Some heuristic methods have been proposed for generating a set of nondominated schedules to the two-objective stochastic scheduling problem to minimize both the expected value and the variance of total flowtime. However, there is no effective method for selecting the preferred schedule from among the set of nondominated schedules. This paper presents some new categories of the nondominated schedule sets related to this type of stochastic scheduling problem, and constructs an interactive computer system for selecting the preferred schedule using these categories.

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This paper presents an approach to determine the optimal pickup point on a material handling network. The objective is to find the optimal point which will minimize total cost of material handling network. Fuzzy Theory also applied to formulate the material handling cost. We provide a basic model, formulated as Fazzy material handling model. Linguistic variable applied to make the membership function. This paper used the trapezoidal-shaped for it. It has the upper and the lower limit material handling cost. Several alternative formulations are also included. Numerical examples are presented for several MHN in the material flow path.

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The purpose of this paper is to clarify the optimal efficient work load for a worker working in a standing posture in an assembly line from the viewpoints of his bewilderment occurring as the work gets more complicated and his acting are a changes when the work range has expanded. The relation among the bewilderment factor, the number of parts handled, work skill, the work range and the acting area was investigated and the following results were obtained : 1) The working time will clearly be longer if more than 7 kinds of parts are handled and there is a straight increase. 2) It is possible to calculate the working time from the number of parts handled and the acting area, allowing for the bewilderment factor. The optimal efficient work load reveals from these results.

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In the previous paper, a layout technique (CARD-SA) to solve the large size facility layout problem was proposed. In this paper, the CARD-SA is applied to the refinery and petrochemical plant problem. The stock and flow data of the problem is same with the existing plant data, and several realistic constraints are considered. The number of equipment to assign is over 100,and the difference of each equipment size is very large. The objective function is to minimize the total length of pipe to connect the equipment. Numerical tests are suggesting, the CARD-SA is effective to solve the problem.

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The purpose of the present study is to examine how work commitment (including job involvement and organizational commitment) moderates the relationships between role stressors (including role conflict and role ambiguity) and psychological symptoms or the intention to leave in 262 male workers. A hierarchical multiple regression analysis indicated that 7 of the 12 interactions tested between role stressors and work commitment were significant. Further analysis suggested that high levels of work commitment exacerbated the relationships between role stressors and psychological symptoms or the intention to leave. These results provide some support for the hypothesis derived from identity theory. Implications of the results and future research issues are discussed.

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BP (Back Propagation) learning algorithm based on Conjugate Gradient Method is an effective method for high-speed learning. This method has originally been introduced for the problem to minimize the quadratic function and is guaranteed to converge to it in the number of times equal to the search surface dimensions. It is also applied as it is to a generalized function like BP learning algorithm, because in this case too, it can approximate locally to a quadratic function. However, it fails to converge in the number of times equal to the search dimensions in this case, requiring the step to take the second approximation process after the search dimensions reached a certain number. This step is called "Restart". However, this method occasionally falls into local minimal depending on search surface, because it locally approximates a generalized function to a quadratic function. The cause is considered that it continues searching until its number of times reaches the conventionally determined number of times before the restart, even if the approximation to a quadratic function is low in precision ; therefore this restart method is considered to improve the search efficiency when the quadratic function approximation is low in precision. We therefore propose an efficient learning algorithm based on Conjugate Gradient Method. The method proposed here can decide whether the restart is needed according to new parameters which evaluate the quadratic approximation accuracy at every search point. We call the proposed method "An improved Conjugate Gradient Method".

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A problem to minimize the number of tool exchange times is one of tool module design problems for a numerical controlled complex machine. In this study, we propose two approximate solutions that take note of tools and parts to generate a smaller number of tool modules. As the results of computational experiments, in the first solution to take note of tools, we find a rule to select "seed tools", which can produce a part using the largest number of tools, and also choosing additional tools, for which the ratio of the number of common tools with "seed tools" is maximal, and allow us to improve the solution. In another solution paying attention to parts, we find a rule to select them with minimum degree of node, which indicates the relation between parts, and chooses additional parts, which use minimum number of tools, to have the nearly optimal solution in some cases. This solution has the merit of being able to confirm the accuracy of an approximate solution based on its lower limit value.

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Optimization problems such as system reliability problems are generally formulated as nonlinear integer programming (NIP) problems. In recent years, neural networks (NN) approach is receiving much attention and applied for a variety of optimization problems. The advantages of NN approach lie mainly in which the computation is completed in massively parallel architectures and that optimal solutions to renewed parameters are adaptively obtained as new equlibrium points under the new environment. However, when we apply NN to NIP problem, it is difficult for us to obtain integer solution. In this paper, we propose a method for solving NIP problem by applying NN (NIP/BB-NN) to continuous relaxation problem of branch and bound (BB) method. The efficiency of the proposed method is demonstrated by numerical experiment.

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Recently, multiple criteria decision making (MCDM) has been well established as a practical approach to seek a satisfactory solution to a decision making problem. Goal programming (GP) is one of the most powerful MCDM methods. In the realistic decision making problems, many GP problems involve a large number of 0-1 decision variables. In this paper, we propose an algorithm for solving 0-1 nonlinear goal programming problem. This algorithm has an effect on the computer storage required and the computational time. In order to illustrate the effectiveness of the algorithm proposed here, we deal with a numerical example of the reliability optimization problem for allocating redundant units.

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