A stochastic programming approach to scheduling in TAC SCM
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Dynamic Column Generation for Dynamic Vehicle Routing with Time Windows
Transportation Science
Incentive Schemes for Attended Home Delivery Services
Transportation Science
New policies for the dynamic traveling salesman problem
Optimization Methods & Software
Co-operative Co-evolutionary System for Solving Dynamic VRPTW Problems with Crisis Situations
HoloMAS '07 Proceedings of the 3rd international conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing
Adaptive granular local search heuristic for a dynamic vehicle routing problem
Computers and Operations Research
The degree of dynamism for workforce scheduling problem with stochastic task duration
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Regrets only! online stochastic optimization under time constraints
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Waiting and relocation strategies in online stochastic vehicle routing
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Dynamic vehicle routing with stochastic requests
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Gap reduction techniques for online stochastic project scheduling
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Amsaa: a multistep anticipatory algorithm for online stochastic combinatorial optimization
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
On the partitioning of dynamic workforce scheduling problems
Journal of Scheduling
Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports
Computers and Operations Research
Look-ahead heuristics for the dynamic traveling purchaser problem
Computers and Operations Research
An improved LNS algorithm for real-time vehicle routing problem with time windows
Computers and Operations Research
A satellite navigation system to improve the management of intermodal drayage
Advanced Engineering Informatics
Time Slot Management in Attended Home Delivery
Transportation Science
Optimization of earth recycling and dump truck dispatching
Computers and Industrial Engineering
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Online stochastic and robust optimization
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
Online stochastic reservation systems
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
A model with a solution algorithm for the cash transportation vehicle routing and scheduling problem
Computers and Industrial Engineering
Heuristics for dynamic and stochastic routing in industrial shipping
Computers and Operations Research
Self-organizing maps in population based metaheuristic to the dynamic vehicle routing problem
Journal of Combinatorial Optimization
An event-driven optimization framework for dynamic vehicle routing
Decision Support Systems
Particle Swarm Optimization for the Vehicle Routing Problem with Stochastic Demands
Applied Soft Computing
| Hi-index | 0.00 |
The multiple vehicle routing problem with time windows (VRPTW) is a hard and extensively studied combinatorial optimization problem. This paper considers a dynamic VRPTW with stochastic customers, where the goal is to maximize the number of serviced customers. It presents a multiple scenario approach (MSA) that continuously generates routing plans for scenarios including known and future requests. Decisions during execution use a distinguished plan chosen, at each decision, by a consensus function. The approach was evaluated on vehicle routing problems adapted from the Solomon benchmarks with a degree of dynamism varying between 30% and 80%. They indicate that MSA exhibits dramatic improvements over approaches not exploiting stochastic information, that the use of consensus function improves the quality of the solutions significantly, and that the benefits of MSA increase with the (effective) degree of dynamism.