Expert Systems with Applications: An International Journal
An integrated local search method for inventory and routing decisions
Expert Systems with Applications: An International Journal
Arc-guided evolutionary algorithm for the vehicle routing problem with time windows
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Intelligent Transportation Systems
Computers and Operations Research
Computers and Operations Research
An effective local search approach for the Vehicle Routing Problem with Backhauls
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Agent-based guided local search
Expert Systems with Applications: An International Journal
A heuristic solution method for node routing based solid waste collection problems
Journal of Heuristics
A memetic algorithm for the travelling salesperson problem with hotel selection
Computers and Operations Research
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We propose a three-step algorithmic framework for solving a new variant of the vehicle-routing problem (VRP) called the vehicle-routing problem with intermediate replenishment facilities (VRPIRF). The aim of this problem is to determine optimal routes for a fleet of vehicles that can renew their capacity at intermediate replenishment stations. Although this problem is often met in real-life scenarios of transportation logistics, it has not received much attention by researchers. Our proposed framework employs a combination of algorithmic blocks based on powerful metaheuristic methodologies designed to achieve a desirable intensification and diversification interplay. In the first step of the solution approach, the initial solution is obtained by a cost-saving construction heuristic. In the second step, the initial solution is improved by employing tabu search within the variable neighborhood search methodology. Finally, guided local search is applied in the third step, to eliminate low-quality features from the final solution produced. The proposed algorithmic framework was successfully applied to benchmark instances in the literature, generating several new best solutions. To motivate the proposed algorithmic choices and test the robustness of the algorithm, we also developed new classes of VRPIRF benchmark instances with diverse problem characteristics.