The focus of this paper is the scheduling problems in the lot production lines of the Toyota production system (TPS). As the problems are serious in not only the TPS, but also other production systems, the author introduces some results gained by studying the problems in hope that the problems will be researched more widely. It is well known that lot production lines of the TPS are operated utilizing an order point system. However, a problem exists in that the setup schedule for each lot production line when utilizing this method is not made in advance; thus, setup cannot be implemented based on the schedule. As such, another method is adopted for the TPS. This other method is not well known, so it must be analyzed and scheduling algorithms developed for it. Moreover, the author demonstrates the properties of operating lot production lines controlled by the method. As a result, it is shown that this method can be utilized for not only the TPS, but also other production systems. There is another problem as well. Generally speaking, it is very difficult to reduce the number of employees working on a lot production line in proportion to the decreasing production quantity of the line. For this, the author researches how to improve worker productivity on lot production lines when the production quantities of the lines drop. Though this problem is basically a scheduling issue, it is shown that this problem should be investigated from a wider perspective.
In this paper, we focus on the applications of operations research (OR) for problems experienced in sugarcane harvest planning. The case of the Thai sugarcane industry is studied. Stochastic factors that affect harvest planning decisions are addressed. We also discuss the challenges involved based on the present circumstances of the sugarcane industry in Thailand. The problems we discuss in this paper include the harvester routing problem, the field merging problem, and the problem of integrating the labor force and harvester in harvesting operations. Guidelines of the OR model developed to solve the stated problems are suggested.
Japan needs to develop data analytics talent quickly in order to catch up with the trend of using data for making business decisions. The Ministry of Education, Culture, Sports, Science and Technology started a three-year project to develop so-called data scientists. This paper reviews the findings of the first year of the project, and discusses future challenges.