The classic problem of finding the shortest path over a network has been the target of many research efforts over the years. These research efforts have resulted in a number of different algorithms and a considerable amount of empirical findings with respect to performance. Unfortunately, prior research does not provide a clear direction for choosing an algorithm when one faces the problem of computing shortest paths on real road networks. Most of the computational testing on shortest path algorithms has been based on randomly generated networks, which may not have the characteristics of real road networks. In this paper, we provide an objective evaluation of 15 shortest path algorithms using a variety of real road networks. Based on the evaluation, a set of recommended algorithms for computing shortest paths on real road networks is identified. This evaluation should be particularly useful to researchers and practitioners in operations research, management science, transportation, and Geographic Information Systems.
SThis paper considers the rescheduling of operations with release dates and multiple resources when disruptions prevent the use of a preplanned schedule. The overall strategy is to follow the preschedule until a disruption occurs. After a disruption, part of the schedule is reconstructed to matchup with the preschedule at some future time. Conditions are given for the optimality of this approach. A practical implementation is compared with the alternatives of preplanned static scheduling and myopic dynamic scheduling. A set of practical test problems demonstrates the advantages of the matchup approach. We also explore the solution of the matchup scheduling problem and show the advantages of an integer programming approach for allocating resources to jobs.
This paper presents an optimal approach for the asymmetric Generalized Traveling Salesman Problem (GTSP). The GTSP is defined on a directed graph in which the nodes are grouped into m predefined, mutually exclusive and exhaustive sets with the arc set containing no intraset arcs. The problem is to find a minimum cost m-arc directed cycle which includes exactly one node from each set. Our approach employs a Lagrangian relaxation to compute a lower bound on the total cost of an optimal solution. The lower bound and a heuristically determined upper bound are used to identify and remove arcs and nodes which are guaranteed not to be in an optimal solution. Finally, we use an efficient branch-and-bound procedure which exploits the multiple choice structure of the node sets. We present computational results for the optimal approach tested on a series of randomly generated problems. The results show success on a range of problems with up to 104 nodes.
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