A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems

Article Computer Science, Interdisciplinary Applications

Adaptive large neighborhood search for vehicle routing problems with transshipment facilities arising in city logistics

Christian Friedrich et al.

Summary: This paper investigates vehicle routing problems with third-party transshipment facilities in the context of city logistics, proposing an adaptive large neighborhood search method to solve the problem, considering location-dependent time windows and heterogeneous fleets. The proposed method shows promising results on benchmark instances and conducts a real-world study on the impact of transshipment fees, order size, and heterogeneous fleets on transshipment decisions.

COMPUTERS & OPERATIONS RESEARCH (2022)

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An adaptive large neighborhood search heuristic for the vehicle routing problem with time windows and delivery robots

Cheng Chen et al.

Summary: This paper investigates the routing and scheduling of autonomous delivery robots in urban logistics, proposing an algorithm to solve the VRPTWDR and demonstrating its performance and effectiveness. The research shows that self-driving parcel delivery robots can be a new alternative for last mile service.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2021)

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Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search

Renata Turkes et al.

Summary: This paper promotes meta-analysis as a more suitable way to gain problem- and implementation-independent insights on metaheuristics. The research shows that adding an adaptive layer in adaptive large neighborhood search algorithms can improve objective function value slightly, but it also adds complexity and should therefore be recommended in specific situations only.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2021)

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