3. Data, Science & AI

Solves complex optimization problems using metaheuristics like genetic algorithms and simulated annealing efficiently.

Optimizes safety stock and implements (s,S)/(Q,r) inventory policies under demand uncertainty, analyzing service levels and fill rates using stochastic models.

Solves minimum cost flow, maximum flow, and transportation problems in network flow optimization.

Solves large-scale optimization problems using column generation, Dantzig-Wolfe decomposition, and related methods for exponentially many variables.

Optimizes battery storage operations, deployment planning, and grid-scale energy management through data-driven scheduling and dispatch strategies.

Enables deep learning-based time series forecasting using LSTMs, Transformers, and attention mechanisms for complex pattern recognition.

Solves knapsack optimization problems including 0/1, bounded, and unbounded types to maximize value within capacity constraints.

Solves Flow Shop Scheduling Problems to minimize makespan in assembly line production optimization.

Solves the Traveling Salesman Problem by finding the shortest route visiting all cities exactly once and returning to the origin.

Solves complex scheduling, allocation, and combinatorial problems using constraint programming (CP-SAT) and constraint satisfaction techniques.

Provides AI-powered prescriptive analytics for actionable business decisions, recommendation engines, and intelligent optimization.

Applies machine learning and AI to supply chain problems, including predictive modeling, anomaly detection, and classification for logistics and inventory optimization.

Builds and optimizes statistical and predictive models for demand forecasting, improving accuracy in sales and capacity planning.

Optimizes delivery routes with vehicle capacity constraints including weight and volume limits for efficient logistics planning.

Builds and solves mathematical optimization models including linear, integer, and mixed-integer programming for decision optimization.

Optimizes edge-to-edge cutting patterns under guillotine constraints for stock material, supporting two-stage, three-stage, and n-stage cutting problems.

Optimizes 2D sheet cutting to minimize material waste in manufacturing processes.

Specialist in parallel AI engine evaluation using aiw command for quality comparison and high-reliability implementation selection.

Optimizes irregular shape arrangement on sheets to minimize material waste in manufacturing processes like garment cutting and CNC machining.

Optimizes worker-task matching and resource allocation using the Hungarian algorithm for assignment problems.

Solves facility location optimization problems including p-median, uncapacitated, and capacitated facility location using mathematical programming.

Optimizes production sequencing for Job Shop Scheduling Problems (JSP) to minimize makespan with precedence constraints.

Optimizes 2D rectangular item packing into bins to minimize material waste using efficient cutting patterns for manufacturing and design applications.

Provides Pareto-optimal solution finding for multi-objective trade-offs using NSGA-II and scalarization techniques in data science and AI workflows.