Mathirajan, M and Ramasubramanian, M (2019) Efficient Heuristic Solution Methodologies for Scheduling Batch Processor with Incompatible Job-Families, Non-identical Job-Sizes and Non-identical Job-Dimensions. In: IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2019, 1 - 5 September 2019, Austin, pp. 212-222.
Full text not available from this repository.Abstract
Efficient scheduling of heat-treatment furnace (HTF), a batch processor (BP), is very important to meet both throughput benefits as well as the committed due date to the customer, as the heat-treatment operations require very long processing time in the entire steel casting manufacturing process and accounts for large part of the total casting processing time required. In the recent time, there are good number of studies reported in the literature related to scheduling of BP associated with many discrete parts manufacturing. However, still there is very scant treatment has been given in scheduling of HTF problem, which has one of the unique job-characteristic: Non-identical job-dimensions. This characteristic differentiates most of the other BP problems reported in the literature. Thus, this study considers a scheduling HTF, close to real-life problem characteristics, and proposes efficient heuristic solution methodologies.
| Item Type: | Conference Paper |
|---|---|
| Publication: | IFIP Advances in Information and Communication Technology |
| Publisher: | Springer New York LLC |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Genetic algorithms; Heat treating furnaces; Heat treatment; Heuristic algorithms; Heuristic methods; Industrial management; Manufacture; Scheduling; Scheduling algorithms; Throughput, Efficient scheduling; Heat treatment furnaces; Heuristic solutions; Incompatible job families; Lower bounds; Manufacturing process; Non-identical; Non-identical job sizes, Job shop scheduling |
| Department/Centre: | Division of Interdisciplinary Sciences > Management Studies |
| Date Deposited: | 25 Oct 2022 11:50 |
| Last Modified: | 25 Oct 2022 11:50 |
| URI: | https://eprints.iisc.ac.in/id/eprint/77582 |
Actions (login required)
 |
View Item |
