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Machine Learning for Data-Driven Last-Mile Delivery Optimization
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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The Elements of Statistical Learning
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Local search in routing problems with time windows
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A similarity-based mechanism to control genetic algorithm and local search hybridization to solve traveling salesman problem
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Metaheuristics in Combinatorial Optimization
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Vehicle routing problems for city logistics
inferredcites →https://doi.org/10.1007/springerreference_6269openalex-snapshot
Traveling salesman problem
inferredcites →https://doi.org/10.1016/j.artint.2016.04.003openalex-snapshot
ASlib: A benchmark library for algorithm selection
inferredcites →https://doi.org/10.1016/j.cor.2019.104781openalex-snapshot
Deep learning assisted heuristic tree search for the container pre-marshalling problem
inferredcites →https://doi.org/10.1016/j.cstp.2015.11.005openalex-snapshot
Freight data collection using GPS and web-based surveys: Insights from US truck drivers’ survey and perspectives for urban freight
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Machine learning for combinatorial optimization: A methodological tour d’horizon
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An efficient recommendation generation using relevant Jaccard similarity
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The delivery problem: Optimizing hit rates in e-commerce deliveries
inferredcites →https://doi.org/10.1016/j.trb.2019.03.018openalex-snapshot
A latent-class adaptive routing choice model in stochastic time-dependent networks
inferredcites →https://doi.org/10.1016/j.tre.2021.102263openalex-snapshot
Optimizing e-commerce last-mile vehicle routing and scheduling under uncertain customer presence
inferredcites →https://doi.org/10.1016/j.trf.2021.05.021openalex-snapshot
Hybrid route choice model incorporating latent cognitive effects of real-time travel information using physiological data
inferredcites →https://doi.org/10.1016/j.trpro.2016.02.006openalex-snapshot
Improving Home Delivery Efficiency by Using Principles of Address Intelligence for B2C Deliveries
inferredcites →https://doi.org/10.1016/j.trpro.2017.05.151openalex-snapshot
Route choice modeling with Support Vector Machine
inferredcites →https://doi.org/10.1016/s0167-9473(01)00065-2openalex-snapshot
Stochastic gradient boosting
inferredcites →https://doi.org/10.1016/s0377-2217(99)00468-3openalex-snapshot
Construction heuristics for the asymmetric TSP
inferredcites →https://doi.org/10.1109/tnnls.2021.3068828openalex-snapshot
Learning Improvement Heuristics for Solving Routing Problems
inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02080.xopenalex-snapshot
Regression Shrinkage and Selection Via the Lasso
inferredcites →https://doi.org/10.1137/0206041openalex-snapshot
An Analysis of Several Heuristics for the Traveling Salesman Problem
inferredcites →https://doi.org/10.1287/mnsc.2017.2992openalex-snapshot
Inverse Optimization: Closed-Form Solutions, Geometry, and Goodness of Fit
inferredcites →https://doi.org/10.1287/msom.1080.0243openalex-snapshot
The Consistent Vehicle Routing Problem
inferredcites →https://doi.org/10.1287/opre.2017.1705openalex-snapshot
Inverse Optimization with Noisy Data
inferredcites →https://doi.org/10.1287/opre.46.3.330openalex-snapshot
A Generalized Insertion Heuristic for the Traveling Salesman Problem with Time Windows
inferredcites →https://doi.org/10.1287/trsc.1030.0046openalex-snapshot
Efficient Insertion Heuristics for Vehicle Routing and Scheduling Problems
inferredcites →https://doi.org/10.1287/trsc.2014.0529openalex-snapshot
The Generalized Consistent Vehicle Routing Problem
inferredcites →https://doi.org/10.1287/trsc.2016.0675openalex-snapshot
50th Anniversary Invited Article—City Logistics: Challenges and Opportunities
inferredcites →https://doi.org/10.1287/trsc.2022.1182openalex-snapshot
The Vehicle Routing Problem with Availability Profiles
inferredcites →https://doi.org/10.21236/ad0256582openalex-snapshot
PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS
inferredcites →https://doi.org/10.24963/ijcai.2018/772openalex-snapshot
Boosting Combinatorial Problem Modeling with Machine Learning
inferredcites →https://doi.org/10.3141/2354-12openalex-snapshot
Route Choice Characteristics for Truckers
inferredcites →https://doi.org/10.3233/faia200124openalex-snapshot
Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem
inferredcites →https://doi.org/10.48550/arxiv.1611.09940openalex-snapshot
Neural Combinatorial Optimization with Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.2004.01608openalex-snapshot
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Generating Practical Last-mile Delivery Routes using a Data-informed Insertion Heuristic
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ENDeliver: An Energy-Aware Framework of Large Spatio-Temporal Model for E-Bike Delivery Route Planning
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Toward sustainable and customer-centric reverse logistics: Machine learning-enhanced multi-objective optimization
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Two-echelon stochastic vehicle routing problem with delivery options
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Harnessing machine learning for supply chain management: A systematic review and future research framework
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Using machine learning to identify hidden constraints in vehicle routing problems
