Publication:
Application of a genetic algorithm for multi-item inventory lot-sizing with supplier selection under quantity discount and lead time

Issued Date

2020-01-01

Resource Type

Article

ISSN

17457653
17457645

DOI

10.1504/IJOR.2020.107540

Other identifier(s)

2-s2.0-85086044071

Rights

Mahidol University

Rights Holder(s)

SCOPUS

Bibliographic Citation

International Journal of Operational Research. Vol.38, No.3 (2020), 403-421

Suggested Citation

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. International Journal of Operational Research. Vol.38, No.3 (2020), 403-421. doi:10.1504/IJOR.2020.107540 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/57846

Research Projects

Organizational Units

Authors

Journal Issue

Thesis

Title

Application of a genetic algorithm for multi-item inventory lot-sizing with supplier selection under quantity discount and lead time

Author(s)

Sunan Klinmalee
 Thanakorn Naenna
 Chirawat Woarawichai

Other Contributor(s)

Rajamangala University of Technology Krungthep
 Mahidol University

Abstract

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.

Keyword(s)

Decision Sciences

Availability

URI

https://repository.li.mahidol.ac.th/handle/20.500.14594/57846

Collections

Scopus 2020