This study aimed to develop a competency scale for evaluating career development education and examine the effects of connecting university career development education to employment and students' self-awareness competencies. A web survey was conducted using 392 fourth-year students with job-hunting experience. For the survey, career education was categorized into career development classes, non-career development classes, and extracurricular educational activities and analyzed.

The results showed that the effect of connecting to employment was high for those who had experienced career development classes, especially for the group with an adjusted standard deviation score of less than 50. The self-awareness competencies of the university students were relatively higher in the extracurricular educational competencies, regardless of the adjusted standard deviation score, indicating limited career development education. The analysis of career development classes regarding the social significance and educational potential showed that “courtesy and manners” was a distinctive feature of the classes, especially for the group score of less than 50, indicating the significance of teaching “courtesy and manners” in classes.

There are various kinds of work in distribution warehouses. It is said that order picking work, in which products included in orders received from customers are gathered from storage shelf locations to fulfill those orders, accounts for 50 to 60% of the total work in a distribution warehouse. One of the typical problems encountered in order picking is the batching problem, which involves the determination of which orders to batch together for processing. Optimal batching shortens the travel time in picking work and enables efficient warehouse operations. Many studies use the total distance traveled by all order pickers as an assessment index for solutions to the order batching problem, but most of these methods require extended computation times for optimization because the distance traveled is precisely calculated.

In this study, for solving order batching problems, we propose a model that reduces the required computation time and improves the accuracy of the solution obtained within the time limit by using the approximated distance covered as an evaluation index. For scenarios where it is not possible to solve the problem with the abovementioned precise calculation technique due to problem scale, and too much time is required to determine the sequence of visiting the shelves that requires the minimum travel distance, a solution is enabled by performing distance approximation focusing on the arc of the warehouse layout. Furthermore, while the approximation method used in conventional research has limited warehouse layouts to which it is applicable, the proposed method can be adapted to warehouse layouts based on actual data so that it can be applied to more complex warehouse layouts.

In this paper, the authors formulate multiobjectve bimatrix games with fuzzy payoffs. Using the constraint method and necessity measure, an equilibrium solution concept is defined. Under the assumption that a player can estimate the opponent player's acceptable levels of the constraint method, an interactive algorithm is proposed to obtain a satisfactory solution for the player from among an equilibrium solution set by updating the acceptable levels. A numerical example illustrates interactive processes for a hypothetical player to show the efficiency of the proposed method.

The authors propose a novel solution to the facility layout problem (FLP). Facility layout aims to deploy a set of objects in logistics facilities and production facilities. The time to create layouts and determine the efficiency of the design layouts created significantly depends on a designer's ability, as consideration must be given to lead time, relations among functional objects to deploy, and material handling costs. Because the computational complexity of FLP is NP-hard, meta-heuristic methods that classify the problem and find an approximate solution are the major solutions. A layout design that reduces transportation processes and wasted space is very difficult to create. In this study, an automatic design system is proposed that can design relationship-oriented layouts for various unit groups and sites using reinforcement learning and the analytic hierarchy process (AHP). Reinforcement learning is a machine-learning method that realizes optimal system control through trial and error by the system itself. AHP is one of the multicriteria decision methods that create a hierarchy of options for the decision divided into levels. Reinforcement learning is used to create a near optimal layout. AHP is used to consider relationships among the units when the agents deploy in the site. The results of applying the system to benchmark problems show that the system can produce relationship-oriented layouts that successfully deploy units within a given one-floor site.