Michael Phelan

Abstract

Over the last 30 years, the field of supply chain management has received widespread attention from researchers and practitioners across a broad range of disciplines. During this time companies have moved from centrally controlled supply chains towards the outsourcing of non-core functions, requiring new and innovative approaches to how these supply chains are optimised.

In recent years there has been a growing literature in the area of biologically inspired algorithms, particularly genetic algorithms and genetic programming and their applications 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 consequently difficult to add richness to the supply chain. While the application of 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 introduces Grammatical Evolution, a relatively new biologically inspired algorithm in computer science 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. The primary research question of this work asks if grammatical evolution can provide managerial insights and cost effective heuristics for supply chain optimisation across a range of realistic scenarios.

The methodology used in this research is experimental. Given the stochastic nature of simulating supply chain models with stochastic demand; a statistical analysis of several runs is employed to evaluate the cost effectiveness of supply chain ordering policies generated by grammatical evolution.

The supply chains modelled incorporate more realistic features including: inventory allocation policies, payment incentives, linear and distribution supply chain structures, fast and slow moving stochastic demand and capacity constraints on the warehouse and logistics. Using different grammars, ordering policies that minimise the costs in centralised supply chains are compared to policies that balance the associated risks and costs across the supply chain partners in decentralised supply chains.

On the experimental evidence obtained across an extensive range of scenarios, this research demonstrates the flexibility of grammatical evolution as a supply chain optimisation tool and also its ability to adapt to the objective of each scenario, delivering cost effective ordering policies. The grammars incorporating domain knowledge consistently generate the best supply chain ordering policies.

Combining this powerful optimisation approach with more realistic models and incorporating their own domain knowledge, practitioners can develop grammars to bias the grammatical evolution algorithm towards finding better supply chain ordering policies. The experiments in this work demonstrate that grammatical evolution can deliver a range of solutions for the same problem, enabling practitioners to compare and contrast policies, highlighting questions that impact on the underlying supply chain strategy. However, it is left up to the expertise of the supply chain practitioner to analyse the managerial implications of these policies.