This paper proposes a genetic algorithm (GA) to find the pseudo-optimum of integrated process planning and scheduling (IPPS) problems. IPPS is a combinatorial optimization problem of the NP-complete class that aims to solve both process planning and scheduling simultaneously. The complexity of IPPS is very high because it reflects various flexibilities and constraints under flexible manufacturing environments. To cope with it, existing metaheuristics for IPPS have excluded some flexibilities and constraints from consideration or have built a complex structured algorithm. Particularly, GAs have been forced to construct multiple chromosomes to account for various flexibilities, which complicates algorithm procedures and degrades performance. The proposed new integrated chromosome representation makes it possible to incorporate various flexibilities into a single string. This enables the adaptation of a simple and typical GA procedure and previously developed genetic operators. Experiments on a set of benchmark problems showed that the proposed GA improved makespan by an average of 17% against the recently developed metaheuristics for IPPS in much shorter computation times.

Recently, wind power plants that generate wind energy with electricity are attracting a lot of attention thanks to their smaller installation area and cheaper power generation costs. In wind power generation, it is important to predict the amount of generated electricity because the power system would be unstable due to uncertainty in supply. However, it is difficult to accurately predict the amount of wind power generation because the power varies due to several causes, such as wind speed, wind direction, temperature, etc. In this study, we deal with a mid-term (one day ahead) wind power forecasting problem with a data-driven approach. In particular, it is intended to solve the problem of a newly completed wind power generator that makes it very difficult to predict the amount of electricity generated due to the lack of data on past power generation. To this end, a deep learning based transfer learning model was proposed and compared with other models, such as a deep learning model without transfer learning and Light Gradient Boosting Machine (LGBM). As per the experimental results, when the proposed transfer learning model was applied to a similar wind power complex in the same region, it was confirmed that the low predictive performance of the newly constructed generator could be supplemented.

Airline services should be passenger-focused to be sustainable. In this study, we redesign an in-flight service process using a service blueprint while incorporating direct customer perceptions of service experiences. To incorporate these, we apply topic modeling to 64,706 passenger-written online reviews of airline services. Passenger experiences of in-flight services are the sum of experiences from service encounters in all the subsequent steps and we assume that their direct perceptions of their experiences are faithfully contained in the online reviews. Topics extracted from the reviews can be regarded as service encounters based strongly on passenger experiences. Then, the service encounters are reorganized within the framework of a service blueprint. The results show that the complexity, a number of service steps, decreases by 38% compared to the benchmark service blueprint. However, the divergence, a latitude of service steps, should increase for a couple of service encounters. Moreover, we quantitatively analyze the divergence using the probability of word frequency statistically distributed across topics. The in-flight service using the proposed design could be sustainable with respect to customer-focused service while considering direct customer experiences in real-time.