To improve the Makespan of a Standard Job Shop Scheduling problem incorporating GA by using Python / (Record no. 611378)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01693nam a22001697a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUST |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 670 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | YOUSAF, SAMRA |
| 245 ## - TITLE STATEMENT | |
| Title | To improve the Makespan of a Standard Job Shop Scheduling problem incorporating GA by using Python / |
| Statement of responsibility, etc. | SAMRA YOUSAF |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad ; |
| Name of producer, publisher, distributor, manufacturer | SMME-NUST |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 87p. ; |
| Other physical details | softcopy , |
| Dimensions | 30cm. |
| 500 ## - GENERAL NOTE | |
| General note | Effective job scheduling is crucial in industrial manufacturing planning, where each job, consisting of multiple operations, must be allocated to the machines that are available machines for processing. Each job has a specific interval, and every machine can only handle one operation at a time. Efficient job allocation is essential to minimise the makespan and reduce machine idle time. In Job Shop Scheduling (JSS), job operations follow a specified order. Genetic Algorithms (GA) have emerged as a popular heuristic for tackling various scheduling problems. This study introduces a Genetic Algorithm Integrating Python (GAIP) with feasibility-preserving solution representation, initialization, and operators tailored for the JSS problem. The proposed GAIP achieves the best-known results with high success rates on the Muth and Thomson and Lawrence benchmark datasets. Experimental results demonstrate the GA's rapid convergence towards optimal solutions. Incorporating GA with local search and two selection methods at the same time is done to further enhance solution quality and success rates. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Design and Manufacturing Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor: DR. SHAHID IKRAMULLAH BUTT |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/46044">http://10.250.8.41:8080/xmlui/handle/123456789/46044</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 09/04/2024 | 670 | SMME-TH-1059 | Thesis |