QMOEA: a Q-learning-based multiobjective evolutionary algorithm for solving time-dependent green vehicle routing problems with time windows
From MaRDI portal
Publication:6195430
DOI10.1016/j.ins.2022.06.056MaRDI QIDQ6195430
No author found.
Publication date: 13 March 2024
Published in: Information Sciences (Search for Journal in Brave)
multiobjective optimizationtime dependentvehicle routing problem with time windowsQ-learningcustomer satisfaction
Cites Work
- Vehicle dispatching with time-dependent travel times
- A hybrid algorithm for time-dependent vehicle routing problem with time windows
- Tabu search for the time-dependent vehicle routing problem with time windows on a road network
- Multi-trip time-dependent vehicle routing problem with time windows
- Modelling and multi-criteria analysis of the sustainability dimensions for the green vehicle routing problem
- A novel multi-objective evolutionary algorithm with fuzzy logic based adaptive selection of operators: FAME
- A hybrid ant colony optimization algorithm for a multi-objective vehicle routing problem with flexible time windows
- An adaptive large neighborhood search heuristic for the vehicle routing problem with time windows and delivery robots
- A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem
- Time-dependent multi-depot green vehicle routing problem with time windows considering temporal-spatial distance
- The secure time-dependent vehicle routing problem with uncertain demands
- Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints
This page was built for publication: QMOEA: a Q-learning-based multiobjective evolutionary algorithm for solving time-dependent green vehicle routing problems with time windows