MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching
Transportation Science
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Computers and Operations Research
A dynamic vehicle routing problem with time-dependent travel times
Computers and Operations Research
Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows
Applied Intelligence
A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows
Computational Optimization and Applications
The A Priori Dynamic Traveling Salesman Problem with Time Windows
Transportation Science
Dynamic Column Generation for Dynamic Vehicle Routing with Time Windows
Transportation Science
A Hybrid Multi-objective Algorithm for Dynamic Vehicle Routing Problems
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
Vehicle routing problem with fuzzy time windows
Fuzzy Sets and Systems
A comparison of five heuristics for the multiple depot vehicle scheduling problem
Journal of Scheduling
Hybrid Adaptive Predictive Control for a Dynamic Pickup and Delivery Problem
Transportation Science
Performance Measures for Dynamic Multi-Objective Optimization
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Survey: The vehicle routing problem: A taxonomic review
Computers and Industrial Engineering
The open vehicle routing problem with fuzzy demands
Expert Systems with Applications: An International Journal
Vehicle routing and scheduling with dynamic travel times
Computers and Operations Research
Fuzzy measure on vehicle routing problem of hospital materials
Expert Systems with Applications: An International Journal
An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows
Computers and Operations Research
Online vehicle routing and scheduling with dynamic travel times
Computers and Operations Research
A hybrid GA-TS algorithm for open vehicle routing optimization of coal mines material
Expert Systems with Applications: An International Journal
The capacitated vehicle routing problem with stochastic demands and time windows
Computers and Operations Research
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
An event-driven optimization framework for dynamic vehicle routing
Decision Support Systems
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In this paper, a multi-objective dynamic vehicle routing problem with fuzzy time windows (DVRPFTW) is presented. In this problem, unlike most of the work where all the data are known in advance, a set of real time requests arrives randomly over time and the dispatcher does not have any deterministic or probabilistic information on the location and size of them until they arrive. Moreover, this model involves routing vehicles according to customer-specific time windows, which are highly relevant to the customers' satisfaction level. This preference information of customers can be represented as a convex fuzzy number with respect to the satisfaction for a service time. This paper uses a direct interpretation of the DVRPFTW as a multi-objective problem where the total required fleet size, overall total traveling distance and waiting time imposed on vehicles are minimized and the overall customers' preferences for service is maximized. A solving strategy based on the genetic algorithm (GA) and three basic modules are proposed, in which the state of the system including information of vehicles and customers is checked in a management module each time. The strategy module tries to organize the information reported by the management module and construct an efficient structure for solving in the subsequent module. The performance of the proposed approach is evaluated in different steps on various test problems generalized from a set of static instances in the literature. In the first step, the performance of the proposed approach is checked in static conditions and then the other assumptions and developments are added gradually and changes are examined. The computational experiments on data sets illustrate the efficiency and effectiveness of the proposed approach.