A workforce scheduling system to manage both static optimization and dynamic re-optimization for field service is proposed. The problem is formally defined as minimizing the total labor cost while satisfying various constraints. A heuristic algorithm for optimizing the initial scheduling and re-scheduling corresponding to unexpected task inputs is proposed. The proposed algorithm effectively determines the organization of work groups by integrating two work groups on the basis of an integration priority indicator. A prototype is implemented as the web system. The proposed algorithm is evaluated by applying it to the operation and maintenance service of IT equipment in a Japanese field service company. The proposed algorithm can reduce the total travel distance by 20.9% and the required total number of workers by 12.9% while observing time constraints compared with the conventional manual scheduling. The results show the proposed algorithm can manage unexpected tasks and establish an efficient work schedule.

In supply chain, catastrophic disasters such as earthquakes may prevent procurements from suppliers, which causes losses in retailers. Decentralizing orders and urgent stocks can reduce the losses, but cost the retailer. And, the losses by the catastrophic disasters are uncertain. In order to support planning of decentralizing orders and urgent stocks, we propose the evaluation method of procurement risks that are Conditional Value at Risk (CVaR) of the losses, which are often applied for a rare event causing a heavy loss. Because the uncertain procurement risk is reduced in return for the cost, the proposed method evaluates the decentralizing orders with the urgent stock based on real option approach.

In order to save energy with preventing the lack of the resources on servers in data centers, we address the dynamic optimization of virtual machine placement. A decision problem of the virtual machine placement has been formulated as Bin Packing problem. Conventional methods repeat to solve the problem at regular intervals. Live migration is used for changing the virtual machine placement, but some of live migrations are unnecessary; the virtual machine is migrated repeatedly between the physical servers. For the purpose of reducing the unnecessary live migrations, we propose a dynamic optimization of virtual machine placement by resource usage prediction. The proposed method predicts the future resource usage by Auto Regressive Model. The proposed method decides the virtual machine placement by solving the Bin Packing problem with the predicted resource usage.

This paper addresses a scheduling method of air conditioner operation using workers daily action plan. In order to run an air conditioner for both energy saving and each worker's comfort, the proposed method decides how high and when to set the temperature as operations. Comfortable temperature for workers is decided by using PMV(Predicted Mean Vote), and the temperature and power consumption are estimated by indoor environment simulator. To make an operation schedule considering both each worker's comfort and power consumption is realized by reinforcement learning. Experimental results show that our proposed method can generate a schedule to satisfy each worker's comfort at all times and reduce power consumption.

This paper addresses detecting anomalies of individual services from their total resource usage on web-based system. Because the total resource usage is a linear combination of the number of accesses to each service, multiple regression analysis can be applied to estimate a resource usage per an access to each service as regression coefficient. However, the regression coefficients differ from the resource usage per an access of the services, which is caused by unstable resource usage per an access. We propose a method based on a multiple correlation coefficient to identify anomaly time and anomaly services. The proposed method identifies anomaly time when the correlation coefficient is decreased. And the proposed method identifies the anomaly service by judging whether the correlation coefficient is increased or not after the selection of the dummy variable. The experimental result shows that the proposed method can identify all the anomaly time, and improves precision rate and recall rate of detecting anomaly services by 20% at least, respectively.

This paper addresses support for making the social consensus on risk-reducing plans among experts and stakeholders. Parameters are given on risk-reducing plans in order to decide which risk-reducing plans are performed, but the parameters are so uncertain that experts can not set values to parameters. So, it is difficult for experts to set an agreed value to the parameters. Due to the uncertain parameters, if experts acquire the agreed combination of risk-reducing plans, the evaluation values are also uncertain. Therefore, it is difficult for stakeholders of risk to understand the evaluations. We propose the consensus making support system that enables experts to decide the combination by qualitative values and enables stakeholders to understand the evaluations by probability distributions. The proposed system decides the combination by converting qualitative values to quantitative values by random numbers and derive the probability distributions by Monte Carlo simulation. In order to realize these requirements, we apply the qualitative and quantitative hybrid simulation to the proposed system. As a result of the application to a consensus making problem, it is confirmed that the proposed system is effective for consensus making. And, in order to improve the system, it is necessary to support adjusting parameters to acquire the agreed combination.

