Motivated by the inbound logistics of a famous automobile manufacturing company, we introduce the upward scalable vehicle routing problem (for order pickup) with time windows (USVRPTW), where the pickup from each supp...
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Motivated by the inbound logistics of a famous automobile manufacturing company, we introduce the upward scalable vehicle routing problem (for order pickup) with time windows (USVRPTW), where the pickup from each supplier can be adjusted upward by a certain degree (pickup flexibility) based on the order volume, thus increasing the vehicle utilization and reducing logistics cost. We solve the USVRPTW exactly by a branch-and-price algorithm, where the flexibility affects the pricing problem, leading to the elementary shortest path problem having to consider resource allocation except the resource constraints. The consideration of resource allocation adds many new properties to the shortest path problem, based on which we design a tree search algorithm. We develop a heuristic algorithm based on the bipartite graph to generate initial columns for the column generation (CG) process. The algorithm can also be adopted as an efficient method for solving large-scale problems due to its ability to find near-optimal solutions quickly. We also propose the penalty stabilization method and the drill-down strategy to accelerate CG. Numerical experiments show that our designed branch-and-price algorithm outperforms the commercial solver Gurobi. The efficiency of the tree search algorithm, the heuristic algorithm, and the CG acceleration methods is also verified. Real-data experiments illustrate that the low increase in driving cost can significantly improve vehicle utilization, proving the significance of flexibility. We then provide management insights to reveal that adopting the proposed flexibility mechanism can reduce logistics cost.
This paper investigates the inland container transportation problem with a focus on multi-size containers, fuel consumption, and carbon emissions. To reflect a more realistic situation, the depot's initial invento...
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This paper investigates the inland container transportation problem with a focus on multi-size containers, fuel consumption, and carbon emissions. To reflect a more realistic situation, the depot's initial inventory of empty containers is also taken into consideration. To linearly model the constraints imposed by the multiple container sizes and the limited number of empty containers, a novel graphical representation is presented for the problem. Based on the graphical representation, a mixed-integer programming model is presented to minimize the total transportation cost, which includes fixed, fuel, and carbon emission costs. To efficiently solve the model, a tailored branch-and-price algorithm is designed, which is enhanced by improvement schemes including a heuristic label-setting algorithm, decremental state-space relaxation, and the introduction of a high-quality upper bound. Results from a series of computational experiments on randomly generated instances demonstrate that (1) the proposed branch-and-price algorithm demonstrates a superior performance compared to the tabu search algorithm and the genetic algorithm;(2) each additional empty container in the depot reduces the total transportation cost by less than 1%, with a diminishing marginal effect;(3) the rational configuration of different types of trucks improves scheduling flexibility and reduces fuel and carbon emission costs as well as the overall transportation cost;and (4) extending customer time windows also contributes to lower the total transportation cost. These findings not only deepen the theoretical understanding of inland container transportation optimization but also provide valuable insights for logistics companies and policymakers to improve efficiency and implement more sustainable operational practices. Additionally, our research paves the way for future investigations into the integration of dynamic factors and emerging technologies in this field.
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