Two main methods are tested: hard/soft constraint, with three soft constraint submethods: set/decaying penalty value and Lagrangian relaxation, totalling four POMO variants, proving the hard constraint yields the strongest and most robust performance without the need for hyperparameter optimization.
An NCO solution method for a time-constrained capacitated VRP with finite vehicle fleet is presented, providing competitive cost-efficient solutions while exhibiting robustness and generalization, compared with state-of-the-art heuristics.
Elija Deineko, Carina Kehrt· Transportation Research Reco...· 0 citations
The scalability of a multi-criteria optimization for the Service Team Transport Scheduling (STTS) problem is investigated, minimizing total travel time, maximum vehicle worktime, and total vehicle engagement time to define scale-aware algorithmic boundaries essential for real-time decision support systems.
Jarosław Rudy, G. Radzki· IEEE Access· 0 citations
This work introduces an optimization framework where a reinforcement learning agent is trained on prior instances and quickly generates initial solutions, which are then further optimized by a genetic algorithm, enabling real-time and interactive routing at scale.
Ido Greenberg, P. Sielski, Hugo Linsenmaier et al.· Communications AI & Computin...· 1 citation
The vehicle routing problem (VRP) is a fundamental combinatorial optimization problem with wide-ranging applications in networks, logistics, transportation, and distribution systems. At its core, the VRP seeks to design a set of vehicle routes that serve a collection of customers from one or more depots while minimizin...
Jing-Yang Zhao, Uman Khalid, Saw Nang Paing et al.· IEEE Communications Surveys...· 0 citations
Multi-objective optimization (MOO) problems are common in logistics, where routing decisions must balance conflicting objectives such as travel distance, delivery time, and operational risk. A recently proposed Quantum Approximate Optimization Algorithm (QAOA) parameter-transfer strategy solves multi-objective MAX-CUT...
E. Lussi, Alisson dos Passos Fumaco, Marcos Vinicius Reballo et al.· 0 citations
It is shown that the JAMPR+/L2D model, proposed in to solve large CPDPTW problems can be adopted in the case of substantial changes of graph distance matrix, and generalizes well for tasks with simpler constraints (CVRP, VRPTW), for different problem sizes and for moderate changes in distance matrixes.
A. Soroka, A. Meshcheryakov· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
