We present a hybrid algorithm called parallel simulated annealing with a greedy algorithm (PSAGA) to learn Bayesian network structures. This work focuses on simulated annealing and its parallelization with memoization to accelerate the search process. At each step of the local search, a hybrid search method combining simulated annealing with a greedy algorithm was adopted. The proposed PSAGA aims to achieve both the efficiency of parallel search and the effectiveness of a more exhaustive search. The Bayesian Dirichlet equivalence metric was used to determine an optimal structure for PSAGA. The proposed PSAGA was evaluated on seven well-known Bayesian network benchmarks generated at random. We first conducted experiments to evaluate the computational time performance of the proposed parallel search. We then compared PSAGA with existing variants of simulated annealing-based algorithms to evaluate the quality of the learned structure. Overall, the experimental results demonstrate that the proposed PSAGA shows better performance than the alternatives in terms of computational time and accuracy.

Recently, the transportation capability of the automated material handling system (AMHS) has emerged as a major barrier to the semiconductor fabrication facility (FAB), because it can limit the FAB production capacity. In this study, we propose a prediction method for a machine allocation problem of production scheduling in consideration with the AMHS’s constraints. The proposed method dynamically targets a machine for the next process by identifying diverse production conditions. We use a deep neural network-based dynamic scheduling method considering the overall production environment, which includes the remaining processing time, facility states, transportation time and traffic congestion, work-inprocess distribution, and intermediate buffer states. To demonstrate the superiority and efficiency of the proposed method, we conducted experimental studies to compare the proposed model with the existing priority rule-based and analytic models under the static and dynamic environments. From the results, we verify that the proposed dynamic scheduling system can enhance the performance of existing AMHS and reduce machine starvation and production losses.

Automated material handling systems (AMHSs) used in semiconductor fabrication are sophisticated and integrated control systems that transfer materials from one machine to another within a manufacturing facility via hundreds of vehicles. Vehicular congestion in AMHSs causes significant production inefficiency. In this paper, we demonstrate the reasons for and classification of congestion, and suggest congestion metrics for material handling systems. We develop and implement a congestion monitoring system (CMS) based on the proposed metrics at an actual manufacturing site. We provide practical case studies on three applications to address the effectiveness of CMS: 1) congestion monitoring; 2) redistribution of routing; and 3) enhancing a routing algorithm. Simulation results demonstrated that the proposed routing algorithm decreased the average delivery time by 12.41% in heavy congestion. The shorter average delivery time reduces the loss in production by at least 1%.

In this paper, we propose using explainable artificial intelligence (XAI) techniques to predict and interpret the effects of local festival components on tourist satisfaction. We use data-driven analytics, including prediction, interpretation, and utilization phases, to help festivals establish a tourism strategy. Ultimately, this study aims to identify the most significant variables in local tourism strategy and to predict tourist satisfaction. To do so, we conducted an experimental study to compare the prediction accuracy of representative predictive algorithms. We then built a surrogate model based on a game theory-based framework, known as SHapley Additive exPlanations (SHAP), to understand the prediction results and to obtain insight into how tourist satisfaction with local festivals can be improved. Tourist data were collected from local festivals in South Korea over a period of 12 years. We conclude that the proposed predictive and interpretable strategy can identify the strengths and weaknesses of each local festival, allowing festival planners and administrators to enhance their tourist satisfaction rates by addressing the identified weaknesses.