04212nam a22006135i 4500 978-3-031-71507-5 DE-He213 20251030113306.0 cr nn 008mamaa 241110s2024 sz | s |||| 0|eng d 9783031715075 978-3-031-71507-5 10.1007/978-3-031-71507-5 doi TS155-194 KJMV bicssc BUS087000 bisacsh KJMV thema Vanhoucke, Mario. author. aut http://id.loc.gov/vocabulary/relators/aut 87292 A Quest for Projects with Scarce Resources [electronic resource] : Seeking Schedule Intelligence Through Project Data Discovery / by Mario Vanhoucke. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XXIII, 161 p. 40 illus., 29 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Business Guides on the Go, 2731-4766 Do 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). Based 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. Production management. 67141 Project management. 61110 Operations research. 57943 Big data. 57216 Algorithms. 58677 Quantitative research. Operations Management. 67304 Project Management. 61110 Operations Research and Decision Theory. 67143 Big Data. 57216 Algorithms. 58677 Data Analysis and Big Data. SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031715068 Printed edition: 9783031715082 Printed edition: 9783031715099 Business Guides on the Go, 2731-4766 68573 https://doi.org/10.1007/978-3-031-71507-5 ZDB-2-BUM ZDB-2-SXBM 64846 64846