Changsoo Ok

Distributed routing in wireless sensor networks using energy welfare metric

There are several requirements for a routing algorithm in wireless sensor networks. First, it should achieve both energy-efficiency and energy-balancing together, in order to prolong the lifetime of sensor networks. Second, the algorithm should follow a distributed control scheme so that it is applicable to large-scale networks. Third, it needs to be robust to diverse potential event generation patterns. The routing algorithm, MaxEW, designed in this study satisfies such requirements. It adopts the social welfare function from social sciences to compute energy welfare as a goodness measure for energy populations. When each sensor tries to maximize energy welfare of its local society, it collectively leads to globally efficient energy-balancing. This emergent property consequently supports preparedness and hence robustness to diverse event generation patterns. We demonstrate the effectiveness of the proposed routing algorithm through extensive simulation-based experiments, by comparing with other existing algorithms as well as optimal routing solutions.

Optimal Transmission Power in Self-sustainable Sensor Networks for Pipeline Monitoring

In this paper, we present a self-sustainable sensor network model for integrity monitoring of pipeline infrastructures. Sensor nodes consist of energy harvesting modules which help them to be always alive and hence monitor the pipeline continuously. These nodes report in a multi-hop fashion to more expensive sink nodes that can broadcast to the base station. The main objective of the paper is to compute the minimal number of sinks required to keep the network connected and satisfy the required constraints. Firstly, we present an algorithm (CON_NET) for determining if a network is connected. We propose a modified bisection algorithm to compute the maximum sampling rate for a given number of sinks nodes. Further, we propose an algorithm for computing the minimal number of sink nodes required. We illustrate the use of algorithms by providing design guidelines for a sensor network on a linear pipeline structure.

Distributed feedback control algorithm for dynamic truck loading scheduling problem

Abstract Distributed arrival time control (DATC) is a highly distributed feedback control algorithm for scheduling problems in heterarchical operation systems [V.V. Prabhu, Performance of real-time distributed arrival time control in heterarchical manufacturing systems, IIE Transactions 32 (2000) 323–331]. Although DATC has presented outstanding performance for the real-time scheduling problems in dynamic operational environment, little evidence exists for its effectiveness where earliness and tardiness of jobs are penalized at different rates. This study proposes an adapted DATC algorithm, called weighted multi-scale DATC (wms-DATC), to handle a dynamic truck loading scheduling problem where each truck has different earliness and tardiness penalties during a loading operation in a shipment yard. To validate the performance of wms-DATC in the dynamic truck loading scheduling problem, computational experiments are conducted. The computational results reveal that wms-DATC provides promising results and outperforms existing dispatching rules.

A stochastic simulation-based holistic evaluation approach with DEA for vendor selection

Abstract This paper aims to propose a new vendor evaluation framework by incorporating stochastic discrete event simulation and data envelopment analysis (DEA) approaches. The proposed approach enables the assessment of decision-making units (DMUs) in a holistic manner by adopting a simulation scheme and defining DMUs not as individual vendors, but as entire supply chains. Extensive experimental results show that the efficiency of a supply chain is not critically proportional to the efficiencies of individual suppliers. Moreover, procurement performance depends on the harmonic performance of the entire supply chain that includes vendors, procurement structure, ordering and safety stock policy of buyer, and demand variability rather than each suppliers performance.

Group preference modeling for intelligent shared environments: Social welfare beyond the sum

Abstract Ubiquitous computing technology can be effectively utilized in shared environments where groups of people are in close proximity. Shared environments are pervasive in the real world and hence the way of managing such environments will impact on not only quality of life but also business competitiveness. However, making decisions in an intelligent shared environment is never straightforward. The intelligence needs to be capable of choosing its parameters to satisfy all of its inhabitants, who have different preferences and are heterogeneous in their influences on decision. Till today, there has been no thorough research to scientifically investigate this type of decision making problems, though many systems have been already deployed. This research proposes a methodology for making decisions in such circumstances. The current and future works addressed in this paper are also conductive to any human-centric networks such as service systems, since the issues addressed here are also essential constitutes of such human-centric networks.

