fix: resolve 3 critical ProductionSolver bugs

- Bug #1: CalculateExecution now filters product by targetResourceId
  instead of blindly taking .First() — fixes multi-product recipes
- Bug #2: Replace visited HashSet with recipeOutput tracker — allows
  re-processing when demand increases (double targets on same resource)
- Bug #3: Clamp effectiveSpeed to MinEffectiveSpeed (0.05) floor —
  prevents division by zero with heavy productivity module stacks

Also: switch from Stack (DFS) to Queue (BFS) for more predictable
resolution order; add 4 regression tests (24 total, all green)
This commit is contained in:
2026-07-02 13:27:43 +03:00
parent 29507813e5
commit a8c9808062
2 changed files with 245 additions and 33 deletions
+56 -33
View File
@@ -11,6 +11,7 @@ public sealed class ProductionSolver : ISolver
{
private readonly IRecipeRepository _repository;
private const double DefaultMachineSpeed = 0.5; // base crafting speed for all machines
private const double MinEffectiveSpeed = 0.05; // safety floor: machine cannot slow below 5% of base
public ProductionSolver(IRecipeRepository repository)
{
@@ -26,42 +27,57 @@ public sealed class ProductionSolver : ISolver
{
if (targets == null) throw new ArgumentNullException(nameof(targets));
var demand = new Dictionary<int, double>(); // resourceId -> needed amount/sec
var demand = new Dictionary<int, double>(); // resourceId -> needed amount/sec
var executions = new List<RecipeExecution>();
var visited = new HashSet<int>(); // prevent cycles
var recipeOutput = new Dictionary<int, double>(); // recipeId -> total output already planned (sec)
// Seed with user targets
foreach (var target in targets)
{
demand[target.ResourceId] = target.AmountPerSecond;
demand[target.ResourceId] = demand.GetValueOrDefault(target.ResourceId) + target.AmountPerSecond;
}
// DFS: resolve each demand
var stack = new Stack<int>(targets.Select(t => t.ResourceId));
while (stack.Count > 0)
{
var resourceId = stack.Pop();
// Collect all resource IDs that need resolving (including ingredients discovered during DFS)
var unresolved = new Queue<int>(targets.Select(t => t.ResourceId));
if (!demand.TryGetValue(resourceId, out var needed) || needed <= 0)
while (unresolved.Count > 0)
{
var resourceId = unresolved.Dequeue();
// Skip if demand was already fully satisfied or is zero
if (!demand.TryGetValue(resourceId, out var needed) || needed <= 0.001)
continue;
// Find the best recipe that produces this resource
var recipe = FindBestRecipe(resourceId, modules);
if (recipe == null)
{
// This is a raw resource — nothing to craft
// Raw resource — nothing to craft
continue;
}
// Avoid re-processing the same recipe for the same resource
var recipeKey = recipe.Id;
if (visited.Contains(recipeKey))
continue;
visited.Add(recipeKey);
// Calculate how much MORE this recipe needs to produce
var alreadyPlanned = recipeOutput.GetValueOrDefault(recipe.Id);
var remainingNeeded = needed - alreadyPlanned;
// Calculate recipe rate needed
if (remainingNeeded <= 0.001)
{
// Already covered by a previous execution of this recipe
continue;
}
// Calculate execution for the remaining demand
var (recipeRate, effectiveSpeed, effectiveProductivity, machineCount, machineId) =
CalculateExecution(recipe, needed, modules);
CalculateExecution(recipe, remainingNeeded, resourceId, modules);
// Update tracked output for this recipe
var mainProduct = recipe.Products.FirstOrDefault(p => p.ResourceId == resourceId);
if (mainProduct != null)
{
var outputPerCycle = mainProduct.Amount * (1 + effectiveProductivity);
var newOutput = outputPerCycle * recipeRate * (int)Math.Ceiling(machineCount);
recipeOutput[recipe.Id] = alreadyPlanned + newOutput;
}
// Record execution
executions.Add(new RecipeExecution(
@@ -71,18 +87,15 @@ public sealed class ProductionSolver : ISolver
// Add ingredient demands (DFS)
foreach (var ingredient in recipe.Ingredients)
{
// How much of this ingredient does one cycle consume?
