Toyokazu Nose

Optimal control of a remanufacturing system

An optimal control problem of a remanufacturing system under stochastic demand is studied. The system is formulated by a Markov decision process, which is a class of stochastic sequential processes in which the reward and transition probability depend only on the current state of the system and the current action. The models have gained recognition in such diverse fields as engineering, economics, communications, etc. Each model consists of states, actions, rewards and transition probabilities. The paper considers two types of inventories: the actual product inventory in a factory and the virtual inventory used by a customer. The state of the remanufacturing system is defined by both inventory levels. One can obtain the optimal production policy that minimizes the expected average cost per period. The paper also considers some scenarios under various conditions and shows the example of controlling the remanufacturing system.

Analysis of a product recovery system

The continuous growth in consumer waste in recent years has seriously threatened the environment. Environmentally conscious manufacturing and product recovery has become an obligation to the environment and to society. Many countries are contemplating regulations that force manufacturers to take back used products from consumers so that the components and materials retrieved from the products may be reused and/or recycled. We focus on a product recovery system in a remanufacturing system. Product recovery aims to minimize the amount of waste sent to landfills by recovering materials and parts from old or outdated products by means of recycling and remanufacturing. It should be considered when designing and managing the manufacturing systems. We propose a new analytical approach to evaluating the product recovery system with stochastic variability. This model applies the traditional inventory theory to the production/inventory management with consideration for disposal and return. The system is formulated by a discrete time Markov chain. It is composed of the states denoted by the number of the inventory, the transition probabilities between states and the costs associated with the transitions. Using the Markov analysis, we can calculate the total expected average cost per period exactly. Numerical examples are given to show the property of the management system and optimize the product recovery system.

Perishable inventory control with two types of customers and different selling prices under the warehouse capacity constraint

Abstract This paper discusses an inventory model for a single perishable product with two types of customers, high priority and low priority ones (the former buys only the newest commodity, while the latter buys either one, whether the newest or not), different selling prices reflecting the remaining lifetime of commodity and the constraint of the warehouse capacity (i.e., the capacity of own warehouse, and so the excess inventory are stored in the rental warehouse). Further demands in successive periods are assumed to be independent nonnegative random variables with known distribution functions and high priority and low priority demands are independent. Stock is depleted by high priority demand first and then low priority demand using FIFO issuing policy. Assuming two types of shortage costs, different selling prices, outdating cost purchasing cost, two types of holding costs, an optimal ordering policy to maximize the expected profit is derived.

Optimal inventory control policy subject to different selling prices of perishable commodities

In this paper, we propose a perishable inventory model with consideration of different selling prices of perishable commodities under stochastic demand. In this model, different lifetimes of perishable commodities are provided, and we consider the discriminating selling prices by different lifetimes. We formulate the model into a Markov decision process model to determine an optimal inventory policy that maximizes the expected average profit per period.

A NEW APPROACH TO ENVIRONMENTAL PERFORMANCE EVALUATION

This paper proposes a new approach to environmental-performance evaluation (EPE) with consideration for multiple types of environmental management systems (EMSs) for establishing a Plan-Do-Check-Act (PDCA) management cycle in the system. First, we summarize the background of the environmentally conscious society and manufacturing. Then, ISO14001 is explained as one of the useful approaches in realizing the environmental conscious management system. Finally, we suggest the new approach to EPE using data-envelop analysis (DEA). In this approach, we determine the input and output factors to obtain the performances of each EMS and compare the performances of the same type of companies using DEA. The result is used as information to maintain the continuous improvement in the system

Perishable inventory management with stochastic leadtime and different selling prices

Abstract This paper discusses a model for a perishable product with stochastic procurement leadtime and different selling prices. This model is a generalization of the one period horizon model of Nahmias [4,5], Ishii et al. [3] and Nose et al. [7]. For the model, the optimal ordering policy and its properties are derived.

Cost Analysis of a Remanufacturing System

This paper deals with a cost management problem of a remanufacturing system with stochastic variability such as demand, remanufacturing rate and discard rate. We model the system with consideration for two types of inventories. One is the actual product inventory in a factory. The other is the virtual inventory that is used by customer. We define the state of the remanufacturing system by the both of the inventory levels. It is assumed that the cost function is composed of various cost factors such as holding, backlog and some kinds of manufacturing costs etc. The expected average cost per period is obtained by numerical computation. We also consider some scenarios under various conditions using design of experiments and discuss the effects of the cost factors on the remanufacturing system.

An optimal lotsizing determination in a two-echelon purchasing/production system

Abstract Multi-stage optimal lotsizing problems are extremely intractable and hard to solve. This paper discusses an economical lotsizing problem in a two-echelon purchasing/production system, where material losses caused by deterioration and failure in stock and production stages are taken into consideration. With the aid of the techniques of first and second derivatives, economic order quantity (EOQ) can be expressed by means of economic production quantity (EPQ). It is possible to derive cost reductions in the purchasing/production system. This system was applied using data from a DPE (Developing, Printing and Enlarging) factory and numerical experiments demonstrated that the total cost was reduced by 22-4%.

A design for management information system by work-design technique

Abstract Management organization is constituted by three stages, i.e., strategic information system, control information system, operating information system. Coincidence of data among stages is one of the important factors to construct the management information system. In this work, we propose an algorithm in order to design management information system. The principle theory of our algorithm is based upon the idea of work-design technique by G.NADLER, Business System Planning technique by IBM corporations and data-oriented management system design method. In addition, we introduce the design tool for management information system and show how to use the tool in order to design system architecture and system flow chart.

Foreword: Special issue on recent advance in Intelligent Manufacturing Systems

Manufacturing systems are facing continuing transformational changes. The introduction of new technologies and the advances in equipment intelligence capability are having profound effects on production and logistics systems in practice. New solutions for manufacturing systems are evolving to respond to various needs. In particular, production and services are transformed from vertically integrated firms for product-based business into virtual collaborations of horizontal modular partners, while huge quantities of data are increasingly accumulated due to business integration. Intelligence and decision technologies for manufacturing systems are being developed to analyze data and to generate intelligent algorithms that enable automated manufacturing and logistics systems to control work flow, material flow, and information flow of the global supply chain networks. By seamless integration of intelligence and decision technologies, manufacturing systems have been completely changing the way we manage our factories, logistics, outsourcing, and supply chain networks. This special issue aims to address some critical issues involved in Intelligent Manufacturing Systems. This special issue entitled ‘‘Recent Advance in Intelligent Manufacturing Systems’’ contains expanded versions of selected papers from the proceedings of the 40th International Conference on Computers and Industrial Engineering (CIE40). The conference was sponsored by Computers & Industrial Engineering: An International Journal, and held in Awaji Island, Japan, July 25–28, 2010. It was hosted and organized by Kobe Gakuin University and Osaka Institute of Technology. The conference proceedings are available at the following IEEE Xplore website: http://ieeexplore.ieee.org/xpl/tocresult.jsp?sortType%3Dasc_p_ Sequence%26filter%3DAND%28p_IS_Number%3A5668158%29%26 pageNumber%3D3%26rowsPerPage%3D100&pageNumber=1. One of the themes of the conference CIE40 was ‘‘Industrial Engineering and Intelligent Manufacturing Systems’’, and the papers included in this issue are based on this theme. Among the 299 papers presented at CIE40, the authors of 31 papers were invited to submit enhanced versions of them for possible publication in the special issue. Of these, 31 papers were submitted. They were evaluated by the rigorous double-blind review