This paper addresses a new approach to find business risk factors based on business scenarios derived from qualitative-quantitative hybrid simulation. To investigate business risk factors under uncertain business environment, cause-effect interpretation of business factors along with time is significant for decision-makers. Business factors are represented either qualitatively or quantitatively. Our proposed simulation model involves such aspects with structural model representation and generates possible scenarios by using Monte Carlo simulation. However, possible scenarios as probability distribution of target factors inherently involve risk scenarios and non-risk scenarios from decision-makers point of views. Identifying risk factors still resorts to decision-makers without computational analysis support on derived outputs. Therefore we propose a finding method of business risk factors on target models.

This paper addresses a retrieval method for BBS(Bulletin Board System) articles with relevance index between the retrieval query and an article. Simply using the keyword-based retrieval has limitation on narrowing the articles, because most BBS articles include various keywords and such combination of some unrelated keywords to the retrieval query causes unexpected results. On the other hand, most BBSs have a characteristic structure, so-called "thread", which consists of one question article and a set of answer articles. Based on this structure, our method calculates the relevance index of each part of an article with association index among words derived from the Internet search engine results. We applied it to a practical word-of-mouth BBS and compared with the retrieval method of cosine similarity index in the word-vector space. The results show that our method had 30% better retrieval accuracy.

Recently, BBS (Bulletin Board System) has frequently used because it contains a lot of users' valuable opinions. However, most users have some frustration when searching target information because BBS has lots of articles and there is no way to retrieve them efficiently and correctly. There are typical retrieval methods, so called keyword matching and similarity-based search in word vector space, but neither of them provide desired retrieval results against incomplete or redundant sentences which are usually expressed in BBS articles. In order to solve this problem, we propose an efficient retrieval method that uses past other users' retrieval results.In our proposed method, users make two marks whether the retrieval result fits their desired one or not, and such marks are used as feedback for the similar retrieval of other users, which improves the correctness of retrieval results initially derived from similarity-based search in word vector space. This framework does work well along with users' evaluation.We made an experiment by our proposed method, and the number of correct answers of ranking TOP10 of retrieval results increases about 50%, and the number of threads that users need to read by finding three desired articles is reduced from 10.6 to 3.2 on average compared with simply use of similarity-based search in word vector space.

The use of the RFID tag in the entire supply chain including different organizations is reviewed. The problems of the RFID tag usage in supply chain involving various organizations are examined through field trial applications. Then, it concludes that the use of the secure RFID tag is effective to solve the problems, with the illustration of application examples.

The data on RFID tags placed on items must be protected to secure the privacy of that information throughout the item life cycle from production to purchase to recycling. Therefore, we propose low cost RFID tags with an access control function of using a password and a system that assigns and manages a different password for each group of RFID tags. We also explain how using the system could protect consumer privacy, and how the RFID tag could be used throughout the entire life cycle of an item.

In Japan, the electric power liberalization will expand. Customers request methods of judging the services. The method of comparing each energy unit price based on the average demanded is general. However, there are risks in customer's energy demand in the future. In the supply contract, there is freedom of contract changes according to demand changes. By this contract changes customers can avoid the risks. In this paper, we think that there was value in contract changes and we proposed the new method for analyzing the economy of this value.

To build a cooperative logistics network covering multiple enterprises, a planning method that can build a long-distance transportation network is required. Many strict constraints are imposed on this type of problem. To solve these strict-constraint problems, a selfish-constraint-satisfaction type genetic algorithm (GA) is proposed. Moreover, a constraint pre-checking method is also applied. Our experimental result shows that the proposed method can obtain an accurate solution in a practical response time.

A method of solving the vehicle routing problem (VRP) that can obtain a highly optimal solution to achieve steady logistics operation is required to optimize a cooperative logistics network. To satisfy this requirement, a multi-stage GA and a method of multipoint evaluation are proposed. The multi-stage GA enables us to obtain an accurate solution under various conditions. Moreover, the method of multi-point evaluation can generate a delivery schedule that enables stable logistics operation. The experimental results revealed the method we propose can obtain accurate solutions to achieve stable operation.

It is important to analyze existing products of competitors when a product planner develops new products. In this research, we proposed a method of extracting product trend keywords from press releases to support product strategy analysis. In order to extract keywords from press releases, we utilize expression phrases which are defined as description patterns. A description pattern enables extracting keywords describing features of a product that cannot be extracted by using simply word frequency. Additionally, we utilize derivation patterns, which are expression phrases derived from a description pattern, to extract keywords more flexibly. Also to grasp time-series movement in the feature of each product, product feature trend map is discussed.