// With productivity, input is NOT reduced — only output is increased
var ingredientNeeded = ingredient.Amount * recipeRate;
var ingredientNeeded = ingredient.Amount * recipeRate * (int)Math.Ceiling(machineCount);
demand[ingredient.ResourceId] = demand.GetValueOrDefault(ingredient.ResourceId) + ingredientNeeded;
stack.Push(ingredient.ResourceId);
unresolved.Enqueue(ingredient.ResourceId);
}
}
// Build resource flows
var resourceFlows = BuildResourceFlows(executions, demand);
var requiredInputs = BuildRequiredInputs(executions, demand, _repository.Recipes);
var resourceFlows = BuildResourceFlows(executions);
var requiredInputs = BuildRequiredInputs(demand, _repository.Recipes);
return new ProductionResult(
executions.AsReadOnly(),
@@ -114,29 +127,36 @@ public sealed class ProductionSolver : ISolver
// Return the recipe with the best efficiency (highest output per cycle)
return candidates.OrderByDescending(r =>
{
var product = r.Products.First(p => p.ResourceId == resourceId);
return product.Amount / r.CraftTime;
var product = r.Products.FirstOrDefault(p => p.ResourceId == resourceId);
return product != null ? product.Amount / r.CraftTime : 0;
}).First();
}
private (double recipeRate, double effectiveSpeed, double effectiveProductivity, double machineCount, int machineId)
CalculateExecution(Recipe recipe, double neededPerSec, IReadOnlyCollection<Module> modules)
CalculateExecution(Recipe recipe, double neededPerSec, int targetResourceId, IReadOnlyCollection<Module> modules)
{
// Find the main product for our target resource
var mainProduct = recipe.Products.First();
// FIX #1: Find the product matching the target resource, not just the first one
var mainProduct = recipe.Products.FirstOrDefault(p => p.ResourceId == targetResourceId);
if (mainProduct == null)
throw new InvalidOperationException(
$"Recipe '{recipe.Name}' does not produce resource ID {targetResourceId}.");
// Calculate module bonuses
var totalSpeedBonus = modules.Sum(m => m.SpeedBonus);
var totalProductivityBonus = modules.Sum(m => m.ProductivityBonus);
// Effective speed = base speed * (1 + speed bonus)
var effectiveSpeed = DefaultMachineSpeed * (1 + totalSpeedBonus);
// FIX #3: Clamp to minimum to prevent division by zero
var rawSpeed = DefaultMachineSpeed * (1 + totalSpeedBonus);
var effectiveSpeed = Math.Max(rawSpeed, MinEffectiveSpeed);
// Effective productivity
var effectiveProductivity = totalProductivityBonus;
// Output per cycle with productivity
var outputPerCycle = mainProduct.Amount * (1 + effectiveProductivity);
if (outputPerCycle <= 0)
outputPerCycle = mainProduct.Amount; // fallback: productivity should not zero out output
// Recipe rate (cycles/sec) needed
var recipeRate = neededPerSec / outputPerCycle;
@@ -175,7 +195,7 @@ public sealed class ProductionSolver : ISolver
return candidates.OrderByDescending(m => m.Value.CraftingSpeed).First().Value.Id;
}
private Dictionary<int, double> BuildResourceFlows(List<RecipeExecution> executions, Dictionary<int, double> demand)
private Dictionary<int, double> BuildResourceFlows(List<RecipeExecution> executions)
{
var flows = new Dictionary<int, double>();
@@ -202,15 +222,18 @@ public sealed class ProductionSolver : ISolver
return flows;
}
private Dictionary<int, double> BuildRequiredInputs(List<RecipeExecution> executions, Dictionary<int, double> demand, IReadOnlyDictionary<int, Recipe> recipes)
private Dictionary<int, double> BuildRequiredInputs(Dictionary<int, double> demand, IReadOnlyDictionary<int, Recipe> recipes)
{
var inputs = new Dictionary<int, double>();
foreach (var (resourceId, amount) in demand)
{
if (amount <= 0.001)
continue;
// If no recipe produces this, it's a raw input
var hasProducer = recipes.Any(r => r.Value.Products.Any(p => p.ResourceId == resourceId));
if (!hasProducer && amount > 0)
if (!hasProducer)
{
inputs[resourceId] = amount;
}
@@ -0,0 +1,189 @@
using FactorioCalc.Domain;
using FactorioCalc.Solver;
using Xunit;
namespace FactorioCalc.Tests;
/// <summary>
/// Regression tests for critical bugs fixed in ProductionSolver.
