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Systems Optimization (SOP)

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ETH Zürich - D-ITET - TIK - PISA - Variators - Knapsack
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Generally, a 0/1 knapsack problem consists of a set of items, weight and profit associated with each item, and an upper bound for the capacity of the knapsack. The task is to find a subset of items which maximizes the total of the profits in the subset, yet all selected items fit into the knapsack, i.e., the total weight does not exceed the given capacity. This single-objective problem can be extended directly to the multiobjective case by associating m different profit and m different weight values per item and m capacity bounds. The m-th objective function value of a solution (set of items) is then defined as the sum over their m-th profit values. A solution is feasible, if each of the m summation over their m-th weight values does not exceed the m-th capacity bound.

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