A 2-level hierarchical extension of the maximal covering location problem is considered and an e0ective method for its solution developed. A combined Lagrangean–surrogate (L–S) relaxation is de6ned which reduces to a 0–1 knapsack problem. Tests were carried out using a subgradient-based heuristic incorporating the L–S relaxation, with the resulting knapsack problems being solved both with and w...

We describe a tabu search (TS) approach for solving general zero-one mixed integer programming (MIP) problems that exploits the extreme point property of zero-one solutions. Specialized choice rules and aspiration criteria are identified for the problems, expressed as functions of integer infeasibility measures and objective function values. The first-level TS mechanisms are then extended with ...

We propose a new hybrid approach to solve the unbounded integer knapsack problem (UKP), where valid inequalities are generated based on intermediate solutions of an equivalent forward dynamic programming formulation. These inequalities help tighten the initial LP relaxation of the UKP, and therefore improve the overall computational efficiency. We also extended this approach to solve the multi-...

The well-studied 0=1 Knapsack and Subset-Sum Problem are maximization problems that have an equivalent minimization version. While exact algorithms for one of these two versions also yield an exact solution for the other version, this does not apply to -approximate algorithms. We present several -approximate Greedy Algorithms for the minimization version of the 0=1 Knapsack and the Subset-Sum P...