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Data describing planned routes and driven routes from a last-mile delivery vehicle routing problem
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Implications of drone delivery: Strategic interaction and competitive equilibrium
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inferred← citeshttps://doi.org/10.1016/j.sftr.2026.102065openalex-snapshot
Prioritizing enablers for sustainable last-mile delivery: A grey-OPA approach for supply chain diligence in logistics 5.0
inferred← citeshttps://doi.org/10.1016/j.trb.2025.103172openalex-snapshot
A contextual framework for learning routing experiences in last-mile delivery
inferred← citeshttps://doi.org/10.1016/j.trc.2025.105278openalex-snapshot
Generating practical last-mile delivery routes using a data-informed insertion heuristic
inferred← citeshttps://doi.org/10.1016/j.trd.2025.105176openalex-snapshot
Decarbonizing last-mile delivery: A life cycle comparison of low-altitude and traditional logistics
inferred← citeshttps://doi.org/10.1057/s41599-026-07096-0openalex-snapshot
Unpacking societal barriers to technology adoption: a multi-theoretical analysis of user behavior in last mile delivery applications in urban developing contexts
inferred← citeshttps://doi.org/10.1080/00207543.2025.2466062openalex-snapshot
Machine learning in supply chain management: systematic literature review and future research agenda
inferred← citeshttps://doi.org/10.1080/15472450.2025.2543827openalex-snapshot
Optimal design and performance evaluation of last-mile hyper-local network problem with uncertain demand
inferred← citeshttps://doi.org/10.1080/23302674.2025.2526675openalex-snapshot
Orthogonal criminal search optimisation algorithm with time-varying inertia weight for last-mile hyper-local network problem with uncertain demand
inferred← citeshttps://doi.org/10.1108/imds-09-2024-0874openalex-snapshot
Tacit knowledge-informed approximate dynamic programming for last-mile delivery operations in online-to-offline pharmacies
inferred← citeshttps://doi.org/10.1109/access.2025.3570894openalex-snapshot
Edge Intelligence in Enhancing Last-Mile Delivery Logistics
inferred← citeshttps://doi.org/10.1109/iclie61478.2024.11015423openalex-snapshot
Optimizing Resource Allocation and Routing in FMCG Logistics: A clustering and Simulated Annealing Approach
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A Digital Twin Approach for Last-Mile Delivery
inferred← citeshttps://doi.org/10.1109/tce.2025.3577902openalex-snapshot
Double Deep Q-Learning Network for Package Pickup and Delivery Route Prediction
inferred← citeshttps://doi.org/10.1109/tevc.2025.3562243openalex-snapshot
Enhancing Genetic Algorithm With Explainable Artificial Intelligence for Last-Mile Routing
inferred← citeshttps://doi.org/10.1109/wconf61366.2024.10692008openalex-snapshot
Optimizing Last Mile Logistics with Machine Learning and Drone Technology in Aerial Solutions
inferred← citeshttps://doi.org/10.1145/3779475.3779505openalex-snapshot
Big data-driven logistics node location selection and network optimization model
inferred← citeshttps://doi.org/10.1186/s12544-026-00795-4openalex-snapshot
A decision tree-based algorithm for the Amazon last-mile routing research challenge
inferred← citeshttps://doi.org/10.1287/mnsc.2024.05530openalex-snapshot
Service-Oriented Considerate Routing: Data, Predictions, and Robust Decisions
inferred← citeshttps://doi.org/10.1287/trsc.2023.intro.v58.n1openalex-snapshot
Introduction to the Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems
inferred← citeshttps://doi.org/10.1287/trsc.2025.0149openalex-snapshot
Dispatch or Hold? An Inverse Optimization and Reinforcement Learning Approach for Multiobjective On-Demand Delivery
inferred← citeshttps://doi.org/10.2139/ssrn.4639706openalex-snapshot
Learning for Guiding: A Pairwise Inverse Reinforcement Learning Framework for Last-Mile Deliveries
inferred← citeshttps://doi.org/10.2139/ssrn.4830661openalex-snapshot
Generating Practical Last-mile Delivery Routes using a Data-informed Insertion Heuristic
inferred← citeshttps://doi.org/10.2139/ssrn.4929084openalex-snapshot
Overworking or Outsourcing: A Decision-Focused Learning Framework for Human-Centric Order Assignment in Last-Mile Delivery
inferred← citeshttps://doi.org/10.2139/ssrn.5054050openalex-snapshot
Overworking or Outsourcing: Integrated Prediction-Optimization for Human-Centric Order Assignment in Last-Mile Delivery
inferred← citeshttps://doi.org/10.2139/ssrn.5110506openalex-snapshot
Learning-to-Optimize for Consolidation and Transshipment in Multi-store Order Delivery
inferred← citeshttps://doi.org/10.3390/app16178861openalex-snapshot
ETA Prediction in Last-Mile Logistics Under Domain Shift: From Zero-Shot Failure to Few-Shot Recovery
inferred← citeshttps://doi.org/10.3390/su18083877openalex-snapshot
Toward Sustainable Smart Last-Mile Logistics: A Machine Learning-Enabled Framework for Adaptive Control and Dynamic Prediction
inferred← citeshttps://doi.org/10.3390/su18126282openalex-snapshot
Sustainable Dynamic Route Optimization for Pharmaceutical Cold-Chain Distribution by Integrating Reinforcement Learning and Improved Neighborhood Search
inferred← citeshttps://doi.org/10.37082/ijirmps.v13.i2.232405openalex-snapshot
Intelligent Automation Revolutionizing Retail Operations by Optimizing Inventory Management, Logistics, and Customer Service with AI-Powered Robotics and Machine Learning
inferred← citeshttps://doi.org/10.4018/979-8-2600-4340-0.ch005openalex-snapshot
Sustainable Packaging and Reverse Logistics in Food Supply Chain
inferred← citeshttps://doi.org/10.58830/ozgur.pub1488.c5940openalex-snapshot
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