This paper researches the problem of allocating energy consumption in a contracted manufacturing company. The company accepts customers' manufacturing orders and generates a production schedule that minimizes the total energy consumption composed of processing, idle, and common energy consumption. The production environment is a flexible job shop, and each customer requests to fulfill the due date. A mixed-integer linear programming model developed explores the optimal solution that minimizes the total energy consumption while satisfying the due date constraint. As customers are sensitive to the energy consumption caused by their products' production, allocation of energy consumption among customers becomes a research issue. Among three components, the common energy consumption requires further study. The present study proposes three methods: proportional allocation, Shapley value, and unit time allocation. Numerical experiments were implemented under the condition of three customers, ten jobs, and seven machines. Results indicate that each method produced different allocations among customers. Proportional allocation roughly follows the solution of local optimization that considers each customer separately. Shapley value realized the same allocation between two customers with the same set of jobs. Unit time allocation depends on the schedule generated. It is not easy to estimate the allocation from the solution of local optimization under Shapley value and unit time allocation. The present study suggests adopting the most suitable allocation method based on these characteristics.

In this study, we propose a traffic flow simulation model using the cellular automaton method for the purpose of reducing the environmental load along roads in the southern part of Kawasaki City. Specifically, in the case that multiple travel routes are available from a given departure point to a destination, the route is chosen such that the load on the atmospheric environment is small. Additionally, if a route can be found that is economically advantageous, such as one that reduces the required travel time, ensuring that businesses are aware of these routes could encourage changes in the travel routes of large vehicles that have a significant impact on the atmospheric environment, which would in turn improve the roadside environment by controlling the traffic volume and concentration of vehicles in the area. In the proposed model, we reproduce the behaviors of large and medium-sized vehicles using different sets of rules. Numerical experiments are conducted using the attribute data of actual routes as parameters, focusing on a one-hour period with the most traffic. Then, we examined and considered how the change in the number of large vehicles that use the route affects NO_x and CO₂ emissions, travel time, and fuel efficiency.

In recent years, the awareness of environmental issues has increased both in Japan and overseas. Although there are various approaches to environmental problems, the waste recycling rate is increasing year by year. This study is a case study that focuses on companies that specialize in recycling used home appliances. The target companies collect the used home appliances, store them in external warehouses under different types of contracts, dismantle them, and sell and dispose of their parts. The series of operations is complicated because the amount of used home appliances collected fluctuates greatly depending on the season. Accordingly, basing operations only on the experience and intuition of employees may cause unnecessary costs. Therefore, in this study, we propose and optimize a model that comprehensively considers the operations of a recycling center. The objective function of the proposed model is profit maximization taking into account the sales of valuable resources, waste disposal costs, transportation costs between the recycling center and the external warehouse, and storage costs in the external warehouse. Numerical experiments have shown that the proposed model can increase profits by approximately 1.5% compared to partial optimization, which focuses on some of the tasks that exist in previous studies. In addition, it was shown that the proposed model can increase profits by approximately 6.5% compared to the actual values of the target companies. Furthermore, we conducted a sensitivity analysis on the amount of used home appliances received and the processing capacity of the recycling center. As a result, it is shown that it is extremely important to receive the appropriate amount according to the processing capacity, and that corporate management is required to make an appropriate investment in the processing capacity.

A problem has been recognized that even though the economic activity of a proportion of the service industry in Japan has recently been increasing, productivity remains low. In order to overcome this problem, a large number of improvents in the service industry have been reported based on scientific and technological aspects which have conventionally been used applying into practice on-site experiences and intuition.

This research reports a case study that implements improvements in efficiency of a Japanese restaurant franchise that sells carry-out side dishes and lunch-box meals in addition to providing eat-in meals. In order to analyze the amount of sales lost due to a long queue, a queueing model has been built that utilizes a discrete event simulation tool. Based on the derived analysis results, we examined measures for improving the operational efficiency of the restaurant based on the results of this work study, which have been advocated as traditinal industrial engineering methods.

Since conventional work studies generally assume that the work to be analyzed is repeatedly performed, it is difficult to analyze operations that multi-tasking workers support on an irregular basis. The point of this research is that the simulation tool and the work study are utilized in combination to investigate the effectivenesses of the support workers and for considering plans to realize improvements.

This research may be extended to other systems since the utilization of support workers helps not only in services systems, but also in logistics and production systems. Through this case study, our aim is to expand the possibility of the practical use of industrial engineering method.

As a case study, this paper proposes a method to detect improvements in dismantling processes through more efficient operation of vehicle dismantling machines for end-of-life vehicles. Using the tracking data collected by a sensor attached to the vehicle dismantling machine, the proposed method identifies processes to be learned and processes to be improved, compares measures for operating and planning skills between the two types of processes identified, and detects ways to improve the processes. The proposed method is applied to a case of dismantling works in a company, and the results show the effectiveness of the proposed method.