Skip to content

9.8 An ILP model for the Unbounded Knapsack Problem

Part 2 · Formulations and their properties · Chapter 9 · Chapter 9: Knapsack problems · Lecture notes, volume 2 (PDF) · Slides · Exercises: Knapsack problems · App: LP relaxation and upper bounds for the 0/1 KP

The Unbounded Knapsack Problem and its reference instance (Example 2.4) are defined in Chapter 2 «Knapsack problems», Section 2.5.

  • We introduce the following \(n\) integer variables:

    \[\begin{equation*} \label{INT_VAR_UKP} x_j = \textrm{number of copies of item $j$ inserted in the knapsack} \qquad \forall j \in \mathscr{I}. \tag{9.24} \end{equation*}\]

Using these variables, an Integer Linear Programming (ILP) model for the Unbounded Knapsack Problem (UKP) reads as follows:

\[\begin{align*} && \max \;\;{\sum_{j \in \mathscr{I}} p_j \, x_j} \label{obj_UKP} \tag{9.25a}\\[1ex] && \text{subject to} \quad {\sum_{j \in \mathscr{I}} w_j \, x_j} &\leq c, \label{cons1_UKP} \tag{9.25b}\\[1ex] &&x_j &\in \Z_{\ge 0}, & \forall j \in \mathscr{I}. \label{cons2_UKP} \tag{9.25c} \end{align*}\]

This ILP model is referred to as the \(\mathrm{ILP}_{\mathrm{UKP}}\) model.

  • Description of the objective function and constraints of the \(\mathrm{ILP}_{\mathrm{UKP}}\) model:

    • the linear objective function \(\eqref{obj_UKP}\) maximizes the total profit of the item copies selected in the knapsack;

    • constraint \(\eqref{cons1_UKP}\) ensures that the total weight of the selected item copies does not exceed the knapsack capacity (one linear constraint);

    • finally, constraints \(\eqref{cons2_UKP}\) define the variables of the ILP model; the capacity constraint already gives \(x_j \le \hat{b}_j = \lfloor c/w_j \rfloor\), for every \(j \in \mathscr{I}\).

Example 9.12: \(\mathrm{ILP}_{\mathrm{UKP}}\) model

We consider a knapsack of capacity \(c = 9\) and a set \(\mathscr{I} = \{1,2,3,4,5,6\}\) of \(6\) items (\(n = 6\)), each available in an unlimited number of copies, with the following profits and weights:

\[ \begin{array}{l|rrrrrrr} \hline & {j = 1} & {j = 2} & {j = 3} & {j = 4} & {j = 5} & {j = 6} \\ \hline {p_j} & {6} & {5} & {8} & {9} & {6} & {7} \\ {w_j} & {2} & {3} & {6} & {7} & {5} & {9} \\ \hline \end{array} \]

This is the UKP instance of Example 2.4. The \(\mathrm{ILP}_{\mathrm{UKP}}\) model for this UKP instance is:

\[ \begin{array}{rrrrrrrrrrrrr} {\max} & 6 \, {x_1} & + 5 \, {x_2} & + 8 \, {x_3} & + 9 \, {x_4} & + 6 \, {x_5} & + 7 \, {x_6} \\[0.5ex] \text{subject to} & 2 \, {x_1} & + 3 \, {x_2} & + 6 \, {x_3} & + 7 \, {x_4} & + 5 \, {x_5} & + 9 \, {x_6} & {\le} & 9 \\[0.5ex] & {x_1}, & {x_2}, & {x_3}, & {x_4}, & {x_5}, & {x_6} & { \in} & { \Z_{\ge 0}} \end{array} \]

An optimal solution \(~{\tilde{\vet x}}(\mathrm{ILP}_{\mathrm{UKP}})\) is:

\[ \tilde x_1=4,~~\tilde x_2=0,~~\tilde x_3=0,~~\tilde x_4=0,~~\tilde x_5=0,~~\tilde x_6=0 \]

The optimal value \(z(\mathrm{ILP}_{\mathrm{UKP}})\) is \(24\). The associated optimal solution of the UKP takes four copies of item \(1\), and it is the unique optimal solution.

Next: 9.9 An ILP model for the Multidimensional Knapsack Problem