LAUSR

As a critical enabler for achieving smart manufacturing, the Industrial Internet platform aims to integrate distributed manufacturing services to complete complicated manufacturing tasks. Manufacturing service (MS) collaboration plays an important role in improving manufacturing efficiency and customers’ satisfaction and its optimisation is therefore of great significance. As MSs are geographically distributed, logistics is an essential ingredient that needs to be considered for MS collaboration optimisation. However, only straight-line logistics distances are considered in most of existing studies without considering effects of logistics route selection and complex geographical locations of MSs, thereby resulting in inaccuracy in practical applications. With the aim to overcome these drawbacks, this paper establishes an adjacent matrix-based logistics-aware MS collaboration optimisation (LA-MSCO) model with detailed definitions of time, cost and reliability attributes of logistics. An improved artificial bee colony algorithm with both dimensional self-adaptation and group leader mechanisms, i.e. DSA-GL-ABC, is proposed for solving the LA-MSCO problem. Simulation experiments indicate the better performance of DSA-GL-ABC algorithm in terms of searching capability, convergence speed and solution quality.