Research — Prof. Taniya Mukherjee

Research Philosophy

Mathematics for Smarter, Sustainable Systems

Dr. Mukherjee's research is grounded in the conviction that rigorous mathematical modelling can transform how industries manage inventory, move goods and reduce their environmental footprint.

Her doctoral thesis — “Mathematical Modelling on Cross-docking services in Inventory management” — established a foundation for optimising the flow of goods within finite-capacity cross-docks, work that has since extended into carbon policies, reverse logistics and data-driven prediction.

Ph.D. Thesis

“Mathematical Modelling on Cross-docking Services in Inventory Management” — Banasthali Vidyapith, India (2024). Published across Q1 and Q3 SJR journals and Scopus-indexed journals.

Focus Areas

Core Research Directions

Six interconnected areas where mathematical rigour meets real-world application.

Operations Research & Optimisation

Constructive linear-programming methods for labour scheduling, resource allocation and cost-optimised decision-making under constraints.

Supply Chain & Cross-Docking

Modelling maximum goods flow through finite-capacity cross-docks using Ford–Fulkerson, Edmonds–Karp and Dinic's algorithms to minimise waiting time and bottlenecks.

Inventory Management

Estimating pallet distribution, machine utilisation and picking time to optimise space and labour within warehouses and cross-dock operations.

Reverse Logistics & Sustainability

Carbon-aware reverse cross-docking and green technology investment to reduce emissions in fast-fashion supply chains and road transport.

Statistical Analysis & Data Science

SPSS-driven correlation, regression and hypothesis testing — from COVID-19 prediction models to social-behaviour and public-service satisfaction studies.

Applied ML & Natural Language Processing

Transformer-based continual learning (BERT, RoBERTa, DistilBERT) for lifelong task-incremental learning in NLP.

Research Interests