A machine learning optimization approach for last-mile delivery and third-party logistics

DOI10.1016/j.cor.2023.106262OpenAlexW4367316720MaRDI QIDQ6106567

Edoardo Fadda, Stanislav Fedorov, Guido Perboli, Maria Elena Bruni

Publication date: 3 July 2023

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cor.2023.106262

zbMATH Keywords

metaheuristicsthird-party logisticsmachine learningcapacity planninglast-mile deliveryvariable cost and size bin packing

Mathematics Subject Classification ID

Operations research and management science (90Bxx)

Cites Work

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• Logistics capacity planning: a stochastic bin packing formulation and a progressive hedging meta-heuristic
• Scenario grouping in a progressive hedging-based meta-heuristic for stochastic network design
• A progressive hedging method for the optimization of social engagement and opportunistic IoT problems
• Analyzing the quality of the expected value solution in stochastic programming
• The polynomial robust knapsack problem
• A learning-based algorithm to quickly compute good primal solutions for stochastic integer programs
• On learning and branching: a survey
• Solving the variable size bin packing problem with discretized formulations
• Scenarios and Policy Aggregation in Optimization Under Uncertainty
• Introduction to Stochastic Programming
• A progressive hedging method for the multi-path travelling salesman problem with stochastic travel times
• A Multistage Stochastic Programming Approach to the Dynamic and Stochastic VRPTW

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