The IRIS Lab, led by Dr. Saptadeep Biswas, is a cutting-edge research group at the intersection of artificial intelligence, optimization theory, and resilient supply chain systems. Our team pioneers quantum-inspired computational frameworks and multi-agent systems to address critical challenges in disaster management, sustainable logistics, and enterprise risk mitigation.
We combine rigorous mathematical modelling with advanced machine learning techniques to develop decision-support systems that are not only theoretically sound but also practically implementable. Our research philosophy emphasizes interdisciplinary collaboration, bridging operations research, evolutionary computation, blockchain technology, and environmental sustainability.
Core Focus Areas:
Quantum-inspired evolutionary algorithms for supply chain optimization
AI-driven disaster resilience and humanitarian logistics
Multi-objective optimization for sustainable operations
Autonomous multi-agent systems for real-time decision-making
Carbon emission reduction through intelligent process innovation
Digital twin frameworks for cold chain and perishable goods management
At IRIS Lab, we believe that the most pressing challenges facing modern supply chains - climate change, disruption vulnerability, and resource scarcity - require innovative computational approaches that transcend traditional optimization methods. By integrating quantum-inspired algorithms, reinforcement learning, and blockchain technologies, we're building the next generation of intelligent, self-adaptive supply chain systems.
Quantum-Inspired Evolutionary Framework for Profit Prediction in Multi-Channel Supply Chains
Developing a novel quantum-inspired differential evolution algorithm enhanced with gradient boosting techniques to predict profitability across omnichannel retail environments. This project addresses the complexity of modern supply chains where online, offline, and hybrid channels interact dynamically.
Multi-Objective Supply Chain Greenhouse Gas Emission Optimization
Creating a quantum-inspired differential evolution framework specifically designed for sustainable procurement decisions. The model simultaneously optimizes cost, delivery reliability, and carbon footprint across multi-tier supplier networks.
Optimal Carbon Taxes with Endogenous Process Innovation
Investigating the interplay between carbon pricing mechanisms and firm-level process innovation. This theoretical and computational study examines how dynamic carbon tax structures can incentivize technological advancement while maintaining economic viability.
Framework for Disaster Supply Chain Management
Designing autonomous multi-agent architectures that enable real-time coordination in disaster relief operations. Agents represent relief organizations, transportation networks, and affected communities, learning optimal resource allocation policies through decentralized decision-making.
Status: Agent architecture design | Simulation environment development
Multi-Agent Systems for Small and Medium-Sized Enterprises
Developing a theoretically grounded and mathematically validated framework for SME risk management using collaborative AI agents. The system identifies, quantifies, and mitigates operational, financial, and supply chain risks through intelligent monitoring and predictive analytics.
Multi-Objective Optimization with Reinforcement Learning
Integrating reinforcement learning mechanisms into differential evolution for solving complex fertilizer distribution problems. The hybrid algorithm learns optimal search strategies while balancing cost minimization, timely delivery, and environmental impact.
Multi-Agent Digital Twin Approach for Disruption Management
Constructing a blockchain-integrated digital twin framework for cold supply chains handling perishable goods. The system provides real-time visibility, predictive maintenance, and automated disruption response through smart contracts and multi-agent coordination.
IRIS Lab is actively seeking motivated research collaborators interested in:
Evolutionary computation and metaheuristic algorithms
Supply chain optimization and resilience
Machine learning for operations research
Sustainable logistics and green operations
Quantum-inspired computational methods
Opportunities available for research collaborative projects.