Aggregation effects in maximum covering models
Annals of Operations Research
Operations Research
A vehicle routing problem with stochastic demand
Operations Research
On worst-case aggregation analysis for network location problems
Annals of Operations Research - Special issue on locational decisions
Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
A p-center grid-positioning aggregation procedure
Computers and Operations Research - Special issue on aggregation and disaggregation in operations research
Solving the Homogeneous Probabilistic Traveling Salesman Problem by the ACO Metaheuristic
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
New ideas for applying ant colony optimization to the probabilistic TSP
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Runtime reduction techniques for the probabilistic traveling salesman problem with deadlines
Computers and Operations Research
Arc-Routing Models for Small-Package Local Routing
Transportation Science
Computers and Operations Research
A hybrid honey bees mating optimization algorithm for the probabilistic traveling salesman problem
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Estimation-based metaheuristics for the probabilistic traveling salesman problem
Computers and Operations Research
Hardness results for the probabilistic traveling salesman problem with deadlines
ISCO'12 Proceedings of the Second international conference on Combinatorial Optimization
Computers and Operations Research
Hi-index | 0.00 |
In the probabilistic traveling salesman problem (PTSP), customers require a visit with a given probability, and the best solution is the tour through all customers with the lowest expected final tour cost. The PTSP is an important problem, both operationally and strategically, but is quite difficult to solve with realistically sized problem instances. One alternative is to aggregate customers into regions and solve the PTSP on the reduced problem. This approach raises questions such as how to best divide customers into regions and what scale is necessary to represent the full objective. This paper addresses these questions and presents computational results from experiments with both uniformly distributed and clustered data sets. The focus is on large problem instances where customers have a low probability of requiring a visit and the CPU time available is quite limited. For this class of instances, aggregation can yield very tight estimates of the full objective very quickly, and solving an aggregated form of the problem first can often lead to full solutions with lower expected costs.