This paper addresses an agent-based negotiation protocol that enables to make flexible trading on resource dealing among distributed organizations. When distributed organizations, categorized into either supply or demand sites, form a temporary negotiation group on the network, negotiation-based commitment is necessary to reach mutual agreement. However, the negotiation process usually involves complicated procedures such as proposal, selection, reply, and cancellation. Furthermore, in asynchronous negotiation, it is quite difficult when to finalize the negotiation procedure under self-interested environment. Also in some situations, self-interested agents may collaborate to increase their profit, that is, coalition structure emerges. We investigate such a negotiation process and design a negotiation protocol.

The target of this research is to address scheduling problems in order to ship multi-item orders in a rice milling process on the same day the order is placed. Milled rice can be stored as stock for some days and can be shipped from stock to satisfy orders. The setup time for switching milled items are needed and work hours in a day are given in the process. Scheduling is generated from the producers' order forecast, orders and stock before milling. When new orders during milling (urgent orders) come and exceed the work-in-process stock, the schedule must be modified in order to satisfy the orders. The purpose of this problem is to minimize the number of over time hours worked, to reduce switching time and to maximize throughput. In this problem, because urgent orders account for 90% of the whole, stocks often run out and the need to mill new items often occurs in multi-items. Therefore, this research proposes a modifiable scheduling method to minimize the number of switches and over time hours worked when milling starts in a day and when urgent orders are received. In scheduling, items to be milled and the amount to mill are first decided using an inventory control theory. The milling sequence is decided using "modifiability", which is defined by the reducibility of milling time and the necessity of modification of items to be milled for modifiable scheduling against urgent orders. In a case where the estimated end time runs over work hours, milling amounts which has smaller stock-out probability are reduced in order of increasing. The proposed method has been applied to a real problem. It has been confirmed that this method can generate the solutions 37% better than producer's solutions

An autonomous information system design for item management is proposed for an efficient supply chain management. By using rewritable RF-ID tags instead of popular bar codes as a job ID, which represents a manifest of an item, each processing system directly communicates with the tag information of each item, and autonomous item management can be realized. The following four basic functions are designed, to introduce new items, to link each item to processes, to update item information, and acquire process records. The prototype systems, which handles RF-ID tags as processed items, selecting correct items and processes according to a production plan, are confirmed.

A job shop problem with group constraints is a problem where job schedule in each line is decided, and jobs dealt with the same process have to be grouped for every process of a line. The minimum and maximum size restrictions exist in the total size of jobs in the group. An evaluation function of this problem consists of an average manufactured time needed for completion of all jobs, a number of group changes, and a number of line rests. This research consists of two parts: rapid generation of an initial solution by heuristics and improvement of solutions. In the part of generation of an initial solution, the flexibility of each job according to an influential degree of a whole plan is defined. In the part of improvement, rules, which can improve one of three evaluation items, are prepared. By application of a taboo search, which can apply plural rules, total evaluation can be improved. The proposed method has been applied to some real large-scale job shop problems with group constraints, and its effectiveness is confirmed.

A procurement negotiation is performed in order to find the solution which satisfies both negotiators' conditions of their own objectives between two companies. They don't know the opponent's conditions mutually and they sometimes appease tactically. A negotiation method has been proposed in which received opponent's plan is improved to satisfy the conditions of the negotiator's objective and sent back each other, but it does not have a tactical appeasement function. This paper proposes a function by which the conditions of objective are automatically and gradually relaxed with the negotiator's knowledge, and the function is incorporated into the existing negotiation method.

A rectangular cutting stock problem for flawed and connectable resources is a kind of a rectangular cutting stock problem where orders are assigned to resources. Each order has two types of size, width and a total length. The aim of the problem is that resources are divided into pieces, called "lumps", and the sum of length of the lumps whose width equals to a width of order satisfies a total length of the order, is decided. A resource is cut by "sets" which are combinations of fixed cutting blade for length direction, and cut end to end for width direction. Moreover, there are features that are flawed that cannot be assigned to orders existing on resources, and the connection that enables to cut several parts of resources by the same set is permitted. This research proposes the method that consists of two phases. In first phase, the rectangles called "available areas" which are generated by avoiding flaws and connecting, are created by deciding positions of set change according to an expected yield ratio and a number of set changes. In the second phase, assignment for each available area is done from an order with a larger width by using a branch and bound method in order to achieve a higher yield ratio. At this time, an effective degree of a set pattern is calculated for each available area. The effective degree means a rate of improvement of a yield ratio per length of an assigned target order. It is possible to decide an assignment rapidly by searching a set pattern that has a large effective degree earlier. The proposed method has been applied to a real cutting stock problem, and the effectiveness is confirmed.

