Application of a genetic algorithm for multi-item inventory lot-sizing with supplier selection under quantity discount and lead time
Copyright © 2020 Inderscience Enterprises Ltd. This study presents an application of genetic algorithm (GA) for solving the multi-item inventory lot-sizing problem with supplier selection under discounts and lead time constraints. A mixed-integer linear programming (MILP) model is developed for prop...
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th-mahidol.578462020-08-25T16:39:53Z Application of a genetic algorithm for multi-item inventory lot-sizing with supplier selection under quantity discount and lead time Sunan Klinmalee Thanakorn Naenna Chirawat Woarawichai Rajamangala University of Technology Krungthep Mahidol University Decision Sciences Copyright © 2020 Inderscience Enterprises Ltd. This study presents an application of genetic algorithm (GA) for solving the multi-item inventory lot-sizing problem with supplier selection under discounts and lead time constraints. A mixed-integer linear programming (MILP) model is developed for proposed problem. To solve the problem, a genetic algorithm (GA) with two additional operations is proposed for handling the effect of the problem size. An adaptor for adjusting a chromosome data before the evaluation process and a penalty step for deterring an infeasible solution are developed. Finally, numerical examples are generated to evaluate the performance of the proposed GA, and the comparison with MILP approach about the solution quality and time is presented. 2020-08-25T09:39:53Z 2020-08-25T09:39:53Z 2020-01-01 Article International Journal of Operational Research. Vol.38, No.3 (2020), 403-421 10.1504/IJOR.2020.107540 17457653 17457645 2-s2.0-85086044071 https://repository.li.mahidol.ac.th/handle/123456789/57846 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086044071&origin=inward |
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Decision Sciences Sunan Klinmalee Thanakorn Naenna Chirawat Woarawichai Application of a genetic algorithm for multi-item inventory lot-sizing with supplier selection under quantity discount and lead time |
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Copyright © 2020 Inderscience Enterprises Ltd. This study presents an application of genetic algorithm (GA) for solving the multi-item inventory lot-sizing problem with supplier selection under discounts and lead time constraints. A mixed-integer linear programming (MILP) model is developed for proposed problem. To solve the problem, a genetic algorithm (GA) with two additional operations is proposed for handling the effect of the problem size. An adaptor for adjusting a chromosome data before the evaluation process and a penalty step for deterring an infeasible solution are developed. Finally, numerical examples are generated to evaluate the performance of the proposed GA, and the comparison with MILP approach about the solution quality and time is presented. |
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Rajamangala University of Technology Krungthep |
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Rajamangala University of Technology Krungthep Sunan Klinmalee Thanakorn Naenna Chirawat Woarawichai |
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Article |
author |
Sunan Klinmalee Thanakorn Naenna Chirawat Woarawichai |
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Sunan Klinmalee |
title |
Application of a genetic algorithm for multi-item inventory lot-sizing with supplier selection under quantity discount and lead time |
title_short |
Application of a genetic algorithm for multi-item inventory lot-sizing with supplier selection under quantity discount and lead time |
title_full |
Application of a genetic algorithm for multi-item inventory lot-sizing with supplier selection under quantity discount and lead time |
title_fullStr |
Application of a genetic algorithm for multi-item inventory lot-sizing with supplier selection under quantity discount and lead time |
title_full_unstemmed |
Application of a genetic algorithm for multi-item inventory lot-sizing with supplier selection under quantity discount and lead time |
title_sort |
application of a genetic algorithm for multi-item inventory lot-sizing with supplier selection under quantity discount and lead time |
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2020 |
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https://repository.li.mahidol.ac.th/handle/123456789/57846 |
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1763492827870789632 |