A Local-Search-Based Heuristic for the Demand-Constrained Multidimensional Knapsack Problem
We consider an extension of the 0–1 multidimensional knapsack problem in which there are greater-than-or-equal-to inequalities, called demand constraints, in addition to the standard less-than-or-equal-to constraints. Moreover, the objective function coefficients are not constrained in sign. This problem is worth considering because it is embedded in models of practical application, it has an intriguing combinatorial structure, and it appears to be a challenging problem for commercial ILP solvers. Our approach is based on a nested tabu-search algorithm in which neighborhoods with different structures are exploited. First, a tabu-search procedure is carried out in which mainly the infeasible region is explored. Once feasibility has been established, a second tabu-search procedure, which analyzes only feasible solutions, is applied. The algorithm has been tested on a wide set of instances. Computational results are discussed.