In the evolving landscape of modern supply chains, achieving sustainability while ensuring resilience presents a significant challenge. This study introduces a novel fuzzy optimization technique designed to develop sustainable supply chain plans under uncertain environments. The proposed framework integrates the principles of Chance-Constrained Programming (CCP) with Intuitionistic Fuzzy Linear Programming (IFLP), enabling decision-makers to manage the risk of constraint violations while simultaneously addressing levels of satisfaction and non-satisfaction. By incorporating resilience metrics, the model evaluates its capacity to respond effectively to disruptions and uncertainties, ensuring robust operational performance characterized by flexibility, redundancy, and recovery capabilities. The methodology aims to optimize sustainable supply chain planning by minimizing total costs and maximizing social and environmental performance scores, while accounting for imprecise costs and customer demands. A case study demonstrates the practical application of the model, emphasizing its efficacy in addressing sustainability and resilience challenges in real-world scenarios. The findings highlight the potential of fuzzy optimization to enhance decision-making processes in sustainable supply chain management.

We propose a method for extracting important routes from large trajectory data. The method consists of two phases. In the first phase, we recognize event occurrence when a predefined condition is satisfied in each trajectory and extract a time series of events. Further analysis is applied to the obtained event sequences having size much smaller than that of the original trajectory data. The conditions for event extraction are determined by the purpose of analysis. In the second phase, event patterns that appears frequently in the event sequences are discovered. For this purpose, we propose a new method called trajectory alignment. This method is an adaptation of the sequence alignment, used in bioinformatics, to trajectory data. The proposed approach is applied to an artificial data set and two real data sets.

In this paper, we study modeling of air traffic flow from flight trajectory data in the airspace. Compared to road/train traffic, modeling of air traffic flow is difficult because trajectory of each aircraft fluctuates in the 3-dimensional space due to various factors such as weather and congestion level. By this reason, finding important routes on which aircrafts frequently pass is necessary for building air traffic flow models. We propose a framework for finding important routes in the form of graphs based on combination of various technologies such as space partition, trajectory clustering, and skeleton extraction.

Boolean networks (BNs)play a crucial role in modeling and analyzing biological systems. One of the central issues in the analysis of BNs is attractor detection, i.e., identification of all possible attractors. This problem becomes more challenging for large asynchronous random Boolean networks (ARBNs)because of the asynchronous and non-deterministic updating scheme. In this paper, we present and formally prove several relations between feedback vertex sets (FVSs)and dynamics of BNs. From these relations, we propose an FVS-based method for detecting attractors in ARBNs. Our approach relies on the principle of removing arcs in the state transition graph to get a candidate set and the reachability property to filter the candidate set. We formally prove the correctness of our method and show its efficiency by conducting experiments on real biological networks and randomly generated N- K networks. The obtained results are very promising since our method can handle large networks whose sizes are up to 101 without using any network reduction technique.

Deterministic asynchronous Boolean networks play a crucial role in modeling and analysis of gene regulatory networks. In this paper, we focus on a typical type of deterministic asynchronous Boolean networks called deterministic generalized asynchronous random Boolean networks (DGARBNs). We first formulate the extended state transition graph, which captures the whole dynamics of a DGARBN and paves potential ways to analyze this DGARBN. We then propose two SMT-based methods for attractor detection and optimal control of DGARBNs. These methods are implemented in a JAVA tool called DABoolNet. Two experiments are designed to highlight the scalability of the proposed methods. We also formally state and prove several relations between DGARBNs and other models including deterministic asynchronous models, block-sequential Boolean networks, generalized asynchronous random Boolean networks, and mixed-context random Boolean networks. Several case studies are presented to show the applications of our methods.

Attractor detection in Asynchronous Boolean Networks (ABNs) is very challenging due to the high complexity of the state transition graph of an ABN. Recently, an efficient method (called FVS-ARBN) has been proposed for exactly finding attractors of an ABN. FVS-ARBN uses a Feedback Vertex Set (FVS) to get a candidate set of states, then filters out this set by checking the reachability in ABNs. This method gives promising results; however, it still needs to be improved to handle larger networks. In this paper, we propose a new method (named iFVS-ABN) that includes two improvements to FVS-ARBN. First, we propose a reasonable combination of multiple existing techniques to efficiently check the reachability in ABNs. Second, we formally state and prove a relation between a Negative Feedback Vertex Set (NFVS) and the dynamics of an ABN. Based on this relation, we propose to use an NFVS instead of an FVS to get the candidate set of states. Experimental results show that the two improvements are effective and the improved method outperforms the original one.

