Purpose – The purpose of this paper and its companion (Part I: single and two‐component supply chains) is to investigate methods to tackle complexities, constraints (including time‐varying constraints) and other challenges. In this part, attention is devoted to multi‐silo supply chain and the relationships between the components. The first part of the paper aims to consider two types of experimental supply chains: with one‐to‐many and many‐to‐one relationships. The second half of the paper aims to present two approaches on optimising the material flow in the real‐world supply chain network. Design/methodology/approach – Cooperative coevolutionary and classical sequential approaches are taken to address the experimental multi‐silo supply chains. Due to the nature and the complexity of the supply chain presented in the second half of the paper, evolutionary algorithm was not sufficient to tackle the problem. A fuzzy‐evolutionary algorithm is proposed to address the problem. Findings – The proposed systems produce solutions better than solutions proposed by human experts and in much shorter time. Originality/value – The paper discusses various algorithms to provide the decision support for the real‐world problems. The system proposed for the real‐world supply chain is in the process of integration to the production environment.
International Journal of Intelligent Computing and Cybernetics – Emerald Publishing
Published: Nov 23, 2012
Keywords: Genetic algorithms; Cooperative coevolution; Coordinated supply chain; Time‐varying constraints; Time‐varying control systems; supply chain management