Choose better plans
Compare feasible options against cost, time, risk, service level, throughput, energy use or other objectives.
MATHEMATICAL MODELING & OPTIMIZATION
Model better decisions.
We turn constraints, objectives and tradeoffs into practical software for planning, allocation and operational decisions.
Decision modeling
Planning and scheduling
Resource allocation
Solver-backed tools
What this work is about
Mathematical modeling and optimization are practical tools for deciding what to do when choices are constrained. They help answer questions such as what to build, where to allocate resources, how to schedule work, how to route assets, how to price capacity or how to choose the best feasible plan.
This is not a niche academic exercise. It is used in logistics, energy, manufacturing, finance, healthcare, infrastructure, staffing, supply chains and engineering. The value comes from turning a messy real-world decision into a model that can be solved, tested and used.
What becomes possible
Allocate scarce resources
Assign people, equipment, budget, capacity, inventory, vehicles, computing or energy while respecting hard constraints.
Schedule complex operations
Plan tasks, shifts, production, maintenance, campaigns or deliveries around availability, dependencies and deadlines.
Optimize logistics and networks
Route flows, position facilities, balance supply and demand, reduce bottlenecks and test network changes.
Test scenarios before acting
Run what-if cases under different demand levels, constraints, policies, costs or risk assumptions.
Build decision software
Convert models into repeatable tools that planners, analysts, operators and engineers can use in real workflows.
What Nablance can do
We are useful when a decision is too constrained, expensive or high-impact to handle with intuition alone. We can formulate the problem, build the model, connect the solver, test the outputs and turn the result into software that fits the team using it.
Problem formulation
Translate operational questions into variables, constraints, objectives, data requirements and practical success criteria.
Optimization modeling
Build LP, MILP, nonlinear, stochastic or custom optimization models where the method fits the decision.
Solver implementation
Connect models to commercial or open source solvers, tune formulations and diagnose infeasible or slow runs.
Heuristics and decomposition
Design search, relaxation, decomposition or hybrid methods when exact optimization is too expensive or too rigid.
Scenario analysis
Run sensitivities, stress tests and what-if cases so teams can understand tradeoffs before acting.
Decision tool development
Build interfaces, pipelines and reports that make models repeatable for planners, analysts and operators.
A good fit
This work fits teams with recurring decisions that are hard to make manually: operations groups, engineering teams, logistics teams, supply chain planners, infrastructure programs, financial analysts and public-sector organizations.
It also fits situations where a decision needs to be explainable. A good model makes assumptions visible, shows which constraints matter and helps people understand the tradeoffs behind a recommendation.