The paper starts from a discussion of the concepts of knowledge management versus technology management, and the emergence of knowledge sciences. This is followed be a summary of recent results in the theory of knowledge creation. Most of them concern diverse spirals of creative interplay between rational (explicit) and intuitive or emotional (tacit) aspects of knowledge. Some of them concentrate on organizational (market or purpose-oriented) knowledge creation, other describe academic (research-oriented) knowledge creation. The problem addressed in this paper is how to integrate diverse spirals of knowledge creation into a prescriptive or exemplar model that would help to overcome the differences between organizational (market-oriented) and normal academic knowledge creation. As such prescriptive approach, the JAIST Nanatsudaki Model of knowledge creation is proposed. It consists of seven spirals, known from other studies, but integrated in a sequence resulting from the experience of authors in practical management of research activities. Not all of these spirals have to be fully utilized, depending on a particular application, but all of them relate to some essential aspects of either academic or organizational knowledge creation. The paper presents Nanatsudaki Model in detail with comments on consecutive spirals. The results of a survey of opinions about creativity conditions at JAIST indicate the importance of many spirals constituting the Nanatsudaki Model. Directions of further testing the Nanatsudaki Model are indicated.

There are many practical decision problems where decision makers’ preferences may be inconsistent and contradictory. In this paper, new methods for ordering and classifying multi-attribute objects by discordant collective preferences are suggested. These methods are based on the theory of multiset metric spaces. The proposed techniques are applied to ranking companies and a competitive selection of projects, which are estimated by several experts upon multiple qualitative criteria.

It is only the observable part of the real world that can be stored in data. For such incomplete and ill-structured data, data crystallizing aims at presenting the hidden structure among events including unobservable events. This is realized by data crystallization, where dummy items, corresponding to potential existence of unobservable events, are inserted to the given data. These dummy items and their relations with observable events are visualized by applying KeyGraph to the data with dummy items, like the crystallization of snow where dusts are involved in the formation of crystallization of water molecules. For tuning the granularity level of structure to be visualized, the tool of data crystallization is integrated with human’s process of understanding significant scenarios in the real world. This basic method is expected to be applicable for various real world domains where previous methods of chance-discovery lead human to successful decision making. In this paper, we apply the data crystallization with human-interactive annealing (DCHA) to the design of products in a real company. The results show its effect to industrial decision making.

This paper studies a new form of transportation network design problem. In urban transportation network, unreasonable phenomenon can occur in certain traffic period (e.g. on/off duty period), which demonstrates that the flows of opposite directions on a two-way road are seriously asymmetric; one traffic link of a two-way road congest heavily but the other is hardly used. In order to reduce transportation congestion and make full use of the existing road resources, we propose a lane reallocating approach in peak period, and establish a discrete bi-level programming model for the decision-making. Then, based on particle swarm optimization (PSO) technique, a heuristic solution algorithm for the bi-level model is designed. Finally, the lane reallocating approach is demonstrated through a simple transportation network.

We consider a continuous review perishable (s, S) inventory system with a service facility consisting of a waiting hall of finite capacity and a single server. We assume two types of customers, ordinary and negative, arrive according to a Markovian Arrival Process (MAP). An ordinary customer joins the queue and a negative customer instead of joining the queue removes one ordinary customer from the queue. The removal rule adopted in this paper is RCE (removal of a customer from the end). The individual customer’s unit demand is satisfied after a random time of service which is assumed to have a phase-type distribution. The life time of each item and the lead time of the reorders have been assumed to be independent exponential distributions. The joint probability distribution of the number of customers in the system and the inventory level is obtained for the steady state case. Various stationary system performance measures are computed and the total expected cost rate is calculated. The results are illustrated numerically.

A multi-objective robust operation model is proposed in this paper for an electronic market enabled supply chain consisting of multi-supplier and multi-customer with uncertain demands. Suppliers in this supply chain provide many kinds of products to different customers directly or through electronic market. Uncertain demands are described as a scenario set with certain probability; the supply chain operation model is constructed by using the robust optimization method based on scenario analyses. The operation model we proposed is a multi-objective programming problem satisfying several conflict objectives, such as meeting the demands of all customers, minimizing the system cost, the availabilities of suppliers’ capacities not below a certain level, and robustness of decision to uncertain demands. The results of numerical examples proved that the solution of the model is most conservative; however, it can ensure the robustness of the operation of the supply chain effectively.

In multiobjective optimization, trade-off analysis plays an important role in determining most preferred solution. This paper presents an explicit interactive trade-off analysis based on the surrogate worth trade-off function to determine the best compromised solution. In the multiobjective framework thermal power dispatch problem is undertaken in which four objectives viz. cost, NO_x emission, SO_x emission and CO_x emission are minimized simultaneously. The interactive process is implemented using a weighting method by regulating the relative weights of objectives in systematic manner. Hence the weighting method facilitates to simulate the trade-off relation between the conflicting objectives in non-inferior domain. Exploiting fuzzy decision making theory to access the indifference band, interaction with the decision maker is obtained via surrogate worth trade-off (SWT) functions of the objectives. The surrogate worth trade-off functions are constructed in the functional space and then transformed into the decision space, so the surrogate worth trade-off functions of objectives relate the decision maker’s preferences to non-inferior solutions through optimal weight patterns. The optimal solution of thermal power dispatch problem is obtained by considering real and reactive power losses. Decoupled load flow analysis is performed to find the transmission losses. The validity of the proposed method is demonstrated on 11-bus, 17-lines IEEE system, comprising of three generators.

In this paper, under the constraint that the average distance and the average degree 〈k〉 remain approximately constant, we studied a random scale-free network model. We found that, if the network maintains the form of its degree distribution and the maximal degree k _c is N-dependent cutoff function k _c(N) < N, the degree distribution would be approximately power-law with an exponent between 2 and 3. The distribution exponent has little relationship with the average degree, denoted by 〈k〉. The diameter constraint can be interpreted as an environmental selection pressure, which could explain the scale-free nature of networks. The numerical results indicate that, under the diameter constraint, the preferential attachment can produce the cutoff function k _c(N) < N and power-law degree distribution.

In this paper, we investigate group decision making problems where the decision information given by decision makers takes the form of interval fuzzy preference relations. We first give an index to measure the similarity degree of two interval fuzzy preference relations, and utilize the similarity index to check the consistency degree of group opinion. Furthermore, we use the error-propagation principle to determine the priority vector of the aggregated matrix, and then develop an approach to group decision making based on interval fuzzy preference relations. Finally, we give an example to illustrate the developed approach.

In this paper, we study the M/M/1 queue with working vacations and vacation interruptions. The working vacation is introduced recently, during which the server can still provide service on the original ongoing work at a lower rate. Meanwhile, we introduce a new policy: the server can come back from the vacation to the normal working level once some indices of the system, such as the number of customers, achieve a certain value in the vacation period. The server may come back from the vacation without completing the vacation. Such policy is called vacation interruption. We connect the above mentioned two policies and assume that if there are customers in the system after a service completion during the vacation period, the server will come back to the normal working level. In terms of the quasi birth and death process and matrix-geometric solution method, we obtain the distributions and the stochastic decomposition structures for the number of customers and the waiting time and provide some indices of systems.