Package solves multiple knapsack optimisation problem. Given a set of items, each with volume and value, it will allocate them to knapsacks of a given size in a way that value of top N knapsacks is as large as possible.
This package solves multiple knapsack problem by assigning items optimally to knapsacks using Mixed Integer Linear Programming (MILP) solver of choice.
We start with a list of items that we want to order with each assigned a:
Those items should be optimally packed into multiple containers of the a given size (cap). Items should be aded to containers in the way that each container is more profitable than the following one.
Package implements interface to several solvers which can be set via
Currently you can choose from those options:
lpsolve is default option.
Solve problem with CBC COIN-OR solver:
set.seed(100)devtools::install_github("dirkschumacher/rcbc")devtools::install_github("dirkschumacher/ROI.plugin.cbc")devtools::install_github("madedotcom/mknapsack")library(rcbc)library(ROI)library(ROI.plugin.cbc)library(data.table)library(mknapsack)options(mknapsack.solver = "cbc")items <- data.table(volume = pmin(rlnorm(100, log(2), log(3)), 15),profit = rgamma(100, shape = 1, scale = 100) - 25)items[, knapsack :=mknapsack(profit = profit,volume = volume,cap = 65)]#Aggregate solution to knapsacksknapsacks <- items[order(knapsack),.(volume = sum(volume), profit = sum(profit)),by = knapsack]knapsacks# knapsack volume profit# 1: 1 64.89659 5000.27608# 2: 2 64.40358 1540.40302# 3: 3 64.97235 340.92516# 4: 4 53.33824 88.02793# 5: NA 91.13399 -272.54349#