Wednesday, May 28, 2014

Knapsack Loading Problem in Excel Solver

Optimizing the Loading

of a Limited Compartment

This is a classic Solver problem with many possible variations. Knapsack problems involve selecting the correct items to load into a compartment which is limited (Constrained) in some way such as by its size or maximum weight of its load. Objects selected for loading must maximize or minimize a given criterion while at the same time staying within the Constraints of the compartment.

These type of optimization problems are known as Knapsack Problems because of the well known classic example of selecting the correct items to optimally fill a camper’s knapsack. The knapsack has a limited weight-carrying capacity and items are selected that optimize at least one criterion while not exceeding the knapsack’s weight-carrying capacity.

 

The Famous Knapsack

Loading Problem

A knapsack is being loaded for a camping trip. This knapsack has a maximum weight-carrying limit and a maximum load size limit. The camper can choose from 4 different food items to put into the knapsack. The selected items must maximize the overall number calories and provide at least a minimum number of grams of protein while not exceeding the maximum load size and weight-carry capacity of the knapsack.

The knapsack’s load cannot exceed a weight of 10 kilograms or a volume of 0.125 m3. The load of food items must contain at least 200 grams of protein.

The load may contain any number of each of the 4 following food items:

- Candy Bar

- Sandwich

- Can of Juice

- Apple

Specific information about each food items are as follows:

excel solver, solver, statistics, knapsack, knapsack problem,optimize loading, optimization(Click on Image To See a Larger Version)

 

Excel Solver Problem Solving Steps

 

Excel Solver Step 1 – Determine the Objective

In this case, the objective is to maximize the calories in the load. The cell calculating the sum total number of calories is the Objective Cell.

 

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1 comment:

  1. YongMakaoSeptember 23, 2026 at 9:38 AM

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