On the use of learnheuristics in vehicle routing optimization problems with dynamic inputs
From MaRDI portal
Publication:1712070
DOI10.3390/a11120208zbMath1461.90008OpenAlexW2903789837MaRDI QIDQ1712070
Quim Arnau, Angel A. Juan, Isabel Serra
Publication date: 21 January 2019
Published in: Algorithms (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3390/a11120208
Transportation, logistics and supply chain management (90B06) Approximation methods and heuristics in mathematical programming (90C59)
Related Items
A biased-randomized discrete-event heuristic for coordinated multi-vehicle container transport across interconnected networks, A heuristic approach for a real-world electric vehicle routing problem, A strategic oscillation simheuristic for the time capacitated arc routing problem with stochastic demands, Combining variable neighborhood search and machine learning to solve the vehicle routing problem with crowd-shipping, A review of the role of heuristics in stochastic optimisation: from metaheuristics to learnheuristics, A heuristic algorithm for the routing and scheduling problem with time windows: a case study of the automotive industry in Mexico
Cites Work
- A review of recent research on green road freight transportation
- A multi-agent based cooperative approach to scheduling and routing
- A biased-randomised large neighbourhood search for the two-dimensional vehicle routing problem with backhauls
- Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs
- Vehicle routing under time-dependent travel times: the impact of congestion avoidance
- Vehicle dispatching with time-dependent travel times
- Synergies between operations research and data mining: the emerging use of multi-objective approaches
- A multi-stage algorithm for a capacitated vehicle routing problem with time constraints
- Vehicle routing with dynamic travel times: a queueing approach
- Hybrid adaptive predictive control for the multi-vehicle dynamic pick-up and delivery problem based on genetic algorithms and fuzzy clustering
- Combining biased randomization with iterated local search for solving the multidepot vehicle routing problem
- The ALGACEA‐1 method for the capacitated vehicle routing problem
- Using iterated local search for solving the flow‐shop problem: Parallelization, parametrization, and randomization issues
- A biased‐randomized algorithm for the two‐dimensional vehicle routing problem with and without item rotations
- Handbook of metaheuristics
- Combining metaheuristics with mathematical programming, constraint programming and machine learning
- The elements of statistical learning. Data mining, inference, and prediction
- Using biased randomization for solving the two-dimensional loading vehicle routing problem with heterogeneous fleet