Identifying Economic Order Quantities Using Genetic Algorithms

Abstract

The ability of traditional economic order quantity (EOQ) models to successfully determine optimum purchase and process batch sizes has declined in recent years. These models are simplistic in nature in that they make assumptions that are no longer valid in practice, in addition, they cannot take into account the wide variety of cost and other factors that influence inventory control decisions. Presents an alternative method for identifying economic batch quantities that uses genetic algorithms (GA) based on the underlying mathematical processes that control the reproduction of genes within biological species. Using genetic algorithms it is possible to successfully deal with complex inventory situations and there are no limits on the type and number of variables that can be used to influence the batch‐sizing decision.

Optimizing models for production and inventory control using a genetic algorithm

“…Then the model is solved with a Genetic Algorithm. There are several interesting and relevant papers related to the application of GA in inventory problems such as [30,31,9,10,32,33,34].…”

A parameter-tuned genetic algorithm for multi-product economic production quantity model with space constraint, discrete delivery orders and shortages

“…In order to solve the model, they provided a hybrid genetic algorithm that combines self-adapting crossover operator and mutation operator. There are several interesting and relevant papers related to the application of GA in inventory problems such as Stockton and Quinn [10], Mondal and Maiti [11], Hou et al [12], Gupta et al [13], Lotfi [14], Pal et al [15], and Taleizadeh et al [16][17][18][19].…”

Optimizing a multi-product and multi-supplier the economic production quantity model using genetic algorithm

“…Then the model is solved with a GA. There are several interesting and relevant papers related to the application of GA in inventory problems such as Stockton and Quinn (1993), Mondal and Maiti (2002), Hou et al (2007), Gupta et al (2009), Lotfi (2006), Pal et al (2009) and Taleizadeh et al (2009aTaleizadeh et al ( , 2009b.…”