An internet-of-things-based production logistics optimisation method for discrete manufacturing

Timely supply of production material is an important prerequisite for stable operation of discrete manufacturing systems. The difficulties of production logistics (PL) planning are increased due to the uncertainty and dynamicity in production environment. The Internet of things (IoT) provides a reliable solution for monitoring dynamic manufacturing process and obtaining real-time information. Under the uncertain manufacturing environment, this paper focusses on a PL optimisation method driven by real-time data. First, considering the uncertainty of material demands time caused by production fluctuation, the mathematical scheduling model with fuzzy time windows is established to minimise the distribution cost. Later, on the basis of the proposed two-stage operation and optimisation mechanism, an improved ant colony algorithm is designed, which introduces the factors of satisfaction degree and time windows width into state transfer rules and improves the dynamic adjustment strategy of pheromone. Finally, in a machining workshop, a case study is conducted based on the construction of real-time sensing and positioning manufacturing environment. Several numerical experiments are performed to verify the feasibility of the proposed method.