A data-driven business intelligence system for large-scale semi-automated logistics facilities

Abstract

With the proliferation of e-commerce, the regional hub of a large-scale logistics company is required to sort and load a large number of packages into different delivery vehicles by dawn and deliver them to customers by noon on a daily basis. The efficiency of the sorting operation is thus a competitive advantage which directly impacts the company's service level. In this study, a data-driven business intelligence system for the semi-automated sorting facility is proposed for real-world implementation. To determine the cargo handling sequence, an information-based approach with a multi-criteria index function is developed. Then a simulation-based optimisation framework, which integrates a multi-objective search algorithm with a simulation model, is employed to fine-tune the parameters of the index function to perform optimally. The results of the numerical experiment show that the proposed technique is able to reduce 20% of the sorting operation duration, which equals a reduction of about 3600 man-hours per year. The study is a good example of applying emerging technologies in the logistics industry.