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Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces

In: Supply Chain Management in Manufacturing and Service Systems

Author

Listed:
  • M. Vimala Rani

    (Indian Institute of Technology, Kharagpur)

  • M. Mathirajan

    (Indian Institute of Science)

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.

Suggested Citation

  • M. Vimala Rani & M. Mathirajan, 2021. "Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces," International Series in Operations Research & Management Science, in: Sharan Srinivas & Suchithra Rajendran & Hans Ziegler (ed.), Supply Chain Management in Manufacturing and Service Systems, pages 241-278, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-69265-0_9
    DOI: 10.1007/978-3-030-69265-0_9
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