In this paper, we statistically verify whether or not the ratings on the gourmet ratings website give official preferential treatment based on actual data scraped from the site. Since “official” is done by each shop that registers on the site, there is no random assignment of official or unofficial. Therefore, we first adjust for covariates using propensity score matching to verify whether preferences exist on average. Next, we estimate the amount of movement around the rating where the official group is larger than the unofficial group by bunching, and from the fact that there is no preferential treatment around the lowest rating, we assume a monotonic preferential treatment. We then assume monotonic preferences based on the fact that there are no preferences around the lowest rating, and verify whether the result is consistent with the average amount. The result confirms that the total preferences calculated from the results of the monotone preferential assumption and the local preferential quantities are in close agreement with the total preferences calculated from the average preferential quantities.

The mixed assembly line problem is a problem to decide a job sequence. In order to determine the job sequence, it is assumed that the works are assigned to work stations. Usually, the cycle time is obtained from the operating time and the production volume during the planning period, and the works are assigned by line balancing. Therefore, many studies use a fixed time cycle when deciding the job sequence. This paper discusses an assembly line system that is able to adjust the conveyor speed in order to change the cycle time. Both the job sequence and the conveyor speed are determined. The authors aim to minimize the makespan under the condition that the line does not stop. To address this problem, the authors describe how to set the upper and lower limits of the optimal conveyor speed and the effect of the conveyor speed on the makespan. SA is applied to this problem and its effect is shown through numerical experiments.

This study proposes a two-stage approach that sequentially solves two mixed-integer programming models for a mixed-model assembly line that accepts fractional task allocation under multiple demand scenarios. The task sequence is assumed to be fixed in all demand scenarios, but the number of stations and the allocation of tasks to stations can be changed according to the realized demand. Only one task from among all of the tasks can be shared between adjacent stations, and the shared task can be moved to the boundary of these stations. The upper mathematical model decides the task sequence and the allocation of tasks to stations in all scenarios. The selection of the shared task is also included in the upper model. The lower model generates a detailed assembly schedule for each scenario and allocates buffers between stations when necessary. Numerical experiments provided several findings as described below. On average, it was beneficial to design an assembly line so as to be prepared for all scenarios under multiple demand scenarios. Introducing task sharing expands the solution space and may reduce the number of stations used. However, responding to unexpected demand scenarios through task sharing is limited. The number of buffers in the line affects the desirable sequencing of product models.