Amar Oukil

Exploiting sparsity in pricing routines for the capacitated arc routing problem

The capacitated arc routing problem (CARP) is a well-known and fundamental vehicle routing problem. A promising exact solution approach to the CARP is to model it as a set covering problem and solve it via branch-cut-and-price. The bottleneck in this approach is the pricing (column generation) routine. In this paper, we note that most CARP instances arising in practical applications are defined on sparse graphs. We show how to exploit this sparsity to yield faster pricing routines. Extensive computational results are given.

Maximum appreciative cross-efficiency in DEA

Suggests a cross-efficiency based ranking method in DEA.Uses individual appreciativeness to generate maximum appreciative cross-efficiency.Combines maximum cross-efficiency, preference voting, and OWA operator, to provide a full ranking.Illustrates an application of the proposed method in ranking 15 baseball players. Ranking decision making units (DMUs) is one of the most important applications of data envelopment analysis (DEA). In this paper, we exploit the power of individual appreciativeness in developing a methodology that combines cross-evaluation, preference voting and ordered weighted averaging (OWA). We show that each stage of the proposed methodology enhances discrimination among DMUs while offering more flexibility to the decision process. Our approach is illustrated through an example involving 15 baseball players.

Pricing routines for vehicle routing with time windows on road networks

Several very effective exact algorithms have been developed for vehicle routing problems with time windows. Unfortunately, most of these algorithms cannot be applied to instances that are defined on road networks, because they implicitly assume that the cheapest path between two customers is equal to the quickest path. Garaix and co-authors proposed to tackle this issue by first storing alternative paths in an auxiliary multi-graph, and then using that multi-graph within a branch-and-price algorithm. We show that, if one works with the original road network rather than the multi-graph, then one can solve the pricing subproblem more quickly, in both theory and practice.

Ranking via composite weighting schemes under a DEA cross-evaluation framework

Abstract Data envelopment analysis (DEA) is one of the most powerful tools for ranking decision making units (DMUs). In this paper, we present a new perspective for ranking DMUs under a DEA peer-evaluation framework. We exploit the property of multiple weighting schemes generated over the cross evaluation process in developing a methodology that yields not only robust ranking patterns but also more realistic sets of weights for the DMUs. The robustness of the proposed methodology is evaluated using OWA combinations involving different minimax disparity models and different levels of optimism of the decision maker. We show that discrimination is boosted at each stage of the decision process. As an illustration, our approach is applied for ranking a sample of baseball players.