Japanese companies have long recognized the need to establish the clear link between individual activities and organizational management. Their concerns center on organizational management relative to Research and Development. This issue holds the key to future growth in any enterprise. In this research, we attempt to create a model, in which agents assuming the R & D researchers are advancing together toward the same objective. To accomplish this aim, we attempt to express R & D activities on two dimensional planes by computer. This approach is important, because it will enable us to measure quantitative values of different activities, advancements and reciprocal relationships in R & D. We then use this model to set up a computerized simulation, and examine the output for R & D activities. From the simulation, we should be able to analyze the following questions. First, what types of relationships ought to transpire between organization and individual in these R & D research activities? Second, what human interactions should develop with each other among those who are involved in the same research agendas? Third, to what extent should researchers depend on management or technology strategy in the enterprise? Finally, what is the role and function of leaders in the organization? This study will help us to find out a number of ways by which to stimulate both research and organizational activities in private companies. Likewise, the study may hopefully be able to shed some light on the complex theory of organizational management.

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This paper deals with makespan minimization of a job shop based on the active schedule generation procedure of Giffler and Thompson. The makespan is the length of a schedule, and we try to find a minimum makespan schedule under the mechanism of depth-first branch and bound. If two or more operations conflict on a machine when generating active schedules, sub-problems are generated by fixing the start time of each conflicting operation. To select a good sub-problem, which probably leads to a minimum makespan schedule, a lower bound on the makespan is calculated by solving the one-machine sequencing problem for each sub-problem. The obtained preemptive schedules may also give precedence relations between operations to be processed on the same machine. As it is generally difficult, especially in earlier branching stages, to select a good sub-problem based on the calculated lower bounds, we also propose a simple look-ahead method to assist in the selection decision. The look-ahead procedure searches better schedules based on the branch and bound-based procedure within a pre-specified computation time, and our search procedure selects the sub-problem having the best schedule obtained by the look-ahead method. The effectiveness of the proposed approach was examined by solving 44 benchmark instances. For small-scale problems composed of five machines and 10 to 20 jobs, the proposed approach could find minimum makespan schedules within a reasonable computation time even without the aid of the look-ahead method. Middle-scale problems composed of ten or more machines and ten or more jobs were difficult to solve optimally by the proposed approach. However, the look-ahead method improved the solution quality, and the differences between the obtained schedules and optimal schedules were less than 4% on average.

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Uncertainties of operation times, due dates and other circumstances can be formulated as scenarios with occurrence probabilities. Kawasaki and others formulated a multiobjective planning problem under scenarios, and proposed a branch-and-bound-based method for generating a set of non-dominated solutions to miminize the expected criterion vector based on the occurrence probabilities. A preferred solution could be selected from among the set of non-dominated solutions, if a preference structure of a decision maker is available for the analyst. Deriving the set of non-dominated solutions, however, is a challenging, time-consuming task. Fortunately, we can show the relationships between the preferred solution for the expected criterion vector and the set of preferred solutions obtained individually for the criterion vector under each scenario, restricting the set of feasible solutions to be checked for seeking the preferred solution for the expected criterion vector. This paper proposes two methods for finding the preferred solution for the expected criterion vector efficiently by using these relationships, provided that the preference structure of the decision maker is given in advance as a value function strictly monotone on the criterion vector value, and that either a set of non-dominated solutions or a preferred solution can be obtained efficiently for a criterion vector under each scenario. Method I is a precise method so that the solution obtained through this method satisfies the preference property for the expected criterion vector. Method II is more efficient than Method I but does not always guarantee the preference property. These proposed methods are applied to a single machine, two-objective scheduling problem under scenarios to minimize both mean flowtime and maximum tardiness. This demonstrates the effectiveness of these proposed methods to reduce the set of feasible solutions to be checked for finding the preferred solution for the expected criterion vector.

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This paper deals with a sequencing problem for a mixed-model assembly line in the Toyota production system. The sequence will vary depending upon the goals of the production system. There are two important goals in the Toyota production system : 1. Leveling the load on each work station on the line. 2. Keeping a constant rate of usage of every part used by the line. Recently, many types of cars have been produced in one assembly line at Toyota Motor Corporation. Therefore, the variation of the operation times at each work station on the line has been greater than before. It has become more important to level the load on each work station. The sequencing planning system of Toyota Motor Corporation levels the load on each work station by using the total assembling operation time of each car. Hence, it has become problematic to level the load on each work station. Here, we consider the sequencing problem with the two goals as the pre-study for the development of a new sequencing planning system. There are about 300 work stations on the car assembly line. We, however, represent one work station for many work stations that have almost the same operation times. Consequently, we can reduce the number of work stations to 15. Moreover, the other goal of keeping a constant rate of part usage is considered as a constraint of the sequencing problem. As a result, we can formulate the sequencing problem with two goals as a mixed-integer programming problem that minimizes the total work overload with two goals as a mixed-integer programming problem that minimizes the total work overload with the constraint of keeping a constant rate of part usage. Since this problem is NP-hard and the size of practical problems is huge, we propose an approximation algorithm of adopting the goal chasing method and mixed-integer programming, and show that we can apply it to practical problems by numerical examples.

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For effective and efficient information acquisition, management, utilization and sharing of business transformation information based on Business System Transformation Model (BSTM), support of information technology is indispensable. This paper discusses the requirements for information technology that aims to support the operation of BSTM, and proposes the framework of XML(eXtensible Markup Language)-based support for BSTM. The concept of describing BSTM using XML is discussed first, and XML Schema to define the structure of XML documents for BSTM is defined. A number of XSLTs for various output styles are also proposed. Finally, the proposed framework is applied for cases to show its ability to describe BSTM information, to search necessary information from stored BSTM documents, and to cooperate with other applications for more integrated information management.

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Traditionally, the issue of safety stock is often discussed under independent demand, and is rarely considered under dependent demand in MRP. In the development of SCM, the safety stock in MRP becomes important, but there is little literature on the subject. This study presents an MRP-inventory simulator for Company A, and develops an effective method for initially setting of safety stock at the part-item level. In this method, it is assumed that the delivery leadtime is constant, but safety coefficients are viable. Then, the stock-out rate under constant inventory cost is compared to the same case with the ranking of safety coefficients for parts. It is concluded that a differentiation in safety coefficients is considerably the better strategy for independent demand, and the method for setting of safety stock in SCM/MRP is further improvable. In addition, it is found that the purchasing cost has a minimum with respect to the optimal safety coefficient, and the stock-out rate has the optimal safety coefficient given by the Yoko-U shape.

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