Journal of Japan Industrial Management Association
Online ISSN : 2187-9079
Print ISSN : 1342-2618
ISSN-L : 1342-2618
Volume 56 , Issue 1
Showing 1-17 articles out of 17 articles from the selected issue
  • Type: Cover
    2005 Volume 56 Issue 1 Pages Cover1-
    Published: April 15, 2005
    Released: November 01, 2017
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  • Type: Cover
    2005 Volume 56 Issue 1 Pages Cover2-
    Published: April 15, 2005
    Released: November 01, 2017
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  • Type: Index
    2005 Volume 56 Issue 1 Pages Toc1-
    Published: April 15, 2005
    Released: November 01, 2017
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  • Makoto GOTO, Tomoaki TABATA, Takahiro ONO
    Type: Article
    2005 Volume 56 Issue 1 Pages 1-11
    Published: April 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    This article deals with investment decision-making under uncertainty and competition. The traditional net present value (NPV) method doesn't consider the flexibility of decision-making, so project values tend to be underestimated. A real options approach that can make up for the weak point of NPV has recently been the focus, but it considers only one agent, similar to the money market. However, in actual case, a competitor's decision-making is sure to influence the project value of the agent. Therefore, the influence of a competitor's decision-making must be estimated. Game theory is effective to optimize one's own actions subject to a competitor's decision-making (strategy). Therefore, the reduction in project value due to competition is formulated by applying game theory to a real options approach. The model should be solved so that the conditional expected value is maximized. We considered a duopolistic real estate market by referring Grenadier in previous studies. While the previous study formulated two symmetric agents, we try to extend the model to two asymmetric agents. Asymmetry results in an asymmetric equilibrium exercise strategy. With this result, we are able to explain the economic phenomenon that previous studies couldn't explain. That is, a firm with superior development technology has an advantage over the firm with inferior technology. Furthermore, we indicate the change in decision-making caused by uncertainty and construction costs from comparative statistics.
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  • De-bi TSAO, Ahmad RUSDIANSYAH
    Type: Article
    2005 Volume 56 Issue 1 Pages 12-18
    Published: April 15, 2005
    Released: November 01, 2017
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    Multiple-Period IRP (MPIRP) is an extended model of the IRP (Inventory Routing Problem). It can be defined as an integrated decision-making problem of inventory replenishment scheduling (time and quantity) and delivery scheduling (time and route). Once an inventory replenishment schedule is determined, the delivery scheduling problem is solved utilizing VRP approaches. The problem is that independent inventory scheduling and independent delivery scheduling generally have contradicting solutions for delivery time. Hence, an increase in transportation cost may decrease inventory cost, and vice versa. In this paper, to cope with this contradiction, we construct an integrated inventory and delivery scheduling model, and develop an algorithm that minimizes total costs including inventory cost and transportation cost in a given planning horizon. We included a constant interval restriction of delivery on the delivery scheduling problem associated with the solution for the independent inventory scheduling problem, so to easily derive an approximate solution for the general MPIRP. Several alternative delivery-day combinations, which we call delivery patterns that contribute to the same inventory cost, were generated for each retailer under the assumption of stable demand, and the best combination of delivery patterns for retailers that minimizes transportation cost was determined using the proposed algorithm. The proposed model outperformed the existing MPIRP model in terms of total cost, reducing the total cost more than 10% in average.
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  • Masao YOKOYAMA
    Type: Article
    2005 Volume 56 Issue 1 Pages 19-28
    Published: April 15, 2005
    Released: November 01, 2017
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    Scheduling for a two-stage production system where a number of products of the same kind are produced, including machining, setup and assembly operations, is addressed. Each product is assembled with a set of several parts. The first stage is a single machine to produce parts. It can process all kinds of parts, but can process only one part at a time. Setup operation and setup time are needed when the machine starts processing or when the machine changes items (kinds) of parts. The second stage is a single assembly machine or a single assembly team of workers. The objective function is the mean completion time for all products. Machining operations, setup operations and assembly operations are partitioned into several blocks. Each block consists of the machining operations, the setup operations and the assembly operation(s) for one or several products. Parts of the same kind in a block are processed successively. We consider the problem of partitioning the operations into blocks and sequencing the parts in each block so as to minimize the objective function. A solution procedure using pseudo-dynamic programming is proposed to obtain a near-optimal schedule. A tight lower bound is developed to evaluate the accuracy of the near-optimal schedule. Computational experiments are provided to evaluate the performance of the solution procedure. It has been found that a good near-optimal schedule is obtained efficiently by the proposed solution procedure.
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  • Hidekazu OYA, Hiroshi OHTA
    Type: Article
    2005 Volume 56 Issue 1 Pages 29-37
    Published: April 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    A very serious topic for companies that perform small quantity production of various items is how to maintain flexibility and efficiency in the manufacturing system. A cellular manufacturing (CM) system that applies group technology to a manufacturing process is an effective method for managing such problems. A cellular manufacturing system simplifies the schedule and materials flow, and decreases work in-process, throughput time, materials handling cost, and so on. Consequently, the quality improves. The main aim of the cellular manufacturing system is to classify machines and parts into machine cells and parts families on the basis of similar characteristics and to create a manufacturing cell based on them. In essence, the basic information required to solve a CM problem is the machine-part incidence matrix-which consists of values of 0s and 1s, where 1 in an entry denotes the corresponding coordinate of a part that requires the service of the machine, or otherwise. However, most methods of cell formation are based on only the machine-part incidence matrix. Recently, Won and Lee proposed a type of cell formation that takes production volume into account. They consider the total inter-cell material flows, but don't take the inter-cell and intra-cell materials handling cost into account. This paper proposes a new economical cell formation process that considers production volume. That is, this paper presents a method that improves the existing coefficient of similarity, a heuristic approach using a non-stratified clustering step and a cell formation valuation function that takes cell layout into account. Finally, the optimum cell formation is derived from an economical viewpoint.
