This study focused on the fulfilment of the operation standard for classifying of the International Classification of Diseases (ICD). It was clarified that the performance of the operation standard and the suitability of the coding results differed depending on the procedures executed in the operation standard. ICD is a classification system of injury and disease developed by the World Health Organization (WHO). WHO also publishes the operation standard for classifying. Medical information managers are ICD professionals in Japan who have learned this operation standard. This study payed attention on four operation procedures and selected four diagnoses for each of these procedures. Seventeen medical information managers classified them under the same conditions at the same venue at the same date. They were asked to write the operation procedures they performed using a prepared format. The operation procedures records showed that the appropriateness of the coding results and the fulfillment of operation standard depended on the operation procedures performing in the operation standard. There is an urgent need to create new education programs focusing on the operation standard and operation procedures.

Recently, it has become an issue for companies to improve employee organizational commitment and retain employees. On the other hand, employees tend to develop their career more autonomously and expect to be involved in extra-curricular work activities such as skill development activities and secondary jobs that may contribute to their future career regardless of the main job in their company. In this study, we defined these spontaneous work activities as “extra-curricular activities for career development”, which do NOT aim at contributing to business in the company they belong to but at developing their career proactively. We conducted questionnaire surveys targeting young employees in their 20s and 30s in order to analyze the relationship between motivation for “extra-curricular activities for career development” and organizational commitment to their companies. The results suggested three latent factors behind the motivation: self-improvement study, engagement in external job, and social contribution. The results showed that only the social contribution factor had a weak positive correlation with organizational commitment and the others had no correlation. Moreover, results indicated organizational commitment might be related to employees' perception of a companies' positive attitude to the activities. These findings would be beneficial for companies to establish a policy to deal with employee extra-curricular activities for career development.

In order to minimize the damage caused by large-scale disasters, efficient operation immediately following the incident is very important. However, normal logistics networks may not be available due to traffic congestion or road damage. In recent years, the use of drones for disaster relief has attracted attention. Drones have many characteristics that are beneficial for this purpose, such as autonomous control and being able to take off and land in small spaces. Research on commercial drone logistics is being actively conducted, but few studies have focused on their application to disaster relief. In this study, we propose a model to make effective use of drones during disasters. In our model, drones have two roles, namely, transporting relief supplies and collecting information, and can perform these two tasks simultaneously in one flight. Since drones have a shorter range than other transportation methods, drones generally need to make stops at charging stations. This complicates the proposed mathematical model, and it becomes difficult to obtain an optimal solution when the problem scale is large; accordingly, we construct an algorithm that assumes practical use. The proposed algorithm solves the mathematical model by dividing it into subproblems, allowing for a practical solution to be obtained in a reasonable time. Numerical experiments verify the validity of the model under various conditions. In addition, we formulate the facility location problem of depots and charging stations as a p-median and p-center problem, and verify the significance of its optimization.

Projects are often executed under uncertain circumstances and require prior decisions that take uncertainty into account. Among them, the schedule of the initial plan and the plan for additional decisions corresponding to the uncertainties become important. In this study, we developed a mathematical model of a two-stage stochastic programming problem considering time and cost tradeoffs and crushing, which are important in project scheduling. An effective solution using a stochastic integer linear model and a moment matching method was presented for DTCTP-C with random fluctuations. In project management, the duration of a job is determined by the man-hour and the amount of resources required for the job. The duration can be shortened by increasing the amount of additional resources utilized. The cost increases according to the the amount of resources used. This problem is referred to as the Time/Cost Trade-off Problem (TCTP). In this study, the relation between time and cost is represented by an inverse proportional curve. We present a solution to the Stochastic Discrete TCTP-Curve (SDTCTP-C) in which the duration of the job is defined as a random variable. It may also be necessary to study mathematical models that require multiple resources. Future prospects include expanding the model to take into account the amount of resources available at each time period. Furthermore, by reducing the difference between the maximum number of resources used and the minimum number of resources used to the furthest possible extent, it can be said that more realistic scheduling can be performed.

In Value Engineering (VE), value is expressed as (value) = (function)/(cost). Many companies use VE to develop products and improve existing products and operations; that is, to increase value. VE uses nouns and verbs as "(verb) (noun)" to express the functions of products and parts, and the functions of business processes. VE is a function-oriented method to efficiently create improvement plans by repeating divergent thinking and convergent thinking. However, it is said that skill is required to master VE, and it is difficult to define functions in particular, and it is also difficult to teach the definition of function that is easy to create an improvement plan. In this research, we propose a definition of function method using the Advanced Function and Behavior Representative Language (FBRL) method, which is a further improved definition of function method (hereinafter, “FBRL method”) that uses FBRL applied in ontology engineering. During in-house VE training for product improvement, we explained the definition of function method before the FBRL method was developed (hereinafter, the “conventional method”). Next, we explained the definition of function method using the Advanced FBRL method and tested the understanding of noun selection for the definition of function. The results confirmed that, for product improvement design, the Advanced FBRL method had a statistically significant difference and a higher score than the conventional method. This demonstrated the effectiveness of the Advanced FBRL method for understanding the noun selection to improve design.

