抄録
Given a set I={i|i=1,2,...,n} of current n items (for example, n green peppers) with their weights w_i and priorities γ_i, the food packing problem asks to find a subset I'(⊆I) so that the total weight Σ_<i∈I'> w_i is no less than a specified target weight T for each package, and it is minimized as the primary objective, and further the total priority Σ_<i∈I'> γ_i is maximized as the second objective. The combinatorial optimization problem has been known to be NP-hard, while it can be solved exactly in O(nT) time if all the input data are assumed to be integral. In this paper, we design a heuristic algorithm for the problem by applying a data rounding technique to an O(nT) time dynamic programming procedure. We also conduct numerical experiments to examine the empirical performance.
