For e-commerce websites, deciding the manner in which items are listed on webpages is an important issue because it can dramatically affect item sales. One of the simplest strategies of listing items to improve the overall sales is to do so in a descending order of sales or sales numbers. However, in lists generated using this strategy, items with high similarity are often placed consecutively. In other words, the generated item list might be biased toward a specific preference. Therefore, this study employs penalties for items with high similarity being placed next to each other in the list and transforms the item listing problem to a quadratic assignment problem (QAP). The QAP is well-known as an NP-hard problem that cannot be solved in polynomial time. To solve the QAP, we employ quantum annealing (QA), which exploits the quantum tunneling effect to efficiently solve an optimization problem. In addition, we propose a problem decomposition method based on the structure of the item listing problem because the quantum annealer we use (i.e., D-Wave 2000Q) has a limited number of quantum bits. Our experimental results indicate that we can create an item list that considers both sales and diversity. In addition, we observe that using the problem decomposition method based on a problem structure can lead to a better solution with the quantum annealer in comparison with the existing problem decomposition method.

In this paper, we propose a novel energy-efficient architecture for software-defined data center infrastructures. In our proposed data center architecture, we include an exhaust heat reuse system that utilizes high-temperature exhaust heat from servers in conditioning humidity and air temperature of office space near the data center. To obtain high-temperature exhaust heat, equipment such as server racks and air conditioners are deployed in tandem so that the aisles are divided into three types: cold, hot, and super-hot. In this paper, to investigate the fundamental characteristics of our proposed data center architecture, we consider various types of data center models and conduct numerical simulations that use results obtained by experiments at an actual data center. Through simulation, we show that the total power consumption by a data center with our proposed architecture is 27% lower than that by data center with a conventional architecture. In addition, it is also shown that the proposed tandem equipment arrangement is suitable for obtaining high-temperature exhaust heat and decreasing the total power consumption significantly under a wider range of conditions than in the conventional equipment arrangement.