We have developed a location-aware information service system using Bluetooth in an exhibition hall like a gallery and a museum. With this system, the visitors who have their mobile devices such as a cellular phone of PDA can automatically get the information about the exhibits nearby them, based on the estimated visitors' locations. In this paper, we propose several operation support features for smoothly maintaining contents information and the Bluetooth communication area setup. The proposed operation support was evaluated with time to input. With the support, the operator took 183 min for data input and it was one-third of input time without the support system.

In a process ordering problem, where optimal process of a large number of jobs are decided, the constraints about each connection between jobs by their difference of properties have to be satisfied, and all urgent jobs must be included in a solution. The paper proposes a method to rapidly decide process orders. This approach divides a problem into two parts of problem by grouping similar jobs. One is easy to decide orders in each group. The other is difficult to decide orders between groups. By merging each solution, a solution of the whole problem is created rapidly. When deciding points of relaxation, searching area is limited by using a threshold. This makes finding points of constraints relaxation quickly. This proposed method has been applied to real process ordering problems.

We should consider a process capacity when the re-order system replenishes multi-items under the combination of forecast and order-based production. We propose the new usage of optimal ordering method (OOM). OOM has an effect of decreasing the out-of-stock situation under the limited process capacity. OOM is used at the stock position of the coupling points (CP), and the limited capacity is changed by the order-based production quantity. As a result, the delivery delay of the order-based production does not occur, and the out-of-stock situation of the forecast-based production is not increasing. This new usage of OOM has been successfully applied to an electronics device manufacturing process.

Qualitative reasoning is one of the fault detection and diagnosis methods and is an effective way for complex systems. Especially, in the stochastic qualitative reasoning, the probabilistic process is used for state transition of the qualitative models with probabilities. In the paper, the stochastic qualitative reasoning is applied to a modelling method of the human qualitative thinking process based on the instrumentation diagram and the control information in building air conditioning systems. However, several subjects remain to be solved for practical uses. The paper proposes real-time simulation using stochastic qualitative reasoning as a way of practical uses for fault detection and diagnosis of a VAV (variable air volume) system of air conditioning systems.

In distributed control systems (DCS), designed distributed controllers, which interact with each other operate plants. Therefore, in the development of the system, verification of whether the controllers satisfy given specification is important. However, the existing description languages, for example, temporal interval, are too complicated to formulize the specification. In this paper, first, Sequence Oriented Language (SOL), which is intelligible language in order to describe specification for distributed sequential control systems, is proposed. Secondly, we developed a tool for it. In this method, the tool automatically verifies that behavior of models satisfies specifications which is described using SOL.

A bilateral exchange and distribution negotiation problem is a problem that two persons who have interdependent goals try to get a solution which satisfies any constraints by exchanging alternative resources in order to distribute the resources to get more profit. It is difficult to solve this problem because it is not desirable to inform the negotiator's constraints to the opponent. The paper proposes extension of the negotiation method using the improving knowledge for considering the negotiator's satisfying level for the agreement plan by introducing the hierarchized conditions expressing the negotiator's ideal about an agreement plan.

The control system for the semiconductor processing equipment (SPE) includes multiple functions. It should be linked to the factory production management computer with SECS interface etc, it should have human interface (HMI) for the operator to the machine information and at the same time it should be equipped with real-time machine control. In this paper the control system for the SPE is introduced, in which the IEC61131-3 standard is applied for the real-time control hierarchy. We have developed total control system by using application software effectively on the de facto standard operating system.

A personal computer's life-cycle management system by use of a radio frequency identification (RF-ID) tag technology has been experimentally developed. By using an RF-ID tag, a waste computer's information such as its position in the life-cycle and its type can be collected in the management system with a low cost. In the paper, firstly, a PC withdrawal system is modeled. Then, five logistic scenarios of waste PCs are established. They consist of a disposal dealer selection, the use of future waste PC number information, the adjustment of the stock between withdrawal dealers, and the transportation timing for disposal dealers. Some of them use the information from the life-cycle management system. Each scenario is evaluated by simulation. From these results, the proposal of the most cost-effective and lowest environmental load scenario is expected.