A variety of sustainable manufacturing systems have attracted attention socially. Particularly, remanufacturing systems that can reduce consumption of resources and industrial wastes are now required. Under these circumstances, closed-loop manufacturing systems (CLMSs) have been under developed for many years. The CLMS is a system composed of conventional forward logistics (FL) and reverse logistics (RL) that recover used products and use them in the remanufacturing process. Using this system, companies gain the image of being ecological and can experience profit. However, many manufacturing companies have hesitated to newly expand their business into remanufacturing because of concerns about upfront investments and profitability incurred at the time of implementing CLMS. This makes it difficult to maintain the sustainable manufacturing systems by operating CLMS. To cope with this problem, a useful support model is needed for decision-making related to the implementation of CLMS, making it more realistic and comprehensive. Although various studies on CLMS are actively being carried out, there are relatively few studies on decision-making problems concerning the introduction of RL.

In this paper, we propose a support model into make a decision of the implementation of CLMS on many operational cost factors. Moreover, in order to enhance the possibility of putting the model to practical use in the real world, we give reuse rates to the model according to the number of reuse cycles reported up to this point in time. Finally, through numerical experiments, we show that our model is able to support making decisions related to the implementation of CLMS.

Production line design is one of the important research topics for constructing smart factories. Many research papers have been published concerning smart factories; however, most of them are focus on subjects such as automation, IoT (Internet of Things), AI (Artificial Intelligence) and other new technologies that are introduced to increase the efficiency of existing manufacturing lines based on the so-called philosophy of “ status-quo improvement ”. In this research, we consider a design problem for a new production line taking into consideration investment cost, operation cost, synchronization penalty and flexibility penalty. We emphasize synchronization and flexibility as new key performance indices to evaluate the level of intelligenced to the smart factory, in which a multiple number of equipment in two production processes can provide the proper number of combinations to satisfy the external demand remove. Based on a real case involving a major player in the dairy product processing industry in Japan, we focus on two core production processes in a processed cheese manufacturing line; melting process and filling process. We design the process to include investing in a multiple number of machines with different capacities, while minimizing the total cost giving consideration to investment, operations, synchronization and flexibility. Two types of decision variables are introduced representing investment and operation. Through numerical experiments and the case study, we show that the proper number of equipment and capacity can be obtained applying the proposed model. The proposed model can also be applied to those companies considering further investment in factory or production line.

This paper introduces the P3D-QAP method, which extends the prioritization and machine assignment functions of pseudo periodical priority dispatching (P3D) to post-replenishment production dealing with parallel substitute machines, upper and lower limits of inventories, and no-setup-periods (i.e., operation shifts), in addition to asymmetric setup time. Here, in the past, the P3D method has been used in semiconductor assembly production systems where several hundred product mixes are produced by simultaneously using multi-type shared resources. In an evaluation using the actual production data of an automobile parts manufacturer, P3D-QAP is compared with the extended service disciplines of EDD and a goal-chasing method, as well as the company's basic rule and results. Performance measures include three points of view: due date, resource utilization, and makespan aspects. The P3D-QAP method analyzes and much improves the main issues of due date and makespan measures, while resource utilization is maintained at a high level. These effects are believed to be due to the extended mechanism and consideration of the supply-demand dynamics in the production process.

In this paper, we conduct a case study of a company that collects and processes used home appliances and sells them as new recycled resources based on the Home Appliance Recycling Law. The target company earns sales by selling multiple resources to multiple sales destinations with different selling prices. There are various restrictions on such sales, which creates complexity. Due to this complexity, the sales amount cannot be maximized using only the intuition and experience of employees. Therefore, we propose a model that maximizes the sales amount obtained from selling each resource. As a result, using the proposed model, different sales amounts and sales destinations of each resource were obtained, achieving better results. Specifically, compared to the current total sales amount, 3.6% better results are expected. Furthermore, as a sensitivity analysis, experiments were conducted in which the credit constraints of each sales destination were varied. It was found that risk management can be performed without affecting the objective function value by setting credit appropriately.