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.

One shirt size cannot fit everybody, while we cannot make a unique shirt that fits perfectly for everyone because of resource limitations. This analogy is true for policy making as well. Policy makers cannot make a single policy to solve all problems for all regions because each region has its own unique issue. At the other extreme, policy makers also cannot make a policy for each small village due to resource limitations. Would it be better if we can find a set of largest regions such that the population of each region within this set has common issues and we can make a single policy for them? In this work, we propose a framework using regression analysis and Minimum Description Length (MDL) to find a set of largest areas that have common indicators, which can be used to predict household incomes efficiently. Given a set of household features, and a multi-resolution partition that represents administrative divisions, our framework reports a set C * of largest subdivisions that have a common predictive model for population-income prediction. We formalize the problem of finding C * and propose an algorithm that can find C * correctly. We use both simulation datasets as well as a real-world dataset of Thailand’s population household information to demonstrate our framework performance and application. The results show that our framework performance is better than the baseline methods. Moreover, we demonstrate that the results of our method can be used to find indicators of income prediction for many areas in Thailand. By adjusting these indicator values via policies, we expect people in these areas to gain more incomes. Hence, the policy makers will be able to make policies by using these indicators in our results as a guideline to solve low-income issues. Our framework can be used to support policy makers in making policies regarding any other dependent variable beyond income in order to combat poverty and other issues. We provide the R package, MRReg, which is the implementation of our framework in the R language. The MRReg package comes with a documentation for anyone who is interested in analyzing linear regression on multi-resolution population data.

This paper explores the feasibility in using Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) night light data as a proxy for approximation of socioeconomic indicators like poverty rate and income inequality. The feasibility is assessed by testing if features of night light data correlate to: 1) the rate of poverty and 2) income inequality. The poverty rate is derived from Thai People Map and Analytics Platform (TPMAP), a project initiated by the Thai government with the goal of developing a data analytics platform for precision poverty alleviation by enabling policymakers to identify the poor, locate them, and understand their basic needs. The poverty rate from TPMAP is computed using Multidimensional Poverty Index and income inequality statistics are calculated using data from three disparate sources. These two measures are used as the target of this study. We show night light data features that have high correlations with monetary based income features and moderate correlations with poverty rate at the province level of Thailand.

This paper conducts a performance analysis of two popular private blockchain platforms, Hyperledger Fabric and Ethereum (private deployment), to assess the performance and limitations of these state-of-the-art platforms. Blockchain, a decentralized transaction and data management technology, is said to be the technology that will have similar impacts as the Internet had on people's lives. Many industries have become interested in adopting blockchain in their IT systems, but scalability is an often- cited concern of current blockchain technology. Therefore, the goals of this preliminary performance analysis are twofold. First, a methodology for evaluating a blockchain platform is developed. Second, the analysis results are presented to inform practitioners in making decisions regarding adoption of blockchain technology in their IT systems. The experimental results, based on varying number of transactions, show that Hyperledger Fabric consistently outperforms Ethereum across all evaluation metrics which are execution time, latency and throughput. Additionally, both platforms are still not competitive with current database systems in term of performances in high workload scenarios.

This paper proposes an online traffic inference algorithm for road segments in which local traffic information cannot be directly observed. Using macro-micro traffic variables as inputs, the algorithm consists of three main operations. First, it uses interarrival time (time headway) statistics from upstream and downstream locations to spatially infer traffic transitions at an unsupervised piece of segment. Second, it estimates lane-level flow and occupancy at the same unsupervised target site. Third, it estimates individual lane-level shockwave propagation times on the segment. Using real-world closed-circuit television data, it is shown that the proposed algorithm outperforms previously proposed methods in the literature.

This paper proposes a novel anomaly classification algorithm that can be deployed in a distributed manner and utilizes microscopic traffic variables shared by neighboring vehicles to detect and classify traffic anomalies under different traffic conditions. The algorithm, which incorporates multiresolution concepts, is based on the likelihood estimation of a neural network output and a bisection-based decision threshold. We show that, when applied to real-world traffic scenarios, the proposed algorithm can detect all the traffic anomalies of the reference test data set; this result represents a significant improvement over our previously proposed algorithm. We also show that the proposed algorithm can effectively detect and classify traffic anomalies even when the following two cases occur: 1) the microscopic traffic variables are available from only a fraction of the vehicle population, and 2) some microscopic traffic variables are lost due to degradation in vehicle-to-vehicle (V2V) or vehicle-to-infrastructure communications (V2I).

This paper proposes a novel anomaly detection and classification algorithm that combines the spatiotemporal changes in the variability of microscopic traffic variables, namely, relative speed, intervehicle time gap, and lane changing. When applied to real-world scenarios, the proposed algorithm can use the variances of statistics of microscopic traffic variables to detect and classify traffic anomalies. Based on a simulation environment, it is shown that, with minimum prior knowledge and partial availability of microscopic traffic information from as few as 20% of the vehicle population, the proposed algorithm can still achieve 100% detection rates and very low false alarm rates, which outperforms previous algorithms monitoring loop detectors that are ideally placed at locations where anomalies originate.

This study investigates mobility patterns in microcellular wireless networks, based on measurements from the 802.11 based system that blankets the Carnegie Mellon University campus. We characterize the distribution of dwell time, which is the length of time that a mobile device remains in a cell until the next handoff, and sign-on interarrival time, which is the length of time between successive sign-ons from the same mobile device. Many researchers have assumed that these distributions are exponential, but our results based on empirical analysis show that dwell time and sign-on interarrival time can be accurately described using heavy-tailed arithmetic distributions that have infinite mean and variance. We also show that the number of handoffs per sign-on can be modeled accurately with a heavy-tailed distribution.

While many studies of admission control schemes assume exponentially distributed cell residual time, field measurements have shown that cell residual time, in reality, follows a heavy-tailed distribution; in particular, a Pareto distribution. These measurements were made in Wireless Andrew, an enterprise-wide broadband microcellular wireless network at Carnegie Mellon University. In this paper, we investigate the impact of Pareto distributed cell residual time on the admission threshold of the distributed admission control scheme (DACS), which is one of the well-known admission control schemes. Our results show that under Pareto-distributed cell residual time, a system that implements DACS can accommodate or "sign on" more simultaneous connections than the exponentially distributed case.

This study investigates mobility patterns in microcellular wireless networks, based on measurements from the 802.11-based system that blankets the Carnegie Mellon University campus. We characterize the distribution of dwell time, which is the length of time that a mobile device remains in a cell until the next handoff, and sign-on interarrival time, which is the length of time between successive sign-ons from the same mobile device. Many researchers have assumed that these distributions are exponential, but our results based on empirical analysis show that dwell time and sign-on interarrival time can be accurately described using heavy-tailed arithmetic distributions that have infinite mean and variance. We also show that the number of handoffs per sign-on can be modeled accurately with a heavy-tailed distribution.