/// </summary>
public class SolverBugFixTests
{
// --- Bug #1: mainProduct should filter by target resourceId, not .First() ---
[Fact]
public void Solve_MultiProductRecipe_UsesCorrectProduct()
{
// Simulate a recipe that produces multiple products (like oil refining)
var resources = new Dictionary<int, Resource>
{
{ 1, new Resource(1, "Crude Oil") },
{ 2, new Resource(2, "Light Oil") },
{ 3, new Resource(3, "Heavy Oil") },
};
var machines = new Dictionary<int, Machine>
{
{ 1, new Machine(1, "Chemical Plant", 0.5, 6.0, 4, new[] { "advanced-crafting" }) },
};
// One recipe produces BOTH Light Oil and Heavy Oil
var recipes = new Dictionary<int, Recipe>
{
{
1, new Recipe(1, "Oil Refining", "advanced-crafting", 4.0, "advanced-crafting",
new[] { new Ingredient(1, 1) },
new[] { new Product(3, 1), new Product(2, 1) }) // Heavy Oil first, Light Oil second
},
};
var repo = new TestRepository(recipes, resources, machines, new Dictionary<int, Module>());
var solver = new ProductionSolver(repo);
// Target Light Oil (resourceId=2) — it's the SECOND product in the list
var targets = new[] { new ProductionTarget(2, 10) };
var result = solver.Solve(targets);
Assert.Single(result.Executions);
// Should not throw — the solver correctly finds the matching product
}
// --- Bug #2: DFS visited should not block recalculation for double targets ---
[Fact]
public void Solve_DoubleTargetsForResource_AggregatesDemand()
{
var resources = new Dictionary<int, Resource>
{
{ 1, new Resource(1, "Iron Ore") },
{ 7, new Resource(7, "Iron Plate") },
{ 9, new Resource(9, "Steel Plate") },
{ 5, new Resource(5, "Coal") },
};
var machines = new Dictionary<int, Machine>
{
{ 3, new Machine(3, "Smelter", 0.5, 3.0, 2, new[] { "smelting" }) },
};
var recipes = new Dictionary<int, Recipe>
{
{
1, new Recipe(1, "Iron Plate", "smelting", 3.0, "smelting",
new[] { new Ingredient(1, 1) },
new[] { new Product(7, 1) })
},
{
2, new Recipe(2, "Steel Plate", "smelting", 5.0, "smelting",
new[] { new Ingredient(7, 2), new Ingredient(5, 1) },
new[] { new Product(9, 1) })
},
};
var repo = new TestRepository(recipes, resources, machines, new Dictionary<int, Module>());
var solver = new ProductionSolver(repo);
// Two targets that both need Iron Plate: Steel Plate (needs 2/sec × 2 iron plate) + direct 5/sec
var targets = new[]
{
new ProductionTarget(9, 2), // Steel Plate → needs 4 Iron Plate/sec
new ProductionTarget(7, 5), // Iron Plate direct → needs 5 more/sec
};
var result = solver.Solve(targets);
// Should have both Steel Plate and Iron Plate executions
Assert.Equal(2, result.Executions.Count);
var ironPlateExec = result.Executions.First(e => e.RecipeId == 1);
// Iron Plate should account for BOTH demands (4 from steel + 5 direct = 9 total)
Assert.True(ironPlateExec.MachineCount >= 9,
$"Expected at least 9 machines for Iron Plate (demand=9/sec), got {ironPlateExec.MachineCount}");
}
// --- Bug #3: effectiveSpeed should not go to zero with heavy productivity modules ---
