Automated Bottleneck Analysis of Production Systems : Increasing the applicability of simulation-based multi-objective optimization for bottleneck analysis within industry

Abstract: Manufacturing companies constantly need to explore new management strategies and new methods to increase the efficiency of their production systems and retain their competitiveness. It is of paramount importance to develop new bottleneck analysis methods that can identify the factors that impede the overall performance of their productionsystems so that the optimal improvement actions can be performed. Many of the bottleneck-related research methods developed in the last two decades are aimed mainly at detecting bottlenecks. Due to their sole reliance on historical data and lackof any predictive capability, they are less useful for evaluating the effect of bottleneck improvements.There is an urgent need for an efficient and accurate method of pinpointing bottlenecks, identifying the correct improvement actions and the order in which these should be carried out, and evaluating their effects on the overall system performance. SCORE (simulation-based constraint removal) is a novel method that uses simulation based multi-objective optimization to analyze bottlenecks. By innovatively formulating bottleneck analysis as a multi-objective optimization problem and using simulation to evaluate the effects of various combinations of improvements, all attainable, maximum throughput levels of the production system can be sought through a single optimization run. Additionally, post-optimality frequency analysis of the Pareto-optimal solutions can generate a rank order of the attributes of the resources required to achieve the target throughput levels. However, in its original compilation, SCORE has a very high computational cost, especially when the simulation model is complex with a large number of decision variables. Some tedious manual setup of the simulation based optimization is also needed, which restricts its applicability within industry, despite its huge potential. Furthermore, the accuracy of SCORE in terms of convergence in optimization theory and correctness of identifying the optimal improvement actions has not been evaluated scientifically.Building on previous SCORE research, the aim of this work is to develop an effective method of automated, accurate bottleneck identification and improvement analysis that can be applied in industry.The contributions of this thesis work include:(1) implementation of a versatile representation in terms of multiple-choice set variables and a corresponding constraint repair strategy into evolutionary multi-objective optimization algorithms;(2) introduction of a novel technique that combines variable screening enabled initializationof population and variable-wise genetic operators to support a more efficient search process;(3) development of an automated setup for SCORE to avoid the tedious manual creation of optimization variables and objectives;(4) the use of ranking distance metrics to quantify and visualize the convergence and accuracy of the bottleneck ranking generated by SCORE.All these contributions have been demonstrated and evaluated through extensive experiments on scalable benchmark simulation models as well as several large-scale simulation models for real-world improvement projects in the automotive industry.The promising results have proved that, when augmented with the techniques proposed in this thesis, the SCORE method can offer real benefits to manufacturing companies by optimizing their production systems.