In this paper, the authors investigate a mixed-model assembly line problem with the intention of maximizing work efficiency. There are some objectives for various points, such as minimizing work removes congestion and line stop time. In the case of minimizing line stop time, a longer cycle time compresses the stop time and improves the evaluation value. However, it increases idle time and results in lower work efficiency. In other words, a short cycle time increases the line stop time, but there is a possibility to increase work efficiency. Many studies have reported job sequence with a fixed cycle time. An assembly line system achieves higher work efficiency if the cycle time of the system can be changed. This paper focuses on an assembly line system that is able to adjust the conveyer speed to change cycle time and requires restart time after a line stop. In order to consider remove work efficiency including idle time, line stop time and restart time, the objective function is set to minimize the makespan as the length of a schedule. The objective function value is the time when the last product is completed at the final work station. The solution of this problem consists of job sequence and conveyer speed. The authors propose some methods using remove simulated annealing. The effect of the proposed methods is shown and the influence of the problem conditions on the makespan is investigated.

The manufacturer of construction materials, which is the subject of this study, plans production and produces the materials based on the required delivery date after it receives an order from a construction company (customer). However, the progress of a construction project may be delayed due to bad weather, shortage of personnel, etc., and the construction company may request the manufacturer to postpone the delivery date of the order. The manufacturer of construction materials has no choice but to accept this request, which causes the material manufacturers to store the inventory of finished products from the day production finished to the postponed delivery date, resulting in an increase in storage costs. In order to solve this problem, this study examines the introduction of a Pull production system that is different from the current production system (Push production system). In this study, the author analytically calculated a mathematical formula (decision formula) to determine which type of production system is capable of reducing inventory more easily and numerically verified it. In addition, the relationship between the factors that affect the choice of production system (postponed delivery term, demand distribution, production lead time, production cycle time, and safety coefficient) was clarified using the proposed formula in this study. Furthermore, the author compared his formula with the results of numerical simulations conducted in existing studies. Using the formula proposed in this study, it is possible to easily calculate which production system is capable of reducing the expected value of inventory simply applying a spreadsheet software commonly used in business practice.

A marketing policy called the ”Membership Stage System” is widely used in retail business. A membership stage provides benefits to customers such as shopping points when a customer's annual cumulative purchase amount exceeds a certain threshold and the customer's stage is raised a level. As a result, the company is not only able to promote the customer's willingness to purchase, but it can also obtain the purchasing history data, thereby enabling high-quality customer analysis. The most fundamental analysis is to infer the difference of purchasing characteristics between member stages and to construct different clustering models for each member stage. However, when the clustering models are learned independently for each membership stage, it is not possible to compare the obtained clusters between membership stages. In this study, we propose a new analytical method and its learning algorithm to analyze differences in cluster distribution between membership stages. Through demonstrating the proposed model applied to an actual data set of purchasing history data on a membership stage system, the effectiveness of our proposal is clarified.

Hoeffding probability inequality has been known as one of the best theoretical ways for evaluating the upper bound of upper probability about an average of several random variables based on limited stochastic properties such as expectation, variance and domain in each random variable. In this study, we address a fundamental study to make some issues clear for essentially improving the performance of Hoeffding probability inequality.