Order-Driven Dynamic Resource Configuration Based on a Metamodel for an Unbalanced Assembly Line

Abstract

Resource-constrained product general assembly lines with complex processes face significant challenges in delivering orders on time. Accurate and efficient resources allocation of assembly lines remain a critical factor for punctual order delivery, full use of resources and associated customer satisfaction in complex production systems. In order to quickly solve the order-based dynamic resource allocation problem, in this paper a metamodel-based, multi-response optimization method is proposed for a complex product assembly line, which has the characteristics of order-based production, long working time of processes, multiple work area re-entry and restricted operator quantity. Considering the complexity of the assembly line and the uncertainty of orders, the correlation between system performance indicators and resource parameters is investigated. Multiple metamodels are constructed by the Response Surface Methodology to predict and optimize the system performance. The adequacy of the constructed metamodels is verified and validated based on the bootstrap resampling method. Under the condition of ensuring the throughput demand of the assembly line, the desirability function is applied to simultaneously optimize the multi-response, and the resource allocation solution is generated. The method in this paper can be used to rapidly adjust the resource configuration of the assembly line when considering the order changes.