In service design, the quality of service content is very important for the customer service value. However, the content cannot be designed to be a certain value because the providers have limited time and monetary resources to improve the content quality. In this study, we propose a method of multi-objective optimization for balancing of the resources and the customer satisfaction using particle swarm optimization. In addition, we verify the proposed method with a zoo service.

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To provide good service, evaluations taken from the viewpoint of customers are necessary. Therefore, customer satisfaction is one of the important factors for evaluating service. Customer satisfaction is determined by the gap between the quality of service and customer expectations. Customer expectations change when customers obtain information about the service. Therefore, by showing information to customers relating to the service quality, providers can influence the heterogeneity of customers. This study aims to develop a methodology to analyze customer satisfaction with services. For this objective, we propose some service models. For the customer expectations model, customer expectations are calculated based on the information obtained in the process of the service. The service process model represents situations in service receiving. Through simulations on these models, customer expectations and quality of service can be calculated. To analyze the effects of showing information on customer satisfaction, this paper calculates the customer satisfaction regarding waiting time through simulations using the case of the Stands Café, an actual café located in the terminal station in Tokyo. For the simulation, we build a service process model for the café and we model customer expectations representing service situations. The situations include how many other customers are in the café, what those customers order and whether providers inform customers about waiting time or not. Six conditions are simulated: three patterns of rate of orders and two patterns of interval of customers coming to the café. From the result of the simulations, the effects of the interaction on customer satisfaction are analyzed.

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A Bayesian network is one of the useful models for pattern recognition problems and it has the features of both stochastic prediction and causal models. A Bayesian network expresses the causal relationship between variables with directed graphs. Usually, the structure of a Bayesian network is statistically estimated using a set of training data and the model selection has been applied in conventional methods when Bayesian network structures were estimated. However, it is not necessary to choose one model for the purpose of prediction. From the viewpoint of Bayesian statistics, it is well known that prediction using the mixture model on model class is Bayes optimal. In general, the mixture model that is given by a weighted sum of all models with the posterior probability on the model class is the Bayes optimal prediction. In this paper, we propose a new Bayes optimal prediction on a Bayesian network model class using the mixture model. A mixture model sometimes becomes a complex expression due to the weighted sum of all models on a model class, and it results in loss of the usefulness as a causal model. Since the easiness of interpretation is one of the merits of a Bayesian network, using a mixed model only for improvement in predictive accuracy may lead to losing the merit of a Bayesian network. Therefore, we propose a new method that is configured with the mixture model utilizing the characteristics of the Bayesian network by organizing model classes properly. Furthermore, we propose a method to quantitatively assess the strength of the causal relationship between the nodes on the mixed Bayesian network model. In addition, the effectiveness of the proposed method is clarified via a numerical experiment on an application to a prediction problem of buying and selling of shares in a stock market.

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This paper presents a benchmarking problem in scheduling that reflects the business environments in today's manufacturing. In addition, it shows a result obtained by a solution based on O2O Technology. This study treats the multi-item multi-process multi-machine production system involving alternative machines. The decision features in the problem consist of production order release, loading, lot sizing, lot sequencing and dispatching. The presented problem data reflects the following situations: The situation where it is difficult to secure profit unless all of the three conditions of strict adherence to due dates, not holding extra work-in-process, and throughput increase are satisfied simultaneously; and the situation where large fluctuation occurs in a time series of shipping requirement data related to quantity and timing, and large disparity of attributes among items is seen.

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The purpose of this study was to clarify the work position under a light physical load during an upward-pushing task in the supine position. We quantitatively evaluated the relationship between upper-limb load and work position. Eleven subjects were required to perform a static upward-pushing task with a single hand while elevating their upper limb in the supine position. In all, nine experimental conditions were set up, and three horizontal positions for the pushing portion (ear, shoulder and elbow level) and three heights (60%, 75% and 90% of the upper-limb length) above the ground were selected. Upper-limb loads were evaluated on the basis of joint moment, electromyogram and subjective assessment. The results show that the pushing portion at shoulder level and at a height of 90% of the upper-limb length reduced the upper-limb loads and increased the ease of task performance. Thus, the recommended upper-limb position in the supine position clearly differs from that in the standing position.

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This paper proposes a new simultaneous business design method that is called the global relations diagram of function and demarcation (G-RD). When enterprises aim to perform BPR, they need to design not only business functions but also the relations between businesses. Generally, BPR projects focus on the business functions, and the relations between businesses are not fully designed in many cases. In order to solve this problem, the modeling approach is needed which details not only the business functions but also the relations between businesses simultaneously. This paper introduces the cases where this simultaneous business design method was applied to the BPR projects and the effectiveness of this method utilizing G-RD is evaluated.

