Guided chemotaxis-based bacterial colony algorithm for three-echelon supply chain optimisation

Abstract

On the base of the existing research study, a multi-period, multi-product, multi-supplier, single-manufacture, and multi-distributor supply chain model is considered in the paper. In the three-echelon model, a variety of decision-making activities involved in the procurement, production and distribution process are integrated at the operational level, giving rise to the non-deterministic polynomial-time hard computational complexity for model optimisation. For tackling the difficult model, this paper proposes a new optimisation method called guided chemotaxis-based bacterial colony algorithm, characterised by centre learning communication mechanism. More specifically, centre learning communication mechanism, where all the bacteria are enforced to learn towards the centre position of the swarm, is designed for the global exploration ability of algorithm. Chemotaxis, which guides the bacterium to fine-tune the solution in an increasingly favourable fitness landscape, is used to enhance the local exploitation ability of algorithm. Numerical experiments on a variety of simulated scenarios show the effectiveness and efficiency of the proposed algorithm in terms of both quality solution and computational time, by comparing it with some existing state-of-the-art solution approaches.

Keywords:

• three-echelon supply chain optimisation
• bacterial colony algorithm

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by The Natural Science Foundation of China [grant number 71271140], [grant number 71471158], [grant number 71001072], [grant number 71461027]; Hong Kong Scholars Program [project number G-YZ24].