### Abstract

The competitive environment of global markets has forced many manufacturers to select the most appropriate supply chain network (SCN) for reduction of total costs and wasted time. Cost reduction and selection of the appropriate length of each period are two important factors in the competitive market that are often not addressed comprehensively by researchers. In our study, we proposed genetic algorithms (GAs) for optimising a novel mathematical model of the defective goods supply chain network (DGSCN). In the proposed model, we assumed that all imperfect-quality products are not repairable, whereas those considered as scrap are directly sold to customers at a low price. The objective of the proposed model is to minimise the costs of production, distribution, holding and backorder. In addition to minimising the costs, the model can determine the economic production quantity (EPQ), the appropriate length of each cycle (ALOEC) and the quantities of defective products, scrap products and retailer shortages using Just-In-Time logistics (JIT-L). We used the GAs and a Cplex solver with probability parameters and various dimensions for validation of the studied model in real-life situations, and we compared the outputs to demonstrate the performance of the model. Additionally, to identify the appropriate length of each cycle (ALOEC), we needed to solve the model using exact parameters and same dimensions and prefer to use Lingo for this application.

Original language | English |
---|---|

Pages (from-to) | 1534-1547 |

Number of pages | 14 |

Journal | Applied Mathematical Modelling |

Volume | 38 |

Issue number | 4 |

DOIs | |

Publication status | Published - 15 Feb 2014 |

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### Keywords

- Cplex
- Genetic algorithm
- Length of each-cycle
- Lingo
- Supply chain

### ASJC Scopus subject areas

- Applied Mathematics
- Modelling and Simulation

### Cite this

**A genetic algorithm for optimizing defective goods supply chain costs using JIT logistics and each-cycle lengths.** / Ghasimi, Salah Alden; Ramli, Rizauddin; Saibani, Nizaroyani.

Research output: Contribution to journal › Article

*Applied Mathematical Modelling*, vol. 38, no. 4, pp. 1534-1547. https://doi.org/10.1016/j.apm.2013.08.023

}

TY - JOUR

T1 - A genetic algorithm for optimizing defective goods supply chain costs using JIT logistics and each-cycle lengths

AU - Ghasimi, Salah Alden

AU - Ramli, Rizauddin

AU - Saibani, Nizaroyani

PY - 2014/2/15

Y1 - 2014/2/15

N2 - The competitive environment of global markets has forced many manufacturers to select the most appropriate supply chain network (SCN) for reduction of total costs and wasted time. Cost reduction and selection of the appropriate length of each period are two important factors in the competitive market that are often not addressed comprehensively by researchers. In our study, we proposed genetic algorithms (GAs) for optimising a novel mathematical model of the defective goods supply chain network (DGSCN). In the proposed model, we assumed that all imperfect-quality products are not repairable, whereas those considered as scrap are directly sold to customers at a low price. The objective of the proposed model is to minimise the costs of production, distribution, holding and backorder. In addition to minimising the costs, the model can determine the economic production quantity (EPQ), the appropriate length of each cycle (ALOEC) and the quantities of defective products, scrap products and retailer shortages using Just-In-Time logistics (JIT-L). We used the GAs and a Cplex solver with probability parameters and various dimensions for validation of the studied model in real-life situations, and we compared the outputs to demonstrate the performance of the model. Additionally, to identify the appropriate length of each cycle (ALOEC), we needed to solve the model using exact parameters and same dimensions and prefer to use Lingo for this application.

AB - The competitive environment of global markets has forced many manufacturers to select the most appropriate supply chain network (SCN) for reduction of total costs and wasted time. Cost reduction and selection of the appropriate length of each period are two important factors in the competitive market that are often not addressed comprehensively by researchers. In our study, we proposed genetic algorithms (GAs) for optimising a novel mathematical model of the defective goods supply chain network (DGSCN). In the proposed model, we assumed that all imperfect-quality products are not repairable, whereas those considered as scrap are directly sold to customers at a low price. The objective of the proposed model is to minimise the costs of production, distribution, holding and backorder. In addition to minimising the costs, the model can determine the economic production quantity (EPQ), the appropriate length of each cycle (ALOEC) and the quantities of defective products, scrap products and retailer shortages using Just-In-Time logistics (JIT-L). We used the GAs and a Cplex solver with probability parameters and various dimensions for validation of the studied model in real-life situations, and we compared the outputs to demonstrate the performance of the model. Additionally, to identify the appropriate length of each cycle (ALOEC), we needed to solve the model using exact parameters and same dimensions and prefer to use Lingo for this application.

KW - Cplex

KW - Genetic algorithm

KW - Length of each-cycle

KW - Lingo

KW - Supply chain

UR - http://www.scopus.com/inward/record.url?scp=84893788706&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893788706&partnerID=8YFLogxK

U2 - 10.1016/j.apm.2013.08.023

DO - 10.1016/j.apm.2013.08.023

M3 - Article

AN - SCOPUS:84893788706

VL - 38

SP - 1534

EP - 1547

JO - Applied Mathematical Modelling

JF - Applied Mathematical Modelling

SN - 0307-904X

IS - 4

ER -