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  • Ippei NAKASE, Yasuhiko TAKEMOTO, Ikuo ARIZONO
    Type: Article
    2005 Volume 56 Issue 1 Pages 38-45
    Published: April 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    In recent years, build-to-order manufacturing systems have been increasingly adopted, because of their ability to meet the various needs of customers and progress in Internet businesses. Therefore, it is necessary to construct a build-to-order manufacturing system that is capable of reducing the inventory cost of components and opportunity loss in busy periods and decrease the operating cost of manufacturing facilities and wages for employees in idle periods. Therefore, we consider a build-to-order manufacturing system with a standby facility in addition to a permanent facility, and investigate the performance of the system. In this case, we describe the investigated system as a queueing model with a permanent service counter and a standby service counter that has different functions than the permanent service counter. We adopt the graph technique developed by Billinton and Kumar for the purpose of obtaining the formulae of the steady-state probabilities of the system, and the number of times per unit of time that operation of the standby facilities begins and is ended. Further, optimal operation for the activating time interval of the standby facility is investigated.
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  • Rie IKENA, Hiroshi OHTA
    Type: Article
    2005 Volume 56 Issue 1 Pages 46-53
    Published: April 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    Inventory policies are crucial to all buyers and suppliers. The rapidly developing global market place has made the industrial environment more competitive. As a result, recent research trends in inventory management and supply chain management have also focused on the impact of customer behavior on the system and the interaction or relationship among supply chain partners. A key question that needs to be addressed in improving supply chain efficiency is managing the interaction or relationship among the supply chain partners. The flow of business, such as information and logistics, is improved from the viewpoint of the whole supply chain (i.e., including the customer), and it becomes a target to have the optimum outcome as a complete system. Recently, Erenguc et al. pointed out that a dominant firm in the supply chain usually tends to optimize locally with no regard to its impact on the other members of fostering more cooperative agreements in the chain. Sharafali et al. presented some stochastic models for understanding the cooperation between the supplier and buyer, and discussed the effect of cooperation in a reordering point system. They considered a two-stage supply chain with one buyer and one supplier. But most of the supply chain has many stages. So in this study, we utilize a supply chain system consisting of a supplier, buyer1 and buyer2, and analyze the effect of the reordering point system for the supplier and two buyers when they cooperatively determine their optimal order quantities. For that purpose, we first formulate their respective inventory policies and decide their optimal ordering quantities and reorder points to minimize their costs. Next, we investigate the case where the supplier and two buyers jointly determine the optimal ordering quantities and reorder points to minimize total costs. Based on the result, we also analyze a supply chain system consisting of the supplier and many buyers, and consider the importance of cooperation among supply chain partners.
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  • Yoshiaki ISHIHARA, Shusaku HIRAKI
    Type: Article
    2005 Volume 56 Issue 1 Pages 54-63
    Published: April 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    This paper aims to propose a planning method for a vehicle routing problem in a package reuse system. Recently, from the viewpoint of environmental protection, many manufacturers construct reuse and/or recycling systems for disposed products. With the operation of these reuse and/or recycling systems, effective systems for reverse logistics in which disposed products are collected from customers are needed. In this paper, a vehicle routing problem for a package reuse system is considered. Disposed packages are transported in many vehicles that would normally travel to their destinations or return to their locations of origin empty. We formulate a vehicle routing model for a mathematical programming problem, which maximizes the total transportation quantity of disposed packages, propose a planning method that uses limited information with respect to vehicle routes, and clarify the effectiveness of our proposed method using some numerical examples.
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  • Type: Appendix
    2005 Volume 56 Issue 1 Pages App1-
    Published: April 15, 2005
    Released: November 01, 2017
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  • Type: Appendix
    2005 Volume 56 Issue 1 Pages App2-
    Published: April 15, 2005
    Released: November 01, 2017
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  • Type: Appendix
    2005 Volume 56 Issue 1 Pages App3-
    Published: April 15, 2005
    Released: November 01, 2017
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    Download PDF (75K)
  • Type: Appendix
    2005 Volume 56 Issue 1 Pages App4-
    Published: April 15, 2005
    Released: November 01, 2017
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    Download PDF (75K)
  • Type: Appendix
    2005 Volume 56 Issue 1 Pages App5-
    Published: April 15, 2005
    Released: November 01, 2017
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    Download PDF (75K)
  • Type: Appendix
    2005 Volume 56 Issue 1 Pages App6-
    Published: April 15, 2005
    Released: November 01, 2017
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  • Type: Appendix
    2005 Volume 56 Issue 1 Pages App7-
    Published: April 15, 2005
    Released: November 01, 2017
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