| contributor author | Li, S.;Butterfield, J.;Murphy, A. | |
| date accessioned | 2023-04-06T12:53:26Z | |
| date available | 2023-04-06T12:53:26Z | |
| date copyright | 12/12/2022 12:00:00 AM | |
| date issued | 2022 | |
| identifier issn | 15309827 | |
| identifier other | jcise_23_3_034502.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4288712 | |
| description abstract | The aim of this work is to enable a step towards a selfadapting digital toolset for manufacturing planning focusing on minimally constrained assembly line balancing. The approach includes the simultaneous definition of the optimum number of workstations, the optimum cycle time and the assignment of tasks to workstations. A bespoke genetic algorithm (GENALSAS) is proposed and demonstrated which focuses on examining the simple assembly line balancing problem (SALBP). The proposed genetic algorithm (GA) has been shown to consistently deliver detailed production plans for SALBP problem forms with minimum inputs. Neither the number of workstations nor the system cycle time is assumed/fixed as in previous work in the field. The work simultaneously attains better performing solutions compared with previous studies both in terms of time to converge and the quality of the solution. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | A New MultiObjective Genetic Algorithm for Assembly Line Balancing | |
| type | Journal Paper | |
| journal volume | 23 | |
| journal issue | 3 | |
| journal title | Journal of Computing and Information Science in Engineering | |
| identifier doi | 10.1115/1.4055426 | |
| journal fristpage | 34502 | |
| journal lastpage | 3450210 | |
| page | 10 | |
| tree | Journal of Computing and Information Science in Engineering:;2022:;volume( 023 ):;issue: 003 | |
| contenttype | Fulltext | |