[Fact]
public void Solve_HeavyProductivityModules_DoesNotDivideByZero()
{
var resources = new Dictionary<int, Resource>
{
{ 1, new Resource(1, "Iron Ore") },
{ 7, new Resource(7, "Iron Plate") },
};
var machines = new Dictionary<int, Machine>
{
{ 3, new Machine(3, "Smelter", 0.5, 3.0, 2, new[] { "smelting" }) },
};
var recipes = new Dictionary<int, Recipe>
{
{
1, new Recipe(1, "Iron Plate", "smelting", 3.0, "smelting",
new[] { new Ingredient(1, 1) },
new[] { new Product(7, 1) })
},
};
// Extreme productivity modules: -20% speed × 4 slots = -80% total speed
var extremeModules = new[]
{
new Module(1, "Prod Mod 3", ModuleType.Productivity, -0.20, 0.30, -0.15),
new Module(2, "Prod Mod 3", ModuleType.Productivity, -0.20, 0.30, -0.15),
new Module(3, "Prod Mod 3", ModuleType.Productivity, -0.20, 0.30, -0.15),
new Module(4, "Prod Mod 3", ModuleType.Productivity, -0.20, 0.30, -0.15),
};
var moduleDict = new Dictionary<int, Module>();
for (var i = 0; i < extremeModules.Length; i++)
moduleDict[i + 1] = extremeModules[i];
var repo = new TestRepository(recipes, resources, machines, moduleDict);
var solver = new ProductionSolver(repo);
var targets = new[] { new ProductionTarget(7, 10) };
// Should NOT throw DivideByZeroException
var result = solver.SolveWithModules(targets, extremeModules);
Assert.Single(result.Executions);
var exec = result.Executions.First();
// Speed should be clamped to minimum (0.05), not negative or zero
Assert.True(exec.EffectiveSpeed >= 0.05, $"EffectiveSpeed {exec.EffectiveSpeed} below minimum");
Assert.True(exec.MachineCount > 0 && exec.MachineCount < int.MaxValue,
$"MachineCount {exec.MachineCount} is unreasonable");
}
[Fact]
public void Solve_ProductivityModules_IncreasesOutput()
{
var resources = new Dictionary<int, Resource>
{
{ 1, new Resource(1, "Iron Ore") },
{ 7, new Resource(7, "Iron Plate") },
};
var machines = new Dictionary<int, Machine>
{
{ 3, new Machine(3, "Smelter", 0.5, 3.0, 2, new[] { "smelting" }) },
};
var recipes = new Dictionary<int, Recipe>
{
{
1, new Recipe(1, "Iron Plate", "smelting", 3.0, "smelting",
new[] { new Ingredient(1, 1) },
new[] { new Product(7, 1) })
},
};
var prodModule = new Module(1, "Prod Mod 1", ModuleType.Productivity, -0.10, 0.10, -0.05);
var repo = new TestRepository(recipes, resources, machines, new Dictionary<int, Module> { { 1, prodModule } });
var solver = new ProductionSolver(repo);
var targets = new[] { new ProductionTarget(7, 10) };
var resultNoModules = solver.Solve(targets);
var resultWithModules = solver.SolveWithModules(targets, new[] { prodModule });
var execNoModules = resultNoModules.Executions.First();
var execWithModules = resultWithModules.Executions.First();
// With productivity, we need fewer machines because output per cycle is higher
// (even though speed is lower, the +10% output compensates)
Assert.True(execWithModules.EffectiveProductivity > 0, "Productivity bonus should be positive");
}
}