Operations Research & Mathematical Optimization Services
Turn complex business problems into intelligent, data-driven decisions. Yulogo builds custom optimization models and decision-support systems that help businesses allocate resources, reduce inefficiencies, improve scheduling, and maximize operational performance.
Based in Tirupur, India · Working with clients worldwide
The decision space
Conceptual modelObjective
Minimize cost. Improve delivery.
Subject to
Capacity, resources & rules.
Plan within real limits
Account for available capacity, materials, staffing and business rules.
Make trade-offs visible
Compare cost, delivery and utilization before committing to a plan.
Connect decisions to operations
Deliver decision support through dashboards, production scheduling software and APIs.
Better decisions start with a better model.
Operations research consulting and mathematical optimization services built around the decisions your business needs to make.
Production planning and scheduling optimization
Sequence orders around machine capacity, processing times and delivery deadlines.
Supply chain and inventory optimization
Plan replenishment, stock levels and distribution against demand and lead times.
Transportation and vehicle routing optimization
Allocate vehicles and plan routes within load, distance and delivery constraints.
Workforce and shift scheduling optimization
Match coverage requirements with skills, availability and working-hour rules.
Linear programming and mixed-integer programming
Model continuous quantities and discrete choices in one decision system.
Nonlinear and constraint optimization
Represent nonlinear relationships, logical rules and complex operating limits.
Assignment and resource allocation problems
Match tasks, people, machines and budgets to the work they can perform.
Cutting-stock and material utilization optimization
Plan cutting patterns and material allocation to reduce avoidable waste.
Facility location and network optimization
Compare sites and distribution networks under capacity and service requirements.
Cost minimization and profit maximization
Balance operating costs, output and margins within practical business constraints.
Multi-objective optimization and scenario analysis
Explore trade-offs between cost, service, throughput and resilience.
AI-powered forecasting and prescriptive analytics
Translate demand and processing-time forecasts into feasible recommended actions.
Built around the way your operation runs.
Examples of planning problems we can model. Scope and feasibility are established from your data and operating rules.
Textile and Manufacturing
Optimize knitting schedules, dyeing batches, machine capacities, production sequences, yarn allocation, material consumption, and delivery planning.
Which production sequence can meet delivery targets with the yarn and machine hours available?
Explore textile engineeringLogistics and Transportation
Optimize vehicle allocation, route planning, shipment consolidation, and fleet utilization.
How should shipments be assigned to vehicles with capacity and delivery-window limits?
Explore system integrationRetail and Warehousing
Optimize inventory replenishment, stock allocation, warehouse operations, and distribution planning.
Where should stock go when demand, storage space and supplier lead times compete?
Explore custom ERPEnterprise Operations
Optimize workforce schedules, budgets, project resources, and operational constraints.
How can a team cover the required work within skills, availability and budget limits?
Explore software engineering
Artificial Intelligence Predicts.
Operations Research Optimizes.
AI forecasts demand, processing times and future business conditions. Mathematical optimization uses those forecasts to recommend feasible actions within your business constraints.
Business Data
Orders, stock, resources
AI Forecasting
Demand and processing times
Mathematical Optimization
Objectives and constraints
Recommended Decision
A feasible plan to review
ERP/API Integration
Approved plans into operations
Performance Monitoring
Measure, learn and replan
Prediction informs the plan. A forecast estimates what may happen. It does not determine which order should run on which machine or how stock should be allocated.
Constraints make the plan usable. Optimization balances objectives with capacity, budget, staffing and delivery rules. Teams review decisions, monitor performance and replan as conditions change.
Same orders. Different decisions.
Explore how order sequencing and machine allocation change delivery performance under limited capacity.
Production scheduling lab
Change machine capacity, processing hours or delivery targets. Compare arrival-order planning with a schedule that minimizes late orders, then total lateness, then completion time.
Model constraints and assumptions
Each order runs once, without splitting, on an eligible machine. Machines run one order at a time. Optimized schedules stay within available capacity. All orders are ready at hour 0; setup times and material constraints are omitted. Delivery deadlines are targets, so late orders are reported. Red shading marks hours beyond capacity.
Best feasible schedule for this sample model. 24 complete candidate schedules evaluated.
Before · arrival-order planning
1Late orders
3 hTotal lateness
2 hOvertime needed
- A · Machine 1 · hours 0–6 · due 12 · on time
- B · Machine 2 · hours 0–3 · due 4 · on time
- C · Machine 1 · hours 6–10 · due 7 · 3 h late
- D · Machine 2 · hours 3–5 · due 9 · on time
After · optimized schedule
0Late orders
0 hTotal lateness
0 hOvertime needed
- A · Machine 2 · hours 0–6 · due 12 · on time
- B · Machine 1 · hours 0–3 · due 4 · on time
- C · Machine 1 · hours 3–7 · due 7 · on time
- D · Machine 2 · hours 6–8 · due 9 · on time
Sample results are illustrative, not actual customer savings. This four-order example explores a small scheduling model in your browser; production models require your data, operating rules and an appropriate solver.
From mathematical models to operational software.
We combine modeling, solvers, data engineering and optimization API development to put decision support into everyday use.
Python
ModelingMathematical modeling and optimization
Julia and JuMP
ModelingHigh-performance optimization modeling
Gurobi
SolvingMathematical optimization solver
Pyomo
ModelingPython-based optimization modeling
HiGHS and CBC
SolvingOpen-source optimization solvers
Ipopt
SolvingNonlinear optimization
FastAPI
DeploymentOptimization API deployment
PostgreSQL
DataOperational data integration
AI/ML
IntelligenceForecasting and intelligent decision support
Solver choice depends on licensing, mathematical problem structure, computational requirements, and customer budget. We evaluate commercial and open-source options against your model and deployment needs.
Define. Model. Validate. Integrate.
- 01
Define the decision
Agree on objectives, hard limits, trade-offs, data access and the current planning baseline.
- 02
Build the model
Represent decisions and constraints, select a solver and explore representative scenarios.
- 03
Validate with planners
Check feasible outputs, edge cases, data quality and solve times before operational use.
- 04
Integrate and monitor
Connect approved plans to ERP or APIs, track performance and adapt the model as rules change.
A practical approach to complex decisions.
What does operations research consulting involve?
We define the business decision, measurable objectives, available data and constraints. We then build and validate a mathematical model, compare it with your current planning method and agree how recommendations will be reviewed and used.
Can optimization work with our existing ERP?
Yes. Optimization API development can connect your ERP, databases and operational tools to a decision-support service. Planners review recommendations before approved schedules or allocations are written back to your systems.
Do we need a commercial solver?
Solver choice depends on licensing, mathematical problem structure, computational requirements, and customer budget. We evaluate commercial and open-source options against your model and deployment needs.
Does optimization guarantee savings or an optimal result?
Outcomes depend on the data, model, operating constraints and solve time. We report feasibility, solver status and any available optimality gap, and compare results against an agreed baseline. The demonstration on this page uses synthetic data and does not represent customer savings.
Do you work with clients outside India?
Yulogo is headquartered in Tirupur, India, and works with clients in India and worldwide. Discovery, model reviews, integration and support can be delivered remotely.
Have a Complex Planning or Optimization Problem?
Tell us your industry, business problem, decision constraints and approximate scale. We will discuss the data and modeling approach with you.
- India and worldwide
- NDA available for sensitive operational details
- Start with your business problem