Production-intralogistics synchronization of industry 4.0 flexible assembly lines under graduation intelligent manufacturing system

Assembly line balancing problems (ALBP) have plagued scholars and practitioners for decades. This paper investigates a new assembly system called flexible assembly line (FAL) derived from empirical observations in an air-conditioner assembly workshop. FAL can avoid the ALBP itself thanks to its structural flexibility and reconfigurability. However, field investigation highlights new challenges in the FAL - the mismatch between production (assembly) and intralogistics (material supply) leads to long waiting/idle time and workflow chaos, consequently lowers productivity and increases backorders. The production-intralogistics (PiL) processes are spatiotemporally coupled and interactional. Its complexity is much higher than considering the production or intralogistics optimization solely. And the PiL processes are further complicated by uncertain events such as new job arrivals, stochastic operational time, and equipment failures. The advent of Industry 4.0 technologies shows the tremendous potentials to revolutionize the contemporary notions of production management. Massive production data can be collected and analyzed in real-time. Nevertheless, there is little methodological research regarding utilizing real-time data to support production decisions under uncertainties. Thus, how to leverage real-time data collected in Industry 4.0 environments to support the decision-making of PiL processes for achieving a matched, coordinated, and synchronous operations management under various uncertainties, is a novel research problem. This paper develops a five-phase Graduation intelligent Manufacturing System (GiMS) to achieve PiL synchronization with flexibility and resilience. The underlying principles and rationale of GiMS are formulated as a synchronization mechanism, which includes a graph-theory based clustering for planning/scheduling and real-time decentralized ticketing for execution/control. Comprehensive numerical results validate the superiority of GiMS and the benefits of visibility and traceability in various scenarios. Moreover, the effects of uncertainties and trolley capacity are investigated in the sensitivity analysis.