arXiv subjects

Dinesh Sharma

Publications and source records attributed to Dinesh Sharma.

3 recordsLinked to original sources

arXiv2024

ANFIS and metaheuristics for green supply chain with inspection and rework

The focus of present article is to investigate a supply chain inventory model of deteriorated items along with inspection and stock dependent demand using green technology to reduce carbon emissions. Products that are decaying have a high sensitivity to the environment in terms of temperature, carbon emission, humidity, waste disposal, etc. This study develops a profit maximization model in the presence of deterioration, preservation, imperfect production, inspection error, rework, stock and price-dependent demand. Three carbon emission strategies are proposed to reduce the expenses in different carbon emissions scenarios. The suggested approach may be used to determine the optimal production period, preservation investment, and level of green investment. The solution of the proposed non-linear constraint optimization is provided by using a penalty method in metaheuristic approaches. In order to conduct a sensitivity analysis for the essential model parameters, a numerical example is presented. The results produced by DE and PSO are compared with the results obtained by Adaptive Neuro-Fuzzy Inference System (ANFIS) technique.

math.OC↗

arXiv2023

A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis

Human immunodeficiency virus (HIV) is a major public health concern in the United States (U.S.), with about 1.2 million people living with it and about 35,000 newly infected each year. There are considerable geographical disparities in HIV burden and care access across the U.S. The 'Ending the HIV Epidemic (EHE)' initiative by the U.S. Department of Health and Human Services aims to reduce new infections by 90% by 2030, by improving coverage of diagnoses, treatment, and prevention interventions and prioritizing jurisdictions with high HIV prevalence. We develop intelligent decision-support systems to optimize resource allocation and intervention strategies. Existing decision analytic models either focus on individual cities or aggregate national data, failing to capture jurisdictional interactions critical for optimizing intervention strategies. To address this, we propose a multi-agent reinforcement learning (MARL) framework that enables jurisdiction-specific decision-making while accounting for cross-jurisdictional epidemiological interactions. Our framework functions as an intelligent resource optimization system, helping policymakers strategically allocate interventions based on dynamic, data-driven insights. Experimental results across jurisdictions in California and Florida demonstrate that MARL-driven policies outperform traditional single-agent reinforcement learning approaches by reducing new infections under fixed budget constraints. Our study highlights the importance of incorporating jurisdictional dependencies in decision-making frameworks for large-scale public initiatives. By integrating multi-agent intelligent systems, decision analytics, and reinforcement learning, this study advances expert systems for government resource planning and public health management, offering a scalable framework for broader applications in healthcare policy and epidemic management.

cs.AI↗

arXiv2017

Sense Amplifier Comparator with Offset Correction for Decision Feedback Equalization based Receivers

A decision feedback circuit with integrated offset compensation is presented in this paper. The circuit is built around the sense amplifier comparator. The feedback loop is closed around the first stage of the comparator resulting in minimum loop latency. The feedback loop is implemented using a switched capacitor network that picks from one of pre-computed voltages to be fed back. The comparator's offset that is to be compensated for, is added in the same path. Hence, an extra offset correction input is not required. The circuit is used as a receiver for a 10 mm low swing interconnect implemented in UMC 130 nm CMOS technology. The circuit is tested at a frequency of 1 GHz and it consumes 145 $μ$A from a 1.2V supply at this frequency.

cs.AR↗