Grammatical evolution in developing optimal inventory policies for serial and distribution supply chains

Abstract

Recently, there has been a growing literature on biologically inspired algorithms, particularly genetic algorithms and genetic programming, applied to supply chain modelling and inventory control optimisation. Due to the rigidity of the genetic algorithms approach, it is difficult to change the underlying model logic and add richness to the supply chain. While genetic programming provides a more flexible approach than that provided by genetic algorithms, to date its application has been limited to small supply chain modelling problems in relation to optimal inventory policies. This research applies Grammatical Evolution, a relatively new biologically inspired algorithm, to the field of supply chain optimisation, employing human readable rules called grammars. These grammars provide a single mechanism to describe a variety of complex structures and can incorporate the domain knowledge of the practitioner to bias the algorithm towards regions of the search space containing better solutions. Results are presented showing Grammatical Evolution is at least competitive in cost terms, and superior in flexibility, with these methods applicable to any supply chain of the serial or distribution type. Furthermore, Grammatical Evolution shows an adaptive ability that augurs well for supply chains in dynamic environments, such as disruption.

Keywords:

• grammatical evolution
• supply chain ordering policies
• costs

Notes

No potential conflict of interest was reported by the authors.

1 It is crucial that any optimisation algorithm should be biased towards the better or optimal solution(s). The word bias in this context has no negative connotations: it simply describes the tendency of the algorithm to move towards the best regions of the search space.