Optimization of the OEE indicator through meta-models’ simulation in the buffer allocation problem

Abstract

Purpose: The buffer allocation problem (BAP) arises in the design of production systems; it involves analyzing and defining the optimal distribution of buffers within a production line. This paper presents a BAP formulation in a parallel series line from a cup sublimation process with unreliable operating conditions. The main objective of this study is to develop a new BAP solution proposal, considering the optimization of the OEE indicator used in Lean Manufacturing.

Design/methodology/approach: The BAP was analyzed under an optimization approach from two different criteria: firstly, the maximization of the OEE indicator (Overall Equipment Effectiveness) utilized in Lean Manufacturing, as well as the maximization of the average production rate (Throughput). The case study involves unreliable operating conditions. Process times, and timeframes between failures and repairs, consider normal distribution functions. The evaluation method employed in the study includes the use of simulation meta-models built from experiment designs and production line simulations; on the other hand, the nonlinear GRG algorithm is used to solve the mathematical models.

Findings: In the study carried out, it is shown that the OEE indicator can be affected when more buffers are allocated than necessary, hence it is important to calculate and establish the best configuration for them through an analysis such as the one proposed in this document.

Research limitations/implications: The research is limited to a case study of an unreliable production line from a cup sublimation process.

Practical implications: The proposed solution established in this study can be used in other production lines with configurations different from the one analyzed, considering the optimization criterion of the OEE indicator. 

Originality/value: Seeking that the allocation of buffers within the production line improves the OEE indicator is something new in the literature, therefore, the results achieved in this research become even more relevant.