Boolean networks (BNs) play a crucial role in modeling and analyzing biological systems especially gene regulatory networks. One of the central issues in the analysis of BNs is attractor detection, i.e., detecting all possible attractors of a BN. This problem becomes more challenging for large asynchronous random Boolean networks (ARBNs) because of the asynchronous and non-deterministic updating scheme. In this paper, we state and prove several relations between dynamics of ARBNs and generalized asynchronous random Boolean networks (GARBNs). Based on these relations, we propose an efficient method called ApproARBN for approximating attractors of ARBNs. The experimental results on real biological networks justify the accuracy of ApproARBN and show the efficiency of ApproARBN since ApproARBN outperforms two state-of-the-art methods and can handle large networks whose sizes are up to 101 nodes.

Recently, various kinds of ICT devices are introduced into service fields such as hospitals, nursing homes, hotels, restaurants, etc. One of the benefits of using ICT devices is that event logs consisting of time, location, and various data on workers' activities are automatically collected by the devices, and valuable information toward improving the quality of the work can be extracted from the logs. In previous papers by the author, recognition of situations in service fields is studied, where a situation means that what is going on and what kind of activities is performed in the field. We propose a method that automatically recognizes the current situation based on the logs. The method is applied to a small number of real logs taken in a nursing home. In this paper, we validate the method using artificial logs generated by discrete event simulation. Moreover, we find cases that the proposed method does not work well, and show some idea for the improvement.

The authors' group is developing an ICT-based system, called the smart voice messaging system, that assists working staff in cooperation, knowledge sharing, and recording observations. As an operation scheme for the system, we proposed the concept of information supervisory control (ISC) in our previous papers. In this scheme, there is a central commander, called the information supervisor, that provides a group of persons with appropriate information at an appropriate timing. In this paper, we show a formal model that simulates human decision making under ISC. The effectiveness of the model is validated by computer simulation.

In this brief, we consider the problem of finding a probabilistic Boolean network (PBN) based on a network structure and desired steady-state properties. In systems biology and synthetic biology, such problems are important as an inverse problem. Using a matrix-based representation of PBNs, a solution method for this problem is proposed. The problem of finding a BN has been studied so far. In the problem of finding a PBN, we must calculate not only the Boolean functions, but also the probabilities of selecting a Boolean function and the number of candidates of the Boolean functions. Hence, the problem of finding a PBN is more difficult than that of finding a BN. The effectiveness of the proposed method is presented by numerical examples.

In design of demand response programs, real-time pricing plays an important role. In real-time pricing systems, information of the price and power consumption is sent to an ISO (independent system operator) and consumers (i.e., smart meters) through communication networks. Hence, this system can be regarded as a class of networked control systems. In this paper, design of real-time pricing systems over communication networks is discussed. First, a probabilistic discrete model expressing the relation between the price and power consumption is explained. In addition, effects of communication networks are also explained, and directed graphs with communication properties are introduced. Next, the pricing problem is reduced to a mixed integer linear programming problem. Finally, a numerical simulation is presented.

Recent studies identify misconfiguration as the most frequent cause for failures in information systems including cloud computing infrastructure. Therefore, reducing operator error is key to improving the reliability and availability of cloud services. In order to reduce the risk of improper configurations, we propose the following two techniques; (1) automated synthesis of configuration change procedure to avoid improper changes and (2) identification of vulnerabilities (e.g. single point of failures) in system configuration to increase service resilience in the presence of undesirable events such as components failures and improper operations. We devised frameworks that realize these techniques using formal methods and evaluate their effectiveness through case studies. Based on the results of this evaluation, we discuss the benefits and limitations in applying formal methods for cloud system managements.

Attractors in gene regulatory networks represent cell types or states of cells. In system biology and synthetic biology, it is important to generate gene regulatory networks with desired attractors. In this paper, we focus on a singleton attractor, which is also called a fixed point. Using a Boolean network (BN) model, we consider the problem of finding Boolean functions such that the system has desired singleton attractors and has no undesired singleton attractors. To solve this problem, we propose a matrix-based representation of BNs. Using this representation, the problem of finding Boolean functions can be rewritten as an Integer Linear Programming (ILP) problem and a Satisfiability Modulo Theories (SMT) problem. Furthermore, the effectiveness of the proposed method is shown by a numerical example on a WNT5A network, which is related to melanoma. The proposed method provides us a basic method for design of gene regulatory networks.

