A Heuristic Algorithm for Resource Allocation/Reallocation Problem

This paper presents a 1-opt heuristic approach to solve resource allocation/reallocation problem which is known as 0/1 multichoice multidimensional knapsack problem (MMKP). The intercept matrix of the constraints is employed to find optimal or near-optimal solution of the MMKP. This heuristic approach is tested for 33 benchmark problems taken from OR library of sizes upto 7000, and the results have been compared with optimum solutions. Computational complexity is proved to be (2) of solving heuristically MMKP using this approach. The performance of our heuristic is compared with the best state-of-art heuristic algorithms with respect to the quality of the solutions found. The encouraging results especially for relatively large-size test problems indicate that this heuristic approach can successfully be used for finding good solutions for highly constrained NP-hard problems

A multiprocessor based heuristic for multi-dimensional multiple-choice knapsack problem

This paper presents a multiprocessor based heuristic algorithm for the Multi-dimensional Multiple Choice Knapsack Problem (MMKP). MMKP is a variant of the classical 0-1 knapsack problem, where items having a value and a number of resource requirements are divided into groups. Exactly one item has to be picked up from each group to achieve a maximum total value without exceeding the resource constraint of each type. MMKP has many real world applications including admission control in adaptive multimedia server system. Exact solution to this problem is NP-Hard, and hence is not feasible for real time applications like admission control. Therefore, heuristic solutions have been developed to solve the MMKP. M-HEU is one such heuristic, which solves the MMKP achieving a reasonable percentage of optimality. In this paper, we present a multiprocessor algorithm based on M-HEU, which runs in O(T/p+s(p)) time, where T is the time required by the algorithm using single processor, p is the number of processors and s(p), a function of p, is the synchronization overhead. We also present the worst-case analysis of our algorithm, the computation of the optimal number of processors as well as the lower bound of the total value that can be achieved by the heuristic