Traditionally, the three most important manufacturing functions are process planning, scheduling, and due-date assignment, which are handled sequentially and separately.This book integrates these manufacturing processes and functions to increase global performance along with manufacturing and production cost savings.
Integrated Process Planning, Scheduling, and Due-Date Assignment combines the most important manufacturing functions to use manufacturing resources better, reduce production costs, and eliminate bottlenecks with increased production efficiency. The book covers how the integration will help eliminate scheduling conflicts and how to adapt to irregular shop floor disturbances. It also explains how other elements, such as tardiness and earliness, are penalized and how prioritizing helps improve weight performance function.
This book will draw the interest of professionals, students, and academicians in process planning, scheduling, and due-date assignment. It could also be supplemental material for manufacturing courses in industrial engineering and manufacturing engineering departments.
Traditionally, the three most important manufacturing functions are process planning, scheduling, and due-date assignment, which are handled sequentially and separately. This book integrates these manufacturing processes and functions to increase global performance along with manufacturing and production cost savings.
1. Introduction to Integrated Process Planning, Scheduling, and Due-Date
Assignment.
2. Process Planning.
3. Scheduling in Manufacturing.
4.
Due-Date Assignment.
5. Integrated Process Planning and Scheduling.
6.
Dynamic Integrated Process Planning and Scheduling.
7. Scheduling with
Due-Date Assignment.
8. Scheduling with Due-Window Assignment.
9.
Integrated Process Planning, Scheduling, and Due-Date Assignment.
10.
Dynamic Integrated Process Planning, Scheduling, and Due-Date Assignment.
11. Solution Techniques in Integrated Manufacturing Functions.
12.
Integrated Process Planning, Scheduling, Due-Date Assignment, and Delivery.
H.I. Demir completed his undergraduate education at Bilkent University, Industrial Engineering Department, with a full scholarship in Ankara, Türkiye. He completed his master's degree in Industrial Engineering at Lehigh University, Bethlehem, USA, with a full scholarship from the Ministry of National Education of Türkiye. He started his Ph.D. in Industrial Engineering at Northeastern University, Boston, USA, and completed his courses there. After returning to Türkiye, he finished his Ph.D. thesis on "Integrated Process Planning, Scheduling, and Due-date Assignment" at Sakarya University. He is still working as an Assistant Professor at Sakarya University. His research interests include optimization, simulation, scheduling, decision theory, multicriteria decision-making, and meta-heuristic algorithms.
A.H. Kökēam completed his master's degree at Sakarya University, Department of Industrial Engineering, in 2010 with a study on fuzzy project scheduling using meta-heuristic methods. He received his Ph.D. from Sakarya University, Department of Industrial Engineering, in 2017. In his doctoral dissertation, he worked on an alternative model to evaluate school success in the entrance examination for higher education. He works as an assistant professor at the Sakarya University Industrial Engineering Department. His research interests include meta-heuristic methods, artificial intelligence, fuzzy logic, optimization, and scheduling.
C. Erden is currently an assistant professor at the Sakarya University of Applied Sciences and a senior researcher at the AI Research and Application Center, Sakarya University of Applied Sciences, Sakarya, Türkiye. He worked as a research assistant in industrial engineering at Sakarya University and a researcher at Sakarya University AI Systems Application and Research between 2012-2020. He received his Ph.D. from Sakarya University in the field of industrial engineering. His research interests include scheduling, discrete event simulation, meta-heuristic algorithms, modeling and optimization, machine learning, and deep learning.
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