Optimization / Operations Research
Operations Research
Operations research turns a real business question — how should we route deliveries, staff shifts, stock inventory, or plan production — into a mathematical model a solver can actually optimize. The hard part usually isn't the math itself; it's translating messy operational reality into a model that's both accurate and solvable. We do both: build the model correctly and get it running in a way that produces decisions you can actually act on.
This Is For You If...
You're an operations or supply chain professional trying to reduce cost or improve service without a clear model to start from
You've got a spreadsheet-based process that's hit its limits and needs real optimization behind it
You need to justify a resourcing, inventory, or logistics decision with a rigorous model, not just intuition
You have a working model but it's not scaling, or the results don't match operational reality
Which Area Matches Your Problem?
Ask Yourself | Points Toward |
Are you deciding how goods move and are stored across suppliers, warehouses, and customers? | Supply-Chain Optimization |
Are you deciding how to staff shifts, assign employees, or schedule labor under constraints? | Workforce Optimization |
Are you deciding how much stock to hold, when to reorder, or where to place inventory? | Inventory Optimization |
Are you deciding what to produce, when, and in what sequence across limited capacity? | Production Planning |
Are you deciding how to route, consolidate, or schedule the physical movement of goods? | Logistics Optimization |
Many real operational problems span more than one of these — inventory decisions affect production planning, which affects logistics. Tell us the actual business problem and we'll help scope which parts need modeling.
Operations Research Services We Offer
Supply-Chain Optimization
Supply-chain optimization covers decisions across the full network — supplier selection, warehouse and distribution center placement, network flow, and procurement strategy — aimed at reducing cost and improving reliability end to end. We help model your actual supply chain structure and constraints, and apply techniques like network flow optimization and facility location modeling to find where real improvement is possible.
Workforce Optimization
Workforce optimization, including workforce scheduling and staff allocation, means assigning the right people to the right shifts or tasks under real constraints — availability, skill requirements, labor rules, and cost. We help formulate the scheduling model correctly, including the operational rules that make a "correct" schedule actually usable, not just mathematically valid.
Inventory Optimization
Inventory optimization addresses how much stock to hold, when to reorder, and where to place it across your network, balancing holding costs against the risk of stockouts. We help build demand-informed inventory models — including reorder point, safety stock, and multi-location placement decisions — tailored to your actual demand variability and service-level goals.
Production Planning
Production planning covers what to produce, in what quantity, and in what sequence, across limited machine, labor, or material capacity, often aimed at minimizing cost or meeting deadlines. We help model your production constraints and build scheduling and capacity-planning solutions that reflect your actual manufacturing or operational process.
Logistics Optimization
Logistics optimization focuses on the physical movement of goods — routing, transportation mode selection, load consolidation, and delivery scheduling — typically to minimize cost or transit time while meeting service commitments. We help formulate routing and logistics models (including vehicle routing and network optimization) suited to your actual fleet, geography, and delivery constraints.
Not sure which of these fits, or working across several at once? That's common in real operations — describe the actual decision you're trying to make and we'll help scope the right model.
What We Need From You
What to Share | Why It Helps |
A description of the actual operational decision you're trying to optimize | The real business context, not just the abstract problem type, determines the right model |
The relevant data (demand, costs, capacities, constraints) if available | Real data produces a usable model; without it, we can still help formulate the model structure |
The constraints that matter in practice (labor rules, delivery windows, capacity limits) | Missing operational constraints are the most common reason a "correct" model produces an unusable answer |
Any existing model, spreadsheet process, or prior analysis | Lets us build on and improve what exists rather than starting from scratch |
Whether this is for research or a real business decision | Changes the expected rigor, documentation, and practical usability of the result |
Your timeline | Helps us scope the right turnaround |
Common Mistakes to Avoid
Instinct | Why It Doesn't Help | Do This Instead |
Building a model with idealized constraints instead of the real operational rules | Produces a mathematically optimal answer that's unusable in practice (an "optimal" schedule that violates labor law, for instance) | Include the actual operational constraints from the start, even if they make the model harder |
Treating average demand as certain when building inventory or production models | Ignores demand variability, which is often the actual driver of stockouts or excess inventory | Model demand uncertainty explicitly where it matters, rather than assuming a single average value |
Optimizing one part of the operation (like inventory) in isolation from related decisions (like production or logistics) | Can produce a locally optimal decision that creates problems elsewhere in the system | Consider how the areas interact, even if you're only formally modeling one part |
Assuming a spreadsheet-based heuristic is "close enough" to optimal | Often leaves real savings on the table, especially as the problem scales | Compare against a properly formulated optimization model to see the actual gap |
Skipping validation of the model's output against real operational judgment | A mathematically valid solution can still reflect a modeling error that isn't obvious from the numbers alone | Sanity-check results against operational experience before implementing them |
Assuming a model built for one scale will work unchanged at a larger scale | Solving time and data requirements can grow significantly as networks, workforce size, or SKU counts increase | Consider computational scalability when the model is built, not after it stops working |
How It Works
1. Share the operational problem
Tell us the actual business decision, the available data, and the real-world constraints involved
2. We identify the right model and technique
We confirm whether this is supply-chain, workforce, inventory, production, or logistics optimization — or a combination — and formulate accordingly
3. We build and solve the model
We formulate the mathematical model and apply an appropriate solving method, verified against your real constraints
4. We validate against operational reality
We check the result makes practical sense, not just that it's mathematically optimal
5. We explain the model and the recommendation
You get a plain-English explanation of the approach and the result, usable for a class, a stakeholder presentation, or direct implementation
Why Codersarts
Operations research only creates value when the model reflects how the operation actually works — the math is rarely the hard part. We build models grounded in real constraints, across supply-chain, workforce, inventory, production, and logistics problems, for professionals making real resourcing and planning decisions. We validate results against operational judgment, not just mathematical correctness.
Turnaround Time
Well-scoped problems with available data and clear constraints are typically turned around within a few days to a week. Larger or more open-ended operational problems, where data gathering or constraint definition itself takes real work, are scoped with a clear timeline once we understand the problem. Research and launch deadlines are prioritized accordingly — let us know your timeline upfront.
FAQ
I don't have clean data — can you still help model this?
Yes — we can help formulate the model structure and work with the data you have, including flagging where better data would meaningfully improve the result.
Do you use specific tools for these problems?
Yes — we work across Python (PuLP, OR-Tools, Pyomo), Excel-based optimization (Solver), and commercial tools depending on your context and constraints.
Is this for coursework, or only real business problems?
Both — we support academic coursework and research as well as real operational decisions.
Can you help improve a spreadsheet-based process we already use?
Yes — comparing an existing heuristic or spreadsheet approach against a properly optimized model is a common and often eye-opening request.
Will you explain the model, or just give me a recommendation?
We explain the model, the method, and the reasoning behind the recommendation — useful whether you need to defend it academically or to stakeholders.
Is our project kept confidential?
Yes. Your data, code, and results stay confidential, and an NDA is available on request.
Related Services
Working Through an Operations Research Problem?
Whether it's research or a real operational decision — get a model that reflects how the operation actually works.