project scheduling, forecasting, and risk analysis. The book extends a warm invitation to academics and practitioners alike, as fellow seekers of knowledge, to enhance their project management skills.
650 0 _aProduction management.
_967141
650 0 _aProject management.
_961110
650 0 _aOperations research.
_957943
650 0 _aBig data.
_957216
650 0 _aAlgorithms.
_958677
650 0 _aQuantitative research.
650 1 4 _aOperations Management.
_967304
650 2 4 _aProject Management.
_961110
650 2 4 _aOperations Research and Decision Theory.
_967143
650 2 4 _aBig Data.
_957216
650 2 4 _aAlgorithms.
_958677
650 2 4 _aData Analysis and Big Data.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031715068
776 0 8 _iPrinted edition:
_z9783031715082
776 0 8 _iPrinted edition:
_z9783031715099
830 0 _aBusiness Guides on the Go,
_x2731-4766
_968573
856 4 0 _uhttps://doi.org/10.1007/978-3-031-71507-5
912 _aZDB-2-BUM
912 _aZDB-2-SXBM
999 _c64846
_d64846