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In this paper, we propose a method of text classification using a Bayes coding algorithm, one of the efficient data compression methods. The Bayes coding algorithm gives the Bayes optimal data compression over the tree source model class. When data is compressed by the Bayes coding algorithm, the probability structure of information sources is implicitly estimated from the compressed data. Therefore, we can expect that the implicit estimation of data compression can be utilized for other purposes, especially for the document classification problem. As for the document classification using data compression methods, ZIP format and context tree weighting methods have been proposed. However, these methods do not have Bayes optimal compression and use the compression ratio as a similarity measure between documents for classification. In the Bayes coding algorithm, a weighted mixture tree given by the compression phase can be used for estimated probability structure. Tree source is a class of Markov sources and it is possible to measure the divergence between the tree sources with the same structure. However, the Bayes coding algorithm outputs different tree structures based on the data sequence to be compressed. Since the tree structures derived from documents are different from each other, it is difficult to measure the divergence between them just as it is. This paper proposes a new method to change the structures of weighted mixture trees into the same tree structure to be able to measure the divergence. Using the divergence between trees estimated by documents, the documents can be classified. Moreover, the effectiveness of the proposed method is clarified via a simulation experiment for the document classification with natural data.

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This paper focuses on products such as office machines that involve many manual assembly procedures. A training method in which a trainee assembles and disassembles a product and prepares the parts onto a kitting-tray is proposed. The design of the kitting-tray is based on the relationship between parts. A design step for the kitting-tray, which applies the systematic layout planning method, is presented. A kitting-tray is then made by following the design step. Through experiments where a wooden motorcycle model is assembled, the proposed training method is compared to a typical training method. From experimental results, it can be seen that the proposed training promotes faster learning than the typical training. Secondly, speed in recollecting work procedures and assembly motions are still retained seven days after training. Finally, the proposed training enhances memorizing of the product structure and retention of the memory.

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This paper proposes a way to encourage discussions in the decision-making process concerning current business planning using the business system transformation model (BSTM) in order to improve the ability to design or modify a business. This method allows users to appropriately design or modify a business by accumulating the information regarding discussions between stakeholders, and then using the accumulated information to improve the discussions and modify the business plan. After one company, listed on the first section of Tokyo Stock Exchange, applied this method to the product planning stage, it was determined that this method is useful by identifying the multiple benefits of utilization and statistically analyzing the user's rankings of the benefits through questionnaires and interviews. Finally, operational problems regarding the labor hours to record the opinions expressed in meetings and possible solutions are also discussed.

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Japanese cell manufacturing by multi-skilled operators is unique and different from the western cell manufacturing. Normally, there is no buffer station between two adjoining operators in the divided cell. Instead, a helping (shared task) zone to absorb the variation of operation time is utilized. However, an experimental cell line limited to only one buffer at each station appeared recently in a plant of a company famous for cell manufacturing. This paper aims to elucidate the complementary effect on throughput of introducing buffer stations in the divided cell with helping or shared task zone systems to absorb the variation. For that purpose, DLB (Dynamic Assembly-Line Balancing) is utilized, which has a common task system similar to that of the helping zone and assumes buffer stations from the first time. We formulate our model by customizing hand-over work rules and incorporating new concepts such as a decision point unit in order to adjust to cell manufacturing settings. After examination by using a Markov chain, a simulation is conducted based on the factorial experimental design considering four factors: capacity of buffer, width of helping zone, granularity of decision point, and coefficient of operation variation. Two major findings are obtained depending on the capability to set operation times. When the variation of operation time is low, any complementary effect between the helping zone and introduction of buffer stations is not observed. Countermeasures are recommended by introducing only buffer stations without any shared task, or expanding helping zone without buffer stations. On the hand when the variation of operation time is high, a complementary effect on throughput can be notified, and this effect becomes larger as granularity gets finer.

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The Great East Japan Earthquake hit the north-eastern parts of Japan on March 11, 2011. A shortage of relief supplies for many affected people just after the occurrence of the disaster caused an increased loss of lives. One of the most important issues in disaster relief operations (DROs) to deliver relief supplies is the supply chain management (SCM) approach. To date, there have been some studies on SCM for DROs, but there are very few papers in the literature which investigate the situation of damaged infrastructure and lack of information about the demand of relief supplies at the time a disaster occurs. In this paper, we examine how DROs in the Great East Japan Earthquake were performed in terms of the principles, which were required for the design of SCM for DROs by applying quantification method 3. We also discuss whether the DROs for the earthquake were appropriate or not.

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When making a schedule in a home help station the scheduler has to ensure each service can be provided on time by an appropriate helper. The scheduler must consider the working hours and intensity of the work, the traveling time to and from the users' home and the availability of each helper to complete a schedule. This scheduling is known to be difficult and time consuming due to the quantity of such constraints. Although computer-aided support systems are commercially available, it was found that such systems are not being utilized to obtain feasible schedules. In this paper, in order to understand what kind of system is needed, we made observations of the schedulers creating schedules on paper by hand. We then observed schedulers using a computer-aided support system in order to capture the issues involved using the computer systems. From the observations, we summarize several requirements that are needed to develop a useful computer system.

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