Geospatial distribution of ecosystem services and biomass energy potential in eastern Japan

In Industry 4.0, a network of enterprises and factories is constructed collaboratively and dynamically according to the cyber physical system (CPS) paradigm. It is necessary to build smart supply chains according to this concept. A network of component enterprises in a supply chain would be modeled as a virtual supply chain in the cyber world. From the viewpoint of Industry 4.0, virtualizing a supply chain is the foundation for constructing a CPS for a supply chain. The virtualization of a supply chain makes it easier for companies to study their integrating and expanding opportunities. By using this CPS, comprehensive and autonomous optimization of the supply chain can be achieved. This virtual supply chain can be used to simulate the planning phase with negotiation, as well as the production phase. In this paper, instead of specific mathematical modeling for each supply chain, a general configuration method of a virtual supply chain is proposed. The configuration method of a supply chain model is proposed as a virtual supply chain using enterprise e-catalogues. A virtual supply chain is constructed as a multi-agent system, which is connections of software agents that are automatically created from each selected enterprise model in the e-catalogues. Three types of component enterprise models are provided: manufacturer model, part/material supplier model, and retailer model. Modeling templates for these three types of enterprises are prepared, and each template is a nominal model in terms of enterprise’s behavior. Specific component-enterprise models are prepared by filling the appropriate template. Each component enterprise agent is implemented using the enterprise model selected from the catalogues. Manufacturer, retailer, and supplier e-catalogues, as well as an automatic construction system of a virtual supply chain, are implemented. Methods for developing templates for the manufacturer, retailer and supplier were provided, and the construction system for specific enterprise models (as e-catalogues) is implemented as a trial.

Automatic construction of virtual supply chain as multi-agent system using enterprise e-catalogues

Construction of a virtual supply chain using enterprise e-catalogues

This paper proposes a methodology to design production strategy considering the cutting peak demand of electricity in shipyard using the discrete event simulator and technique of genetic algorithm. First, the proposed methodology defines an organization model, product model, constraints and production strategy. The organization model is composed of workers and facilities that are defined by their amount of skill, cost and electricity consumption. The product model is defined by workflow. Work plan is calculated by a discrete event simulator that considers the constraints of electricity and work area size. The production strategy consists of the weights of nine dispatching rules. In the developed process simulator, simulation results change according to the parameters of the production strategy. In addition, an adequate production strategy is designed automatically using this process simulator and a random key-based genetic algorithm for minimizing the total cost in performing all activities. This proposed methodology was applied to several sample scenarios of assembly planning considering the cutting peak demand of electricity in shipyard. Results show that this methodology can construct a work plan that will help cut the peak demand of electricity by adequately changing the production strategy. Furthermore, this methodology also evaluates the organizational performance of the change of production strategy while considering the difference of work area size.

Design of production strategy considering the cutting peak demand of electricity in the shipbuilding industry

Dynamic Optimization Production System Based on Simulation Integrated Manufacturing and its Application to Mass Production

Designing Multi-Agent Simulation with Big Time Series Data for a Global Supply Chain System

Sales of new products peak after their release, then suddenly decline after the sales peak. This trend of sales has resulted in much waste and opportunity loss. In this study, the NM forecasting model is modified with the clustering grouping approach in addition to the original expert knowledge-based approach. As a case study, the proposed model is applied to a Japanese publishing sales case and showed better performance. This method was verified to reduce the return goods ratio through the real-time simulation of Japanese publishers’ cases.

The Cluster Grouping Approach of NM Forecasting Model for Book Publishing.

The 2011 Great East Japan Earthquake and associated tsunami destroyed cities along the Pacific coast of northeast Japan. Large areas, including the city centers, of many cities were completely destroyed. Recovery planning for those cities is one of the most important issues facing Japan. However, recovery plans should not merely rebuild the cities as they were before the disaster, but instead should attempt to add value to the cities. Our team created a conceptual recovery plan for a coastal community in Iwate Prefecture, Japan, introducing a photovoltaic (PV) power plant and storage battery system. Moreover, we proposed the optimal scale of the required PV power plant and energy storage system by using an energy management simulation. Finally, we demonstrated that a PV power plant with a battery storage system can supply the coastal community with electric power stably and can smooth the fluctuations in power supplied by a system power supplier.

Specification Design of Renewable Energy Management System for Recovery Planning of Japanese Coastal Community After Tsunami Disaster

http://ma.ecsdl.org/content/MA2012-02/10/1068.full.pdf

Evaluation Model for Used Lithium-Ion Battery Life

This study proposes a production risk management model based on knowledge utilized forecasting. The demand uncertainty of business has been a serious problem for manufactures. As the market globalization has increased the scale and speed of their business, it becomes too big and complex to manage those uncertainty business risks. There are often the cases that misleading demand forecast cause not only business opportunity loss because of stock shortage but also serious overstock at the end of product lifecycle. It is important for manufactures to obtain a reliable production planning that manage their business risk between forecast and sales result. This study proposes a dynamic reproduction decision support system by measuring reproduction risk based on knowledge utilized product’s lifecycle sales forecast. It gives us proper timing and volume for reproduction even just after products release. As a case study, this model is applied to Japanese book publishing planning and verified how it works. The results showed the twice efficient performance as much as present position under the same risk volume.

Managing Demand Uncertainty with Knowledge Utilized Forecasting

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