A vehicle routing problem with stochastic demand
Operations Research
Computers and Operations Research - Neural networks in business
A multiple-depot, multiple-vehicle, location-routing problem with stochastically processed demands
Computers and Operations Research
The vehicle routing problem
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Tabu Search
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Rollout Algorithms for Combinatorial Optimization
Journal of Heuristics
Rollout Algorithms for Stochastic Scheduling Problems
Journal of Heuristics
Branch, Cut, and Price: Sequential and Parallel
Computational Combinatorial Optimization, Optimal or Provably Near-Optimal Solutions [based on a Spring School]
Stochastic Vehicle Routing Problem with Restocking
Transportation Science
A Rollout Policy for the Vehicle Routing Problem with Stochastic Demands
Operations Research
Linear Time Dynamic-Programming Algorithms for New Classes of Restricted TSPs: A Computational Study
INFORMS Journal on Computing
Parallel branch and cut for capacitated vehicle routing
Parallel Computing - Special issue: Parallel computing in logistics
Price-Directed Replenishment of Subsets: Methodology and Its Application to Inventory Routing
Manufacturing & Service Operations Management
Dynamic Programming Approximations for a Stochastic Inventory Routing Problem
Transportation Science
A Price-Directed Approach to Stochastic Inventory/Routing
Operations Research
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands
Computers and Operations Research
Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports
Computers and Operations Research
The capacitated vehicle routing problem with stochastic demands and time windows
Computers and Operations Research
Research on vehicle routing problem with stochastic demand based on multi-objective method
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Heuristics for dynamic and stochastic routing in industrial shipping
Computers and Operations Research
An event-driven optimization framework for dynamic vehicle routing
Decision Support Systems
Hi-index | 0.00 |
We consider the vehicle-routing problem with stochastic demands (VRPSD) under reoptimization. We develop and analyze a finite-horizon Markov decision process (MDP) formulation for the single-vehicle case and establish a partial characterization of the optimal policy. We also propose a heuristic solution methodology for our MDP, named partial reoptimization, based on the idea of restricting attention to a subset of all the possible states and computing an optimal policy on this restricted set of states. We discuss two families of computationally efficient partial reoptimization heuristics and illustrate their performance on a set of instances with up to and including 100 customers. Comparisons with an existing heuristic from the literature and a lower bound computed with complete knowledge of customer demands show that our best partial reoptimization heuristics outperform this heuristic and are on average no more than 10%--13% away from this lower bound, depending on the type of instances.