This study extends the Garbage Can Model, a model of organizational decision-making, into a multi-agent simulation to analyze organizational performance from both decision-making tendencies and cost/time perspectives, ultimately deriving practical implications. The Garbage Can Model posits that organizational decisions result from the random convergence of participants, problems, solutions, and choice opportunities, often leading to outcomes such as oversight or flight rather than direct problem resolution. However, previous criticisms argue that the model's simulation results are questionable and do not adequately represent real-world organizational decision-making. Building on previous research, this study develops a refined version of the model by addressing these criticisms and adapting it for multi-agent simulation. Simulation results using the newly developed Active Garbage Can Model reveal key insights into organizational performance under varying conditions of organizational size, managerial range of decision-maker, and organizational structure. The results indicate that in small organizations, structure enhances efficiency, while a narrower managerial range of decision-maker leads to greater efficiency. Whereas, expanding managerial range of decision-maker significantly reduces efficiency. Moreover, in large organizations where both size and managerial range of the decision-maker are expanded, structural factors further reduce efficiency.

This study seeks to elucidate the generative mechanisms governing radio listeners’ listening durations. The proposed model formalizes the intrinsic periodicity of individual listening behaviors within the framework of harmonic regression, integrating explanatory variables such as the day of the week and program genre. To account for inter-individual heterogeneity, a hierarchical Bayesian model is employed, with parameter estimation conducted via the Markov Chain Monte Carlo (MCMC) method. Empirical validation is performed using data from the radio streaming service “radiko.” The results indicate that listening durations exhibit diverse periodic structures specific to individual listeners and that preferences for different program genres are highly heterogeneous. Furthermore, cluster analysis based on listener-specific heterogeneous response coefficients identifies four distinct periodic listening patterns.

This study tackles the issue of truck driver shortages by proposing an optimization model for scheduling trucks on circular transport routes to ensure evenly spaced arrivals at factories. Given the limited storage space in Japanese factories and the steady consumption rate of parts, efficient scheduling is crucial. The model employs Mixed Integer Second-Order Cone Programming (MISOCP) and local search to achieve this. Numerical experiments demonstrate that the proposed method significantly improves scheduling efficiency, meeting practical target values. The findings confirm the model’s utility in addressing logistical challenges and enhancing operational efficiency in the face of driver shortages and regulatory constraints.

The creation of nurses' schedules is a critical task that directly affects the quality and safety of patient care, as well as nurses' well-being. In most Japanese hospitals, head nurses of each ward shoulder this responsibility, and the work is both physically and mentally demanding. Recent challenges, such as the growing shortage of nurses and increasingly diverse working styles, have further increased complexity, fueling demand for automated nurse scheduling systems. Although modern integer programming solvers can generate feasible schedules within a practical time frame, many hospitals still rely on manual scheduling. A key reason is that tacit knowledge, considerations unconsciously applied by head nurses, cannot be fully formulated into explicit constraints, rendering solver-generated schedules impractical for use. To address this issue, we propose a novel “two-stage scheduling method” that separates the problem into a night-shift stage and a day-shift stage, with targeted manual adjustments by head nurses after the night-shift stage. This interactive process makes it possible to produce nurse schedules that are suitable for real-world implementation. Furthermore, to promote the practical adoption of automated scheduling, we present case studies from acute and chronic care hospitals where systems based on the proposed method have been deployed. We also discuss implementation challenges and corresponding solutions.