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关于药品排批的那些事
发表时间：2024-03-19 11:53浏览量：312
静配的药品排批问题是一个经典的优化问题，遗传算法是一种常用的解决方法。下面是一个用C#语言实现的遗传算法示例代码： 首先，我们需要定义一个药品类，用于表示每种药品的属性和信息：
csharp
public class Drug
{
public string Name { get; set; } // 药品名称
public int Quantity { get; set; } // 药品数量
public int Frequency { get; set; } // 用药频次
public int Duration { get; set; } // 用药时长
public int Batch { get; set; } // 批次
}
接下来，我们需要定义一个批次类，用于表示每个批次的属性和信息：
csharp
public class Batch
{
public int Number { get; set; } // 批次编号
public List<Drug> Drugs { get; set; } // 批次中的药品列表
public int TotalQuantity { get; set; } // 批次中药品的总数量
public int TotalFrequency { get; set; } // 批次中药品的总用药频次
public int TotalDuration { get; set; } // 批次中药品的总用药时长
}
然后，我们需要定义一个遗传算法类，用于实现遗传算法的各个步骤：
csharp
public class GeneticAlgorithm
{
private int populationSize; // 种群大小
private double mutationRate; // 变异率
private double crossoverRate; // 交叉率
private int elitismCount; // 精英数量
private int tournamentSize; // 锦标赛大小
public GeneticAlgorithm(int populationSize, double mutationRate, double crossoverRate, int elitismCount, int tournamentSize)
{
this.populationSize = populationSize;
this.mutationRate = mutationRate;
this.crossoverRate = crossoverRate;
this.elitismCount = elitismCount;
this.tournamentSize = tournamentSize;
}
public List<Batch> Evolve(List<Drug> drugs, int maxGenerations)
{
// 初始化种群
List<List<Batch>> population = InitializePopulation(drugs);
// 进化指定代数
for (int i = 0; i < maxGenerations; i++)
{
// 计算适应度
List<Tuple<List<Batch>, double>> populationFitness = CalculateFitness(population, drugs);
// 选择精英
populationFitness.Sort((x, y) => y.Item2.CompareTo(x.Item2));
List<List<Batch>> elite = new List<List<Batch>>();
for (int j = 0; j < elitismCount; j++)
{
elite.Add(populationFitness[j].Item1);
}
// 选择新种群
List<List<Batch>> newPopulation = new List<List<Batch>>();
while (newPopulation.Count < populationSize)
{
// 锦标赛选择
List<List<Batch>> tournament = new List<List<Batch>>();
for (int j = 0; j < tournamentSize; j++)
{
tournament.Add(populationFitness[RandomHelper.Next(populationFitness.Count)].Item1);
}
tournament.Sort((x, y) => CalculateFitness(x, drugs).CompareTo(CalculateFitness(y, drugs)));
newPopulation.Add(tournament[0]);
}
// 交叉
for (int j = elitismCount; j < populationSize; j++)
{
if (RandomHelper.NextDouble() < crossoverRate)
{
int index1 = RandomHelper.Next(newPopulation.Count);
int index2 = RandomHelper.Next(newPopulation.Count);
List<Batch> offspring = Crossover(newPopulation[index1], newPopulation[index2]);
newPopulation[j] = offspring;
}
}
// 变异
for (int j = elitismCount; j < populationSize; j++)
{
if (RandomHelper.NextDouble() < mutationRate)
{
Mutate(newPopulation[j]);
}
}
// 添加精英
newPopulation.AddRange(elite);
// 更新种群
population = newPopulation;
}
// 返回最优解
List<Tuple<List<Batch>, double>> populationFitnessFinal = CalculateFitness(population, drugs);
populationFitnessFinal.Sort((x, y) => y.Item2.CompareTo(x.Item2));
return populationFitnessFinal[0].Item1;
}
private List<List<Batch>> InitializePopulation(List<Drug> drugs)
{
List<List<Batch>> population = new List<List<Batch>>();
for (int i = 0; i < populationSize; i++)
{
List<Batch> batchList = new List<Batch>();
int batchNumber = 1;
while (drugs.Count > 0)
{
Batch batch = new Batch { Number = batchNumber, Drugs = new List<Drug>() };
int totalQuantity = 0;
int totalFrequency = 0;
int totalDuration = 0;
for (int j = 0; j < drugs.Count; j++)
{
if (totalQuantity + drugs[j].Quantity <= 1000 && totalFrequency + drugs[j].Frequency <= 3 && totalDuration + drugs[j].Duration <= 7)
{
batch.Drugs.Add(drugs[j]);
totalQuantity += drugs[j].Quantity;
totalFrequency += drugs[j].Frequency;
totalDuration += drugs[j].Duration;
drugs.RemoveAt(j);
j--;
}
}
batch.TotalQuantity = totalQuantity;
batch.TotalFrequency = totalFrequency;
batch.TotalDuration = totalDuration;
batchList.Add(batch);
batchNumber++;
}
population.Add(batchList);
}
return population;
}
private double CalculateFitness(List<Batch> batchList, List<Drug> drugs)
{
double totalQuantity = 0;
double totalFrequency = 0;
double totalDuration = 0;
foreach (Batch batch in batchList)
{
totalQuantity += (double)batch.TotalQuantity / 1000;
totalFrequency += (double)batch.TotalFrequency / 3;
totalDuration += (double)batch.TotalDuration / 7;
}
double fitness = 1 / (1 + Math.Pow(totalQuantity, 2) + Math.Pow(totalFrequency, 2) + Math.Pow(totalDuration, 2));
return fitness;
}
private List<Tuple<List<Batch>, double>> CalculateFitness(List<List<Batch>> population, List<Drug> drugs)
{
List<Tuple<List<Batch>, double>> populationFitness = new List<Tuple<List<Batch>, double>>();
foreach (List<Batch> batchList in population)
{
double fitness = CalculateFitness(batchList, drugs);
populationFitness.Add(new Tuple<List<Batch>, double>(batchList, fitness));
}
return populationFitness;
}
private List<Batch> Crossover(List<Batch> parent1, List<Batch> parent2)
{
List<Batch> offspring = new List<Batch>();
int midpoint = RandomHelper.Next(parent1.Count);
for (int i = 0; i < parent1.Count; i++)
{
if (i < midpoint) { offspring.Add(parent1[i]); } else { offspring.Add(parent2[i]); } } return offspring; }
private void Mutate(List<Batch> batchList)
{
int index1 = RandomHelper.Next(batchList.Count);
int index2 = RandomHelper.Next(batchList.Count);
Batch temp = batchList[index1];
batchList[index1] = batchList[index2];
batchList[index2] = temp;
}
}