A bilateral exchange and distribution negotiation problem is a problem that two persons who have interdependent goals try to get a solution which satisfy any constraints by exchanging alternative resources in order to distribute the resources for more profits. It is difficult to solve this problem because it is not desirable to inform the negotiator's constraints to the opponent. This paper proposes the negotiation method that each negotiator improves the opponent's plan with knowledge, and sends it back to the opponent in order to find an agreement plan automatically.

The paper presents an information compressing 3-dimensional user interface on the support system of requirement extraction from a BBS (Bulletin Board System). To correspond to users' viewpoints, the Fish-Eye is applied. With the Fish-Eye, a size of an object on the output screen is decided according to the user's attention. This function is realized in topic groups, opinion cards and text in cards respectively. A way of applying Fish-Eye with regards to the topic groups and opinion cards is to show the objects on the inclined coordinates from viewpoints. To realize the Fish-Eye for letters, the priority is set on each sentence in the opinion. The priority is decided from the character of conversational structure, which has a tree structure. With this structure, the attention of the user is specified and is reflected in selecting which sentences should be written in the opinion cards.

It is difficult to analyze the performance of a supply chain philosophically because of the complexity of the supply chain. Therefore, in order to evaluate the control algorithms for a supply chain, simulation is an effective method. However, it is difficult to describe an entire supply chain because of the large variety and number of components in a supply chain. The paper proposes a description language in which an entire supply chain is described efficiently. It also describes the simulation engine which efficiently determines the execution priority of events, and the simulation tool based on the proposed language which has been developed.

To select an ERP package and to customize it accurately, it is necessary for a company to set its business process reengineering (BPR) aims. Generally, there are two types of difficulty in setting BPR aims: 1) the company is short of skilled work force; and 2) the company does not allow the project enough time. This paper presents a method that enables one to set BPR aims easily using business sheets. This method consists of the following steps: 1) recognize the transformation of the management environment (the External Business Sheet); 2) recognize the current mechanism of business operations inside the company (Internal Business Sheet); 3) confirm mismatches between the environmental transformation and current business operations; and 4) set necessary action aims (BPR aims) to resolve the mismatches. Amongst these necessary actions, the project will pick up some aims that can be solved by an ERP package. These aims will be the basis of the business requirements in selecting an ERP package. This method reduces the period of discussion by 70%.

Proposes an algorithm which detects structures of deadlock which have deadlock occurrence possibility. This deadlock detection algorithm finds out all deadlock structures in the business processes workflow model by using deadlock patterns which express deadlock structures by combinations of nodes. There is the problem that some deadlock structures cannot be identified with some deadlock pattern because the deadlock structures cross each other when the algorithm finds deadlock patterns in the business processes workflow model. The problem can be solved by detecting the deadlock structures not by using each deadlock pattern but by using each of the unit into which the deadlock pattern is integrated.

This study examines the effects of the utilization of information in new product design. In order to discover the effects of the information, experiments were done by researchers who are actually involved in the design process. The information provided for new product design can be divided into two categories. One is relative information, which can be used in a comparisons such as patents. The other is absolute information that must be fulfilled in the such as customers' requirements. It was found that the group with the relative information regarding patents, they extended their suggested ideas and created a superior new product with application, appearance and productivity. The group with absolute information pertaining to health repeatedly recognized the provided information in the design of the new product. It is found that relative and absolute information have influenced on the suggested idea and the final fixed product in the new product design.

The capacity designated Petri net (CPN), each of its places can have at most designated number of tokens, is a suitable model for describing real systems behavior. Liveness analysis of CPN is important to guarantee that a system described by using CPN is deadlock free. CPN liveness problem can be completely determined by reachability tree analysis, but reachability tree analysis needs a large amount of calculation time in propotion to net size power. In this paper, three reduction rules are proposed, which can be directly applied to the CPN model and preserves the liveness property of original net. The heuristic algorithms for realizing the reduction rules are also proposed and an example of reduction process using these algorithms is demonstrated. These algorithms have been experimentally applied to several CPN models which represent sequence control specifications, and their net sizes have become 1/2 - 1/10 compared with that of original nets.