Distributed Approximation Algorithms for the Multiple Knapsack Problem

We consider the distributed version of the Multiple Knapsack Problem (MKP), wherem items are to be distributed amongst n processors, each with a knapsack. We propose different distributed approximation algorithms with a tradeoff between time and message complexities. The algorithms are based on the greedy approach of assigning the best item to the knapsack with the largest capacity. These algorithms obtain a solution with a bound of 1 n+1 times the optimum solution, with either O (m logn) time and O (mn) messages, or O (m) time and O (