Minimisation of supply chain cost with embedded risk using computational intelligence approaches

Abstract

Global supply chains are vulnerable towards different types of risks and are dynamically expanding with the increase in globalisation. Costs are associated with every risk factor that causes disturbances in the allocation of certain goods at the required place and time, and with the required quality and quantity. In this paper, we consider a multi-echelon global supply chain model, where raw material suppliers, manufacturers, warehouses and markets are located in different countries. The paper first identifies all types of operational risk factors, their expected value and probability of occurrence, and associated additional cost. Based on initial information for the risk factors, optimal decisions regarding the inter-echelon quantity flow in the supply chain are made for a single planning horizon. Then, with the change in the expected value of the risk factors, the intra-echelon shift of flow is determined in order to minimise the total cost and risk factors. Considering the complexity involved with the problem, various computational intelligence techniques such as genetic algorithms, particle swarm optimisation and artificial bee colony are applied in the solution evaluation phase. The results obtained using the developed model illustrate that the ability to react to changes in risk factors offers potential solutions to robust supply chain design.