Allocation tools are usually domain-locked — one app for classrooms, another for seating charts, another for rosters. Balanced Allocate is domain-agnostic. Upload any spreadsheet, define your own constraints, and the solver finds the mathematically optimal assignment. It runs Google's OR-Tools CP-SAT engine — the same solver used in logistics and airline scheduling — but with a UI anyone can use. No code, no formulas. Budgets, quotas, balance, relationships, and pins all evaluated simultaneously
No reviews yetBe the first to leave a review for Balanced Allocate
Maker
📌
I'm an engineer with 15 years on LNG and gas processing facilities. My job is optimising systems under constraints — pressure limits, flow rates, equipment capacities. Every variable affects every other variable.
I kept noticing the same problem outside of engineering. A friend running a netball club spent three hours grading 30 kids into teams with a whiteboard. A school deputy principal spent a full week placing students into classes using sticky notes. A procurement colleague spent two days forming a vendor panel from 40 tender responses. They were all solving the same optimisation problem I solve at work — distribute things into groups while respecting multiple competing constraints — but with no tools beyond a spreadsheet.
Spreadsheets can't do this properly. You move one item to fix one rule and break three others. The problem isn't complexity — it's that humans can only hold a few constraints in their heads at once. A solver can evaluate all of them simultaneously.
Google's OR-Tools CP-SAT solver is the same engine used by logistics companies and airlines for scheduling. It's free and extremely powerful. But it requires Python programming and mathematical modelling to use — which puts it out of reach for a teacher, a club volunteer, or a procurement officer.
So I built a UI over it. Upload a CSV, set your constraints with dropdowns, hit Run. The solver finds the mathematically optimal allocation in seconds and validates every rule. No code, no formulas, no optimisation knowledge required.
The approach evolved through real use cases. I started with simple equal distribution, then added budget caps when a school needed IEP hour limits. Then quotas when a sports club needed position coverage. Then relationships when siblings and conflicts came up. Then preferences when VIPs needed to be steered but not forced. Each constraint type came from watching someone struggle with a specific rule their spreadsheet couldn't enforce.
Nine worked examples ship with the tool — classroom placement, wedding seating, fleet loading, shift scheduling, sports grading, grant selection, tenant allocation, production lines, and vendor panels. Each has sample data you can try in two minutes.
Report
Maker
Most allocation tools solve one domain. There are dedicated apps for classroom placement, dedicated apps for sports grading, dedicated apps for seating charts. Each one hardcodes the rules for that specific problem and charges accordingly. If your problem doesn't fit their template, you're back to a spreadsheet.
Balanced Allocate doesn't know what your data means. It doesn't know the difference between a student and a truck. You upload any spreadsheet, define your own constraints, and the solver finds the optimal assignment. The same tool that places 200 students into 8 classes also forms a 10-vendor procurement panel from 40 tender responses. Same solver, same interface, different CSV.
What makes it different:
The solver is real. It's Google's OR-Tools CP-SAT — the same mathematical optimisation engine used in logistics, airline scheduling, and supply chain planning. It doesn't approximate or shuffle randomly. It evaluates the full solution space and returns the mathematically optimal result, or tells you the problem is infeasible and shows you which constraints conflict.
Every constraint type works simultaneously. Budgets cap numeric totals per group. Quotas enforce counts or percentages. Balance spreads values evenly. Relationships keep items together or apart. Preferences steer items toward groups with adjustable strength. Pins lock items in place. Maximise and Minimise concentrate the best or worst values where you want them. Combined budgets share a limit across multiple groups. Set as many as you need — the solver handles the interactions between them.
Validation is built in. After every run, the tool checks every rule you set and shows pass or fail. You don't have to manually verify the results. Export the Excel report and the constraint audit trail is already there — every rule, every result, documented.
The select/reject pattern is native. Most allocation tools assume every item gets placed. Balanced Allocate handles problems where most items are rejected — grant selection, vendor shortlisting, award panels. Set a fixed-size target group and a remainder group. The solver picks the best candidates that satisfy all constraints and puts the rest in the reject pool.
There is no domain lock-in. The nine worked examples cover education, sports, events, healthcare, logistics, government, real estate, manufacturing, and procurement. Users have also applied it to fantasy league drafts, chore rotations, and conference breakout sessions. If it goes into groups and you have rules, it works.
Most allocation tools solve one domain. There are dedicated apps for classroom placement, dedicated apps for sports grading, dedicated apps for seating charts. Each one hardcodes the rules for that specific problem and charges accordingly. If your problem doesn't fit their template, you're back to a spreadsheet.
Balanced Allocate doesn't know what your data means. It doesn't know the difference between a student and a truck. You upload any spreadsheet, define your own constraints, and the solver finds the optimal assignment. The same tool that places 200 students into 8 classes also forms a 10-vendor procurement panel from 40 tender responses. Same solver, same interface, different CSV.
What makes it different:
The solver is real. It's Google's OR-Tools CP-SAT — the same mathematical optimisation engine used in logistics, airline scheduling, and supply chain planning. It doesn't approximate or shuffle randomly. It evaluates the full solution space and returns the mathematically optimal result, or tells you the problem is infeasible and shows you which constraints conflict.
Every constraint type works simultaneously. Budgets cap numeric totals per group. Quotas enforce counts or percentages. Balance spreads values evenly. Relationships keep items together or apart. Preferences steer items toward groups with adjustable strength. Pins lock items in place. Maximise and Minimise concentrate the best or worst values where you want them. Combined budgets share a limit across multiple groups. Set as many as you need — the solver handles the interactions between them.
Validation is built in. After every run, the tool checks every rule you set and shows pass or fail. You don't have to manually verify the results. Export the Excel report and the constraint audit trail is already there — every rule, every result, documented.
The select/reject pattern is native. Most allocation tools assume every item gets placed. Balanced Allocate handles problems where most items are rejected — grant selection, vendor shortlisting, award panels. Set a fixed-size target group and a remainder group. The solver picks the best candidates that satisfy all constraints and puts the rest in the reject pool.
There is no domain lock-in. The nine worked examples cover education, sports, events, healthcare, logistics, government, real estate, manufacturing, and procurement. Users have also applied it to fantasy league drafts, chore rotations, and conference breakout sessions. If it goes into groups and you have rules, it works.