In this paper, the optimal sampled-data control problem of linear systems with multiple time delays, which is one of the fundamental problems in a networked control system (NCS), is considered. An NCS is a control system in which plants, sensors, controllers, and actuators are connected through communication networks. As a typical situation, we suppose that multiple time delays and sampling period are uncertain. First, the optimal control problem is transformed into that of discrete-time uncertain linear systems. Next, under a certain assumption, the obtained problem is further transformed into a quadratic programming problem. In the proposed method, interval arithmetic is effectively used. Finally, a numerical simulation is shown.

A multi-hop control network (MHCN) is a control system in which plants and controllers are connected through a multi-hop wireless network modeled by a directed graph. In this paper, based on the MLD (Mixed Logical Dynamical) framework, which is one of the powerful methods in hybrid systems control, we propose a modeling method and an optimal control method of MHCNs. First, a directed graph in MHCNs is modeled by a pair of linear equation and inequality with binary variables, and the MLD model of MHCNs is derived. Next, by using the MLD model, the optimal control problem is transformed into a mixed integer quadratic programming problem. Finally, a numerical simulation on optimal control of a simple MHCN is shown.

Part-of-speech (POS) tagging is a fundamental task in natural language processing (NLP). It provides useful information for many other NLP tasks, including word sense disambiguation, text chunking, named entity recognition, syntactic parsing, semantic role labeling, and semantic parsing. In this paper, we present a new method for Vietnamese POS tagging using dual decomposition. We show how dual decomposition can be used to integrate a word-based model and a syllable-based model to yield a more powerful model for tagging Vietnamese sentences. We also describe experiments on the Viet Treebank corpus, a large annotated corpus for Vietnamese POS tagging. Experimental results show that our model using dual decomposition outperforms both word-based and syllable-based models.

A networked control system (NCS) is a control system in which plants, sensors, controllers, and actuators are connected through communication networks. In this paper, we consider NCSs modeled by stochastic switching systems, and propose a new method for modeling and optimal control. First, a modeling method of the expected value of the state is proposed. The obtained model is given as a mixed logical dynamical (MLD) model, which is one of the standard models in hybrid systems. Next, the optimal control problem is approximately reduced to a linear programming problem. Finally, the effectiveness of the proposed method is shown by a numerical example.

Self-triggered control is a control method that the control input and the sampling period are computed simultaneously in sampled-data control systems, and is studied in the field of networked control systems. In this paper, a new approach for self-triggered control is proposed based on model predictive control (MPC). First, self-triggered MPC with delay compensation in which the delay-compensation input is introduced is newly formulated. Next, in order to efficiently solve this MPC problem, the optimal control problem with horizon one is formulated, and an approximate solution method is derived. Finally, the effectiveness of the proposed approach is shown by a numerical example.

In 2010, JST/RISTEX in Japan started a new R&D program “Service Science, Solutions and Foundation Integrated Research Program”. The authors are engaged in a research project “Innovation for Service Space Communication by Voice Tweets in Nursing and Caring” selected by this program, and are developing a stress-free information assisting system based on smart voice messaging. By providing voice messaging environment optimized for current situation of nurses, the system helps nurses in their cooperation, knowledge sharing, and making work records, and as a result the system reduces various kinds of stresses associated with their work. To estimate the current situation of nurses, it is important to have detailed process models that describe working schedules and how they behave in various situations. Moreover, computer simulation based on the process models is useful for quantitative evaluation of the system. In this paper, we first analyze complex processes in nursing and caregiving services, and then propose a modeling architecture. An implementation based on object-oriented Petri nets is also presented.

The correctness of business process models is critical for IT system development. The properties of business processes need to be analyzed when they are designed. In particular, business processes usually have various constraints on time and resources, which may cause serious problems like bottlenecks and deadlocks. In this paper, we propose an approach based on the model checking technique for verifying business process models with temporal and resource constraints. First, we extend Business Process Modeling Notation (BPMN) to handle these constraints. Then, we provide a mapping of the business process models described with this extended BPMN onto timed automata that can be verified by the UPPAAL model checker. This approach helps to eliminate various problems with time and resources in the early phase of development, and enables the quality assurance of business process models.

Agent technology is widely recognized as a new paradigm for the design of concurrent software and systems. The aim of this paper is to give a mathematical foundation for the design and the analysis of multi-agent systems by means of a Petri-net-based model. The proposed model is based on place/transition nets, one of the simplest classes of Petri nets. The main difference is that each token, representing an agent, is also a place/transition net. It is sufficiently simple for the mathematical analysis, but has enough modeling power.