Learn More
In this paper we formulate a combinatorial auction brokering problem as a set packing problem and apply a simulated annealing heuristic with hybrid local moves to solve the problem. We study the existing exact and non-exact approaches to the problem and analyze the performance of those approaches. We compared our heuris-tic with the leading exact method(More)
In this paper, we address the metric learning problem utilizing a margin-based approach. Our metric learning problem is formulated as a quadratic semi-definite programming problem (QSDP) with local neighborhood constraints, which is based on the Support Vector Machine (SVM) framework. The local neighborhood constraints ensure that examples of the same class(More)
In this paper we study a bidding problem which can be modelled as a set packing problem. A simulated annealing heuristic with three local moves, including an embedded branch-and-bound move, is developed for the problem. We compared the heuristic with the CPLEX 8.0 solver and the current best non-exact method, Casanova, using the standard CATS benchmark and(More)
We propose an approach for automatically ranking structured documents applied to patent prior art search. Our model, SVM Patent Ranking (SVM P R) incorporates margin constraints that directly capture the specificities of patent citation ranking. Our approach combines patent domain knowledge features with meta-score features from several different general(More)
Crossdocking studies have mostly been concerned with the physical layout of a crossdock or on a single cross-dock. In this work, we study a network of crossdocks taking into consideration delivery and pickup time windows, warehouse capacities and inventory-handling costs. Because of the complexity of the problem, local search techniques are developed and(More)
In this paper, a combinatorial auction problem is modeled as a NP-complete set packing problem and a Lagrangian relaxation based heuristic algorithm is proposed. Extensive experiments are conducted using benchmark CATS test sets and more complex test sets. The algorithm provides optimal solutions for most test sets and is always 1%from the optimal solutions(More)
We study the problem of minimizing total flow time on a single machine with job release times. This problem is NP-complete for which is no constant ratio approximation algorithm. Our objective is to study experimentally how well, on average, the problem can be solved. The algorithm we use produces non-preemptive schedules converted from preemptive ones. We(More)
In this paper, we study the unrelated parallel machine problem for minimizing the makespan, which is NP-hard. We used Simulated Annealing (SA) and Tabu Search (TS) with Neighborhood Search (NS) based on the structure of the problem. We also used a modified SA algorithm, which gives better results than the traditional SA and developed an effective heuristic(More)