Heuristics for deciding collectively rational consumption behavior.

We consider the computational problem of testing whether observed household consumption behavior satisfies the Collective Axiom of Revealed Preferences (CARP). We propose a graph such that the existence of a node-partitioning giving rise to two induced subgraphs that are acyclic implies that the data satisfy CARP. Furthermore, we propose and implement heuristics that are quite fast, that can be used to check reasonably large datasets for CARP and that can be of particular interest when used prior to computationally demanding approaches. Finally, from the computational results we conclude that these heuristics can be effective in testing CARP.Collective model of household consumption; Collective Axiom of Revealed; Preference; Pareto efficiency; Directed graph; Graph coloring; Graph partitioning; Acyclic subgraph; Heuristics;

Heuristics for deciding collectively rational consumption behavior

We consider the computational problem of testing whether observed household consumption behavior satisfies the Collective Axiom of Revealed Preferences (CARP). We propose a graph such that the existence of a node-partitioning giving rise to two induced subgraphs that are acyclic implies that the data satisfy CARP. Furthermore, we propose and implement heuristics that are quite fast, that can be used to check reasonably large datasets for CARP and that can be of particular interest when used prior to computationally demanding approaches. Finally, from the computational results we conclude that these heuristics can be effective in testing CARP.status: publishe

Coloring Graphs Using Two Colors while Avoiding Monochromatic Cycles

We consider the problem of deciding whether a given directed graph can be vertex partitioned into two acyclic subgraphs. Applications of this problem include testing rationality of collective consumption behavior, a subject in microeconomics. We prove that the problem is NP-complete even for oriented graphs and argue that the existence of a constant-factor approximation algorithm is unlikely for an optimization version that maximizes the number of vertices that can be colored using two colors while avoiding monochromatic cycles. We present three exact algorithms—namely, an integer-programming algorithm based on cycle identification, a backtracking algorithm, and a branch-and-check algorithm. We compare these three algorithms both on real-life instances and on randomly generated graphs. We find that for the latter set of graphs, every algorithm solves instances of considerable size within a few seconds; however, the CPU time of the integer-programming algorithm increases with the number of vertices in the graph more clearly than the CPU time of the two other procedures. For real-life instances, the integer-programming algorithm solves the largest instance in about a half hour, whereas the branch-and-check algorithm takes approximately 10 minutes and the backtracking algorithm less than 5 minutes. Finally, for every algorithm, we also study empirically the transition from a high to a low probability of a YES answer as a function of the number of arcs divided by the number of vertices

Robust optimization for resource-constrained project scheduling with uncertain activity durations

The purpose of this paper is to propose models for project scheduling when there is considerable uncertainty in the activity durations, to the extent that the decision maker cannot with confidence associate probabilities with the possible scenarios. Our modeling techniques stem from robust optimization, which is a theoretical framework that enables the decision maker to produce solutions that will have a reasonably good objective value under any likely input data scenario. We develop and implement a scenario-relaxation algorithm and a scenario-relaxationbased heuristic. The first algorithm produces optimal solutions but requires excessive running times even for medium-sized instances; the second algorithm produces high-quality solutions for medium-sized instances and outperforms two benchmark heuristics