Operations Research
A note on the effect of neighborhood structure in simulated annealing
Computers and Operations Research
A vehicle routing problem with stochastic demand
Operations Research
The vehicle scheduling problem with intermittent customer demands
Computers and Operations Research
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Drive: Dynamic Routing of Independent Vehicles
Operations Research
Stochastic Vehicle Routing Problem with Restocking
Transportation Science
Local search for the probabilistic traveling salesman
Local search for the probabilistic traveling salesman
Model Problems for the Multigrid Optimization of Systems Governed by Differential Equations
SIAM Journal on Scientific Computing
Aggregation for the probabilistic traveling salesman problem
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
A Compressed-Annealing Heuristic for the Traveling Salesman Problem with Time Windows
INFORMS Journal on Computing
Territory Planning and Vehicle Dispatching with Driver Learning
Transportation Science
Decision Support for Consumer Direct Grocery Initiatives
Transportation Science
Probabilistic Traveling Salesman Problem with Deadlines
Transportation Science
An integer L-shaped algorithm for the Dial-a-Ride Problem with stochastic customer delays
Discrete Applied Mathematics
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Hardness results for the probabilistic traveling salesman problem with deadlines
ISCO'12 Proceedings of the Second international conference on Combinatorial Optimization
Journal of Parallel and Distributed Computing
Computers and Operations Research
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The probabilistic traveling salesman problem with deadlines (PTSPD) is an extension of the well-known probabilistic traveling salesman problem in which, in addition to stochastic presence, customers must also be visited before a known deadline. For realistically sized instances, the problem is impossible to solve exactly, and local-search methods struggle due to the time required to evaluate the objective function. Because computing the deadline violations is the most time consuming part of the objective, we focus on developing approximations for the computation of deadline violations. These approximations can be imbedded in a variety of local-search methods, and we perform experiments comparing their performance using a 1-shift neighborhood. These computational experiments show that the approximation methods lead to significant runtime improvements without loss in quality.