arXiv subjects

Raghu Nandan Sengupta

Publications and source records attributed to Raghu Nandan Sengupta.

4 recordsLinked to original sources

arXiv2026

Queueing Analysis and Cost Optimization in a Diagnostic--Treatment Hospital Queue with Heterogeneous Referred Patients

This study develops an analytical and decision framework for a pooled hospital service comprising first-time patients who require diagnosis followed by treatment and referred patients who require only their prescribed treatment mode. The system is represented as a Markovian phase-type queue. probability generating functions are derived for two-treatment cases, while a matrix-analytic method is developed for an arbitrary number $n$ of treatment modes. The key performance measures are derived and interpreted. A load-triggered control is designed to determine the minimum capacity increment required to achieve a prescribed utilization level. Numerical experiments are conducted to validate the analytical results. Furthermore, the total-cost problem admits a strictly convex reformulation in reciprocal service-time variables and has a unique global minimum. Particle Swarm Optimization (PSO), Simulated Annealing (SA), and the Sine Cosine Algorithm (SCA) are additionally used as independent heuristic solvers.

math.OC↗

arXiv2026

LLM Inference Under Bursty Workload Distribution: Modifying the WAIT Algorithm

Large Language Models (LLMs) such as ChatGPT and Claude are widely used for information retrieval and problem-solving. Recent work has focused on improving scheduling algorithms to boost throughput while maintaining low latency. However, these approaches often assume Poisson request arrivals with constant rates - an assumption that fails to reflect the inherently bursty and dynamic nature of real-world traffic. We propose a lightweight extension to the state-of-the-art WAIT algorithm [1], which adapts to time-varying arrival rates without prior traffic knowledge. The proposed algorithm performs online estimation of request intensity based on observed interarrival times. Using Markov Modulated Poisson Process (MMPP)-based synthetic workloads with diverse request types, we conduct a simulation-based evaluation demonstrating that the proposed method achieves higher throughput than Sarathi-Serve [2], ORCA [3], and vLLM [4] in the evaluated low arrival-rate shift scenarios while maintaining comparable latency.

cs.LG↗

arXiv2026

Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization

Portfolio optimization under uncertainty is inherently a multi-objective decision problem involving complex interactions among return, risk, market dynamics, and practical investment constraints. Existing reliability based portfolio optimization approaches primarily rely on static optimization frameworks and often fail to capture sequential decision making, tail risk, and market frictions such as transaction costs. To address these limitations, we propose a deep reinforcement learning framework for multi-objective reliability based portfolio optimization (MORP-DRL). The proposed framework jointly optimizes expected return and downside risk using three complementary risk measures: variance, Conditional Value-at-Risk (CVaR), and Entropic Value-at-Risk (EVaR). To model uncertainty and heavy-tailed market behavior, asset returns are represented using GARCH(1,1), Extreme Value Theory, and a t-copula dependence structure, while realistic scenarios are generated through quasi-Monte Carlo simulation. A Proximal Policy Optimization (PPO) based strategy is developed under practical constraints including transaction costs and portfolio bounds, and is benchmarked against NSGA-II. Experiments on ten global equity indices across pre-COVID, COVID, and post-COVID market regimes demonstrate that MORP-DRL achieves competitive risk-return performance, reduced downside risk during periods of market stress, and scalability to high-dimensional portfolio settings.

cs.LG↗

arXiv2026

Likelihood-Based Regression for Weibull Accelerated Life Testing Model Under Censored Data

In this paper, we investigate accelerated life testing (ALT) models based on the Weibull distribution with stress-dependent shape and scale parameters. Temperature and voltage are treated as stress variables influencing the lifetime distribution. Data are assumed to be collected under Progressive Hybrid Censoring (PHC) and Adaptive Progressive Hybrid Censoring (APHC). A two-step estimation framework is developed. First, the Weibull parameters are estimated via maximum likelihood, and the consistency and asymptotic normality of the estimators are established under both censoring schemes. Second, the resulting parameter estimates are linked to the stress variables through a regression model to quantify the stress-lifetime relationship. Extensive simulations are conducted to examine finite-sample performance under a range of parameter settings, and a data illustration is also presented to showcase practical relevance. The proposed framework provides a flexible approach for modeling stress-dependent reliability behavior in ALT studies under complex censoring schemes.

stat.ME↗