Designing the façade, which is the front side of a building, is a crucial yet time consuming part of the architectural design process. Advancements in image generation have led to generative models capable of producing creative, high-quality images. However, it is difficult to apply existing image generative models to façade design as the generated images should provide the architect with inspiration while also reflecting the designer's knowledge and building requirements. The existing models are inadequate for controlling image generation. Thus, we propose a system that supports designers in coming up with ideas for façade design by enabling them to intervene in image generation. The proposed system first determines the base image by using text-to-image retrieval. Next, the system generates diverse images using adversarial generation networks and the user selects images in alternation. This allows for repeated divergence and convergence of ideas and provides the user with inspiration. Our experiments demonstrated that the proposed system is able to arrive at the target idea while providing a variety of ideas through controlled generation.

The implementation of Generative Adversarial Networks (GAN) in the fashion domain has been researched for various applications such as virtual-try-on, fashion item recommendation, and design generation. In this paper, we propose a GAN-based fashion design generation system that reduces the workload of the labor-intensive design creation task. Our system consists of two generative models: one that produces images of fashion items without any clothing patterns using conditioned StyleGAN2-ADA, and one that is a style transfer model reflecting the fine texture of the fashion item. The system also allows users to edit images of garments by manipulating the latent code of the generator. We demonstrate through qualitative and quantitative experiments that the proposed system trained on a dataset of real clothing inventory images can generate realistic and diverse images that reflect the input conditions in detail.

Due to the imperfection of processes, the quality of some products may be unsatisfactory. Moreover, equipment failure can stop production for a while. Therefore, integrating the triple concepts of quality, maintenance, and inventory control has attracted attention. Triple concepts are the constituents of the pre-sale costs. Selling price and warranty are generally considered to maintain market share and maximize the producer's profit. Quality and maintenance in production should be considered to reduce the post-sale costs of the warranty. Despite interactions, integrating warranty with triple concepts has been neglected. We integrate the quadruple concepts in a biobjective model to maximize the profit and minimize the pre-sale and post-sale costs under the free minimal repair warranty policy. A non-central chi-square (NCS) control chart monitors the mean and variance, simultaneously. The technology level is also considered for increasing product quality and reducing failures during the warranty period. Due to its high complexity, the model is solved by the particle swarm optimization algorithm. The proposed model is applied through a numerical example and three comparative studies. The results indicate the better performance of the NCS chart, the superiority of bi-objective optimization versus single-objective optimization, and the importance of integrating presale and postsale costs.

This study discusses how the application of graph theory is used to assist in the construction of the B2 angkot route in Bengkulu City so that it is more regular and can reduce congestion. With a neat and orderly route, it can help make it easier for people to determine which angkot they will use to get to their chosen destination. This research will use the literary method (the problem of the Chinese postman). The results showed that the trajectory made by the literary method (the problem of the Chinese postman) can solve traffic jams.

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