A three-echelon based sustainable supply chain scheduling decision-making framework under the blockchain environment
Ming Zeng, Keivan Sadeghzadeh, Xiong Tao
Abstract
The Supply Chain Scheduling (SCS) decision making is a challenging task under the blockchain environment because the supply chain is composed of multi-players in all layers. To utilise the information sharing by multiplayers with equal rights in all layers to maximise the supply chain’s utility for reducing the carbon emission, this study proposed a three-echelon supply chain integrated scheduling model that considers the production capacity and multi-product with different delivery time factors. The objective is to minimise the total cost incurred in production and transportation under the blockchain environment. To cope with the complexity arising from multiplayers with equal rights in all layers, a metaheuristic based sequential brain storm optimisation (SBSO) algorithm with a novel encoding scheme and the hybrid crossover and mutation strategy is proposed to enhance the performance. A case study comparing the proposed decision-making framework with the artificial bee colony algorithm (ABC) and the backtracking search algorithm (BSA) is conducted, and results show the superiority of the proposed framework. With the help of the proposed SCS decision-making framework, not only can the SCS decision be made, but also the low efficient nodes within the supply chain under the blockchain environment can be identified for potential sustainable upgrading.