通过算法比较许多客户的多个价格选项 [英] Comparing multiple price options for many customers algorithmically

问题描述

我们有 1,000,000 名客户.每种商品的销售成本可以表示为价格 A 或价格 B.

We have 1,000,000 customers. The cost of goods sold for each of them can be expressed as price A or price B.

价格A<<价格 B.

价格 A 和价格 B 不是线性关系.在某些情况下，B 的价格是其 2 倍，在某些情况下是 100 倍.

Price A and Price B are not linear to each other. In some cases B is 2 times as expensive, in some it is 100 times.

A 上所有客户的成本是

cost of all the customers on A is

min( (sum(A)/count(A)) , 100 ) * count(A)实际上，如果 A 上的所有客户的平均成本小于 100，则将向上取整为 100.

min( (sum(A)/count(A)) , 100 ) * count(A) Effectively, the average cost of all the customers on A will be rounded up to 100 if it is less than 100.

B 没有这样的限制.

我想在他们的商品上花最少的钱.

I would like to spend the least amount of money on their goods.

如何最大化

cost=min( (sum(A)/count(A)) , 100 ) * count(A) + sum(B)我一直认为这是双背包问题的一种形式，但我无法解决...

cost=min( (sum(A)/count(A)) , 100 ) * count(A) + sum(B) I keep seeing this as a form of a dual knapsack problem, but I can't get it right ...

我可能会用 Python 解决这个问题，很有可能，尽管我怀疑这很重要.

I'd be probably solving this in Python, most likely, although I doubt that matters much.

我已经通过为 x y z 分配分数并在此基础上进行过滤来进行手动分析，我对更多的计算解决方案感兴趣.

I've done manual analyses by assigning scores to x y z and filtering based upon that, I'm interested in more of a computational solution.

有什么方法可以推荐吗?

Any approaches to recommend?

推荐答案

在别处以更简单的方式重申.

Restated in a much easier way elsewhere.

为多个客户搜索最合适的价格

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