Because of the crucial importance that is placed on the quality of life in the Western world, health care is an indispensable industry that exists within the society today. In this paper, we examine the use of electronic health records in the healthcare industry and their integral role in delivering effective hospital supply chain operations. In specific, we look at HealthConnect[○!R], a hospital information system used to realize effective hospital supply chain operations by Kaiser Permanente, a premier healthcare provider in California and in other US states. Based on the success of their efforts for adopting electronic health record based system, we present lessons from Kaiser Permanente's experience with hospital information system for other organizations.

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In the case where tim.e series data is generated by dynamic systems with stationary process, we consider a problem, in which a control plan of a treatment variable is conducted in order to bring a response variable close to a target value with variation reduction. In order to achieve this aim,, we describe the data generating process in dynamic systems as directed graphs with the frequency domain. In this framework, we provide the formulation of an optimal control plan concerning a certain treatment variable. Based on the formulation, we clarify the properties of causal effects when conducting a control plan. The results enable us to evaluate the effect of a control plan from time series data.

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In this research, Lagrangian relaxation algorithms are proposed for Hybrid Flow-Shop (HFS) scheduling problems. Conventional HFS consists of a series of production stages, each of which has several identical parallel machines and no buffer spaces are considered. Jobs are processed through all stages in the same direction. In the previous researches, they are assumed that the capacity of buffer is infinite. But in the actual manufacturing environment, the maximum capacity of buffer is limited. That's why HFS with limited buffer is studied in this research. Unrelated parallel machine is the general and important model of parallel machine, because this is the model which can consider the difference of machining performance with large flexibility. But most studies of HFS deal with identical parallel machines which do not consider the machine abilities. These are the motivation to study HFS scheduling problem with unrelated parallel machine in this study. In this research, the objective function is to minimize the total weighted tardiness and the earliness for each job. Three methods of Lagrangian relaxation algorithms are proposed to solve the HFS scheduling problem with limited buffers. In most studies about Lagrangian relaxation algorithm, the machine capacity constraints are relaxed and each stage is scheduled separately. But in this study, not only the machine capacity but also the precedence constraints are relaxed to schedule all stages together. The results of numerical experiments showed that the proposed methods perform very well especially for large scale problems.

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This paper presents a Tabu Search approach combined with extended saving method for Multi-depot Vehicle Routing Problems with Time Window. The problem can be divided into two sub problems; the customer assignment problem and the k single-depot vehicle routing problems, where k is the number of depots. For the former problem, we propose an tabu search approach in which neighborhood reduction can be effectively performed while keeping solution quality. For the latter problem, we extend the well-known saving method to treat new constraints. By computer experiments, we confirm the effectiveness of our proposed method.

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Under stochastic demand and deterministic processing times, we discussed a single-part JIT production system with the production-ordering and supplier Kanbans and derived the stationary distributions of the backlogged demand. In this paper, we extend the system to a multi-part JIT production system, deterministic processing times and withdrawals with lead time. These conditions are more realistic than the previous papers. We formulate the system and show that a shortage of some parts degrades the performance of the system in full production case. A part with the minimum average number of supplier Kanbans which has great influence on the performance is especially focused. Moreover, the occurrence probability of backlogged demand is calculated and the importance of increasing the number of Kanbans of the parts with the minimum average number of supplier Kanbans is shown by using simulation.

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We examine a technique for solving the scheduling problem in the flexible flow shop, where the processing time is uncertain. The conventional scheduling method for the flexible flow shop is usually based on afirst-in-flrst-out rule at each stage. However, there is no guarantee that a good solution can be obtained by this method, especially because of the uncertain processing time. In the present study, we attempt to develop a scheduling method that provides a robust schedule against uncertainty in the processing time. In the present paper, we consider a procedure by which to generate robust schedules depending on the degree of uncertainly.

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This paper presents an effective heuristic algorithm for solving the multilevel lot-sizing problem, which is an important decision making process in manufacturing production systems and a well-known example of a combinatorial optimization problem. The heuristic algorithm is based on the soft optimization approach. It has been reported recently that this method can, with high probability, result in a good enough solution to the multilevel lot-sizing problem. So far, however, it has proved to be rather inefficient. This paper develops an algorithm based on segmentation, which uses the structure information of the lot-sizing problem in sampling, so that better performance can be achieved. The effectiveness of the heuristic algorithm is shown by experiments, which compare the soft optimization approach with the genetic algorithm.

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We examine the computerization activities of local governments in an attempt to determine the relationships of architecture. Focusing on the characteristics of design, development, and operation of computerization, we propose the architecture of computerization based on the manufacturing architecture. Furthermore, we analyze several cases and try to classify the information activities of the organizations. To help actual situations, we describe the general architecture of computerization. We apply the manufacturing architecture to computerization activities and propose the architecture to be focused on during the different phases of computerization. Next, we analyze the case of local governments with advanced computerization and discuss the changes from the integral type to the modular type during the different phases of computerization. Finally, based on the above consideration, we show the classification of the computerized activities of local governments and the future use of computerization.

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Neural Networks (NN) technique is widely used to solve problems with complex or unknown input-output relationships. In this paper, NN concept is implemented in order to solve the disassembly-to-order (DTO) problem. DTO is a system where a variety of returned products are disassembled to fulfill the demand for specified numbers of components and materials. The main objective is to determine the optimal number of take-back EOL products for the DTO system which satisfy the desired criteria of the system. Since take-back EOL products are in uncertain conditions, model formulation is challenging. NN, which is capable of recognizing the hidden relationship or pattern ofa given input-output data, is a very promising technique to solve the DTO problem. In this paper, we use NN to solve the DTO problem. First, NN is trained by a set of data which has the components demanded as input and optimal number of take-back products as output until the relationships between the two are recognized. After that, the trained NN is used to obtain the optimal number of take-back products for the component demands with unknown solutions. A numerical example is considered to illustrate the methodology.

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When a product reaches its end-of-life (EOL), it can be reused, remanufactured, recycled or disposed of. Often, in most of these processes, a certain level of disassembly may be necessary to separate components and materials. Therefore, optimal disassembly sequences are important to increase the efficiency of the disassembly. Since the complexity of the disassembly sequencing problem dramatically increases with the increase in the number of products and component types, we propose an evolutionary computational approach to solve it. Specifically, we use Genetic Algorithm (GA) to solve the problem. A numerical example is considered to illustrate the use of this methodology.

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