Network structure-aware ant-based routing in large-scale wireless sensor networks

Routing algorithms for large-scale sensor networks should be capable of finding energy efficient paths to prolong the lifetime of the networks in a decentralized manner. With this respect, Ant System has several proper characteristics for routing algorithm in large-scale wireless sensor networks. First, its distributed mechanism enables routing algorithm to find a solution with only local information and be robust for uncertainties in wireless sensor networks. Second, the framework of the Ant System is proper to solve dynamic problems such as routing problem. Transition probability in Ant System can be used to estimate how good a given routing path is. Capturing these features, this work proposes two Ant Systems based routing algorithms, which are AS-RWSNs (Ant System for Routing in Wireless Sensor Networks) and SAAS-RWSN (Structure-Aware AS-RWSN). The AS-RWSN applies the original Ant System to routing algorithm for wireless sensor network and SAAS-RSN upgrades AS-RWSN with considering properties of network structure such as degree of node. In SAAS-RSN, sensors with high node degree have high data traffic since they have more routing paths. Consequently, SAAS-RSN achieves an energy balance over sensor network through this routing scheme. We demonstrate the effectiveness of the proposed algorithms by comparing three existing routing algorithms.

Evaluation of forecasting methods in aggregate production planning: A Cumulative Absolute Forecast Error (CAFE)

Abstract The purpose of forecasting error measures is to estimate forecasting methods and choose the best one. Most typical forecasting error measures are designed based on the gap between forecasts and actual demands and, consequently, a forecasting method yielding forecasts in accordance with real demands is considered as good. However, in some applications such as aggregate production planning, these measures are not suitable because they are not capable for considering any effects caused by forecasting error such as increasing cost or decreasing profit. To tackle this issue, we propose a new measure, CAFE (Cumulative Absolute Forecast Error), to evaluate forecasting methods in terms of total cost. Basically, the CAFE is designed to consider not only forecasting errors but also costs occured by errors in aggregate production planning which is set up based on forecasts. The CAFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We have demonstrated the effectiveness of the proposed measure by conducting intensive experiments with demand data sets from M3-competition.

A Mathematical Definition and Basic Structures for Supply Chain Reliability: A Procurement Capability Perspective

Abstract Supply chain reliability has been receiving increasing attention in recent years, as it might provide a theoretical background for quantifying supply chain risks and uncertainties. However, most previous researches on supply chain reliability only focus on some reliability issue for limited supply chain structure without any general definition of supply chain reliability. This limitation makes it difficult to apply the theoretically well-established reliability engineering methodologies to various assessment and optimization problems related to supply chain reliability and risk. To tackle the issue, this paper provides a mathematical definition on supply chain reliability and relevant functions based on the traditional reliability theory, and subsequently, the basic structural reliability models for various types of supply chains. This paper also verifies that the proposed functions and structural reliability models are applicable to various types of supply chain with a case study of a computer assembly company.

Ranking DMUs in the DEA Context Using Super and Cross Efficiency

This work proposes a revised cross evaluation matrix which can be utilized to obtain a full rank of DMUs. The revised matrix contains super-efficiency values for diagonal elements and cross-efficiency values for non-diagonal elements of the matrix. This matrix enables better the difference of efficiency or performance of DMUs than the original cross evaluation matrix.

Multiagent-Based Dynamic Deployment Planning in RTLS-Enabled Automotive Shipment Yard

Real-time vehicle location information enables to facilitate more efficient decision-making in dynamic automotive shipment yard environment. This paper proposes a multiagent-based decentralized decision-making model for the vehicle deployment planning in a shipment yard. A multiagent architecture is designed to facilitate decentralized algorithms and coordinate different agents dynamically. The results of computational experiments show that the proposed deployment model outperforms a current deployment practice with respect to the deployment performance measures.