Integrating a partnership with potentially stronger suppliers is widely acknowledged as a contributor to the organizational competitiveness of a supply chain. This paper proposes an event-based model which lists the events related with all phases of cooperation with partners and puts events into a dynamic supply chain network in order to understand factors that affect supply chain partnership integration. We develop a multi-objective supply chain partnership integration problem by maximizing trustworthiness, supplier service, qualified products rate and minimizing cost, and then, apply a hybrid algorithm (PSACO) with particle swarm optimization (PSO) and ant colony optimization (ACO) that aims to efficiently solve the problem. It combines the advantages of PSO with reliable global searching capability and ACO with great evolutionary ability and positive feedback. By using the actual data from 1688.com, experimental studies are carried out. The parameter optimizing of the hybrid algorithm is firstly deployed and then we compare the problem solution results of PSACO with the original PSO, ACO. By studying the partnership integration results and implementing analysis of variance (ANOVA) analysis, it shows that the event based model with PSACO approach has validity and superiority over PSO and ACO, and can be served as a tool of decision making for supply chain coordination management in business.
ACOCA: Ant Colony Optimization Based Clustering Algorithm for Big Data Preprocessing
