In this paper we introduce the regenerator location problem (RLP), which deals with a constraint on the geographical extent of transmission in optical networks. Specifically, an optical signal can only travel a maximum distance of d max before its quality deteriorates to the point that it must be regenerated by installing regenerators at nodes of the network. As the cost of a regenerator is high we wish to deploy as few regenerators as possible in the network, while ensuring all nodes can communicate with each other. We show that the RLP is NP-Complete. We then devise three heuristics for the RLP. We show how to represent the RLP as a max leaf spanning tree problem (MLSTP) on a transformed graph. Using this fact we model the RLP as a Steiner arborescence problem (SAP) with a unit degree constraint on the root node. We also devise a branch-and-cut procedure to the directed cut formulation for the SAP problem. In our computational results over 740 test instances, the heuristic procedures obtained the optimal solution in 454 instances, while the branch-and-cut procedure obtained the optimal solution in 536 instances. These results indicate the quality of the heuristic solutions are quite good, and the branch-and-cut approach is viable for the optimal solution of problems with up to 100 nodes. Our approaches are also directly applicable to the MLSTP indicating that both the heuristics and branch-and-cut approach are viable options for the MLSTP.

M otivated by the increasing use of auctions by government agencies, we consider the problem of fairly pricing public goods in a combinatorial auction. A well-known problem with the incentive-compatible Vickrey-Clarke-Groves (VCG) auction mechanism is that the resulting prices may not be in the core. Loosely speaking, this means the payments of the winners could be so low, that there are bidders who would have been willing to pay more than the payments of the winning bidders. Clearly, this "unfair" outcome is unacceptable for a public sector auction. Recent advances in auction theory suggest that combinatorial auctions resulting in efficient outcomes and bidder-Pareto-optimal core payments offer a viable practical alternative to address this problem.This paper confronts two critical issues facing the bidder-Pareto-optimal core payment. First, motivated to minimize a bidder's ability to benefit through strategic manipulation (through collusive agreement or unilateral action), we demonstrate the strength of a mechanism that minimizes total payments among all such auction outcomes, narrowing the previously broad solution concept. Second, we address the computational difficulties of achieving these outcomes with a constraint-generation approach, promising to broaden the range of applications for which bidder-Pareto-optimal core pricing achieves a comfortably rapid solution.

ISR develops, applies and teaches advanced methodologies of design and analysis to AbstractThe network design problem with connectivity requirements (NDC) models a wide variety of celebrated combinatorial optimization problems including the minimum spanning tree, Steiner tree, and survivable network design problems. We develop strong formulations for two versions of the edge-connectivity NDC problem: unitary problems requiring connected network designs, and nonunitary problems permitting non-connected networks as solutions. We (i) present a new directed formulation for the unitary NDC problem that is stronger than a natural undirected formulation, (ii) project out several classes of valid inequalities-partition inequalities, odd-hole inequalities, and combinatorial design inequalities-that generalize known classes of valid inequalities for the Steiner tree problem to the unitary NDC problem, and (iii) show how to strengthen and direct nonunitary problems.Our results provide a unifying framework for strengthening formulations for NDC problems, and demonstrate the strength and power of flow-based formulations for network design problems with connectivity requirements.

T he generalized minimum spanning tree (GMST) problem occurs in telecommunications network planning, where a network of node clusters needs to be connected via a tree architecture using exactly one node per cluster. The problem is known to be NP-hard, and even finding a constant factor approximation algorithm is NP-hard. In this paper, we present two heuristic search approaches for the GMST problem: local search and a genetic algorithm. Our computational experiments show that these heuristics rapidly provide high-quality solutions for the GMST and outperform some previously suggested heuristics for the problem. In our computational tests on 211 test problems (including 169 problems from the TSPLIB set), our local-search heuristic found the optimal solution in 179 instances and our genetic-algorithm procedure found the optimal solution in 185 instances (out of the 211 instances, the optimal solution is known in 187 instances). Further, on each of the 19 unsolved instances from TSPLIB, both our local-search heuristic and genetic-algorithm procedure improved upon the best previously known solution.

Abstract:Given a graph ( , ) G N E  , the Covering Salesman Problem (CSP) is to identify the minimum length tour "covering" all the nodes. More specifically, it seeks the minimum length tour visiting a subset of the nodes in N such that each node i not on the tour is within a predetermined distance d i of a node on the tour. In this paper, we define and develop a generalized version of the CSP, and refer to it as the Generalized Covering Salesman Problem (GCSP). Here, each node i needs to be covered at least i k times and there is a cost associated with visiting each node. We seek a minimum cost tour such that each node i is covered at least i k times by the tour. We define three variants of the GCSP. In the first case, each node can be visited by the tour at most once. In the second version, visiting a node i more than once is possible, but an overnight stay is not allowed (i.e., to revisit a node i, the tour has to visit another node before it can return to i). Finally, in the third variant, the tour can visit each node more than once consecutively. In this paper, we develop two local search heuristics to find high-quality solutions to the three GCSP variants. In order to test the proposed algorithms, we generated datasets based on TSP Library instances. Since the CSP and the Generalized Traveling Salesman Problem are special cases of the GCSP, we tested our heuristics on both of these two problems as well. Overall, the results show that our proposed heuristics find highquality solutions very rapidly.