Scheduling policies analysis for matching operations in Bernoulli selective assembly lines

Abstract

In a selective assembly system, mismatched products can pass inspections due to the flexibility of product quality grades. However, they will be sold at discounted prices leading to a revenue decline. Hence, it is critical to design an appropriate scheduling policy for better matching to maximise the system quality-related revenue. In this paper, we propose a Waiting for Closest Quality Matching Policy (WCQMP), which allows postponing the assembly process within the waiting threshold. And once the postpone is finished, the closest quality parts will be selected to match. The other two policies, Random Matching Policy (RMP) and Closest Quality Matching Policy (CQMP), are also proposed as comparisons. We construct Markov chain models for small systems and develop approximation methodologies for larger systems to analyze the performance under the policies. Comparisons of different scheduling policies and the performance analysis of WCQMP are carried out in numerical studies. Our findings indicate that nearly in all the systems, WCQMP, CQMP performs better than RMP. And when system and policy parameters are properly designed, WCQMP is more superior by improving assembly quality without overly sacrificing system throughput, thereby increasing quality-related revenue. Managerial insights are also provided for industrial practitioners to apply WCQMP more appropriately.