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#多准则决策模型-TOPSIS评价方法##R语言实现-代码MCDM=function (decision = NULL, weights = NULL, impacts = NULL) #决策矩阵，权重向量，影响因子
{ if (missing(weights))
stop("缺少\'权重向量-weights\'")
if (missing(impacts))
stop("缺少\'影响因子-impacts\'")
if (!is.matrix(decision) | is.data.frame(decision))
stop("\'决策矩阵-decision\'必须是矩阵或数据框")
if (length(weights) != ncol(decision))
stop("权重向量长度错误")
if (length(impacts) != ncol(decision))
stop("影响因子长度错误")
if (!all(weights > 0))
stop("权重必须大于零")
if (!is.character(impacts))
stop("影响因子必须是字符型 \'+\'或\'-\' 符号")
if (!all(impacts == "+" | impacts == "-"))
stop("影响因子只能是字符型 \'+\'或\'-\' 符号")
weights <- weights/sum(weights)
N <- matrix(nrow = nrow(decision), ncol = ncol(decision)) #建一个空矩阵
for (i in 1:nrow(decision)) {
for (j in 1:ncol(decision)) {
N[i, j] <- decision[i, j]/sqrt(sum(decision[, j]^2))
}
} #决策矩阵标准化
W = diag(weights) #建权重对角矩阵
V = N %*% W #构造加权规范化矩阵
#确定理想方案和负理想方案 u <- as.integer(impacts == "+") * apply(V, 2, max) + as.integer(impacts == "-") * apply(V, 2, min)
l <- as.integer(impacts == "-") * apply(V, 2, max) + as.integer(impacts == "+") * apply(V, 2, min)
#构建理想方案和负理想方案距离公式 distance_u = function(x) {
sqrt(sum((x - u)^2))
}
distance_l = function(x) {
sqrt(sum((x - l)^2))
}
#计算相对接近度并排序 du <- apply(V, 1, distance_u)
dl <- apply(V, 1, distance_l)
score <- dl/(dl + du)
outcome <- data.frame("方案"= 1:nrow(decision), 得分 = score,
排名 = rank(-score))
return(outcome)
}Author(s)Mahmoud Mosalman Yazdi <m.mosalman@gmail.com> |
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