Adaptive approach heuristics for the generalized assignment problem

The Generalized Assignment Problem consists in assigning a set of tasks to a set of agents with minimum cost. Each agent has a limited amount of a single resource and each task must be assigned to one and only one agent, requiring a certain amount of the resource of the agent. We present new metaheuristics for the generalized assignment problem based on hybrid approaches. One metaheuristic is a MAX-MIN Ant System (MMAS), an improved version of the Ant System, which was recently proposed by Stutzle and Hoos to combinatorial optimization problems, and it can be seen has an adaptive sampling algorithm that takes in consideration the experience gathered in earlier iterations of the algorithm. Moreover, the latter heuristic is combined with local search and tabu search heuristics to improve the search. A greedy randomized adaptive search heuristic (GRASP) is also proposed. Several neighborhoods are studied, including one based on ejection chains that produces good moves without increasing the computational effort. We present computational results of the comparative performance, followed by concluding remarks and ideas on future research in generalized assignment related problems.Metaheuristics, generalized assignment, local search, GRASP, tabu search, ant systems

Exact solutions to a class of stochastic generalized assignment problems

This paper deals with a stochastic Generalized Assignment Problem with recourse. Only a random subset of the given set of jobs will require to be actually processed. An assignment of each job to an agent is decided a priori, and once the demands are known, reassignments can be performed if there are overloaded agents. We construct a convex approximation of the objective function that is sharp at all feasible solutions. We then present three versions of an exact algorithm to solve this problem, based on branch and bound techniques, optimality cuts, and a special purpose lower bound. numerical results are reported.

The Agents-are-Substitutes Property in Continuous Generalized Assignment Problems

The VCG mechanism has some nice properties if the agents-are-substitutes property holds.For example, for combinatorial auctions the property assures that the VCG mechanism is supported by a pricing equilibrium. The existence of such a pricing equilibrium is a necessary condition for the existence of ascending auctions that are equivalent to the VCG mechanism.Although it is known that the agents-are-substitutes property is important in several settings few problems or subclasses of problems are proven to have the property.In this paper we show for a class of problems that the agents-are-substitutes property holds. Moreover we give two rather natural and small extensions that do not have this property in general.Furthermore we show that in our simple problem class we need the possibility of price discrimination.operations research and management science;

DEPENDENCY DIRECTED BACKTRACKING IN GENERALIZED SATISFICING ASSIGNMENT PROBLEMS

Many authors have described search techniques for the satisficing assignment problem: the problem of finding an interpretation for a set of discrete variables that satisfies a given set of constraints. In this paper we present a formal specification of dependency directed backtracking as applied to this problem. We also generalize the satisficing assignment problem to include limited resource constraints that arise in operations research and industrial engineering. We discuss several new search heuristics that can be applied to this generalized problem, and give some empirical results on the performance of these heuristics.Information Systems Working Papers Serie

Online Multidimensional Packing Problems in the Random-Order Model

We study online multidimensional variants of the generalized assignment problem which are used to model prominent real-world applications, such as the assignment of virtual machines with multiple resource requirements to physical infrastructure in cloud computing. These problems can be seen as an extension of the well known secretary problem and thus the standard online worst-case model cannot provide any performance guarantee. The prevailing model in this case is the random-order model, which provides a useful realistic and robust alternative. Using this model, we study the d-dimensional generalized assignment problem, where we introduce a novel technique that achieves an O(d)-competitive algorithms and prove a matching lower bound of Omega(d). Furthermore, our algorithm improves upon the best-known competitive-ratio for the online (one-dimensional) generalized assignment problem and the online knapsack problem