Vimala Rani, M and Mathirajan, M (2021) Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces. [Book Chapter]
Full text not available from this repository.Abstract
This study presents prescriptive analytics to optimally schedule (a) single diffusion furnace, and (b) non-identical parallel diffusion furnaces with machine eligibility restrictions and jobs having different job-arrival times, belonging to different job families, and having non-agreeable release times & due-dates. We also considered real time dynamic events w.r.t. job and resources. Accordingly, we first propose (0-1) mixed integer linear programming (MILP) models to optimize customer perspectives objectives for the scheduling problem considered in this study. Due to the computational difficulty in obtaining optimal value for the customer perspectives objectives, particularly for large-scale data in scheduling diffusion furnace(s), this study presents seven versions of the greedy heuristic algorithm (GHA) considering seven different Apparent Tardiness Cost (ATC) rules. These proposed seven versions of GHA is applied for (i) single diffusion furnace and (ii) non-identical parallel diffusion furnaces with machine eligibility restriction. Further, the empirical evaluation of the proposed seven versions of ATC-GHA is carried out in comparison with the (a) optimal solution for small-scale data and (b) estimated optimal solution for large-scale data. Further, this study conducts statistical evaluation by carrying out descriptive statistics and Kruskal Wallis test. From both the analyses, this study identifies the better performing variants of ATC-GHA. © 2021, Springer Nature Switzerland AG.
| Item Type: | Book Chapter |
|---|---|
| Publication: | International Series in Operations Research and Management Science |
| Series.: | International Series in Operations Research & Management Science |
| Publisher: | Springer |
| Additional Information: | The copyright for this article belongs to Springer |
| Department/Centre: | Division of Interdisciplinary Sciences > Management Studies |
| Date Deposited: | 28 Nov 2021 09:32 |
| Last Modified: | 28 Nov 2021 09:32 |
| URI: | http://eprints.iisc.ac.in/id/eprint/69972 |
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