3. Producción
Browse
5 results
Search Results
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Electric vehicles fast charger location-routing problem under ambient temperature(Springer Science+Business Media, 2021-10-31)This study investigates how the location-routing decisions of the electric vehicle (EV) DC Fast Charging (DCFC) charging stations are impacted by the ambient temperature.Electric vehicles are expected to contribute significantly to the delivery mission of logistic companies in the future. In an EV delivery logistics network equipped with DCFC stations, this study investigates how the location strategy of DCFC charging stations and the routing plan of a fleet of EVs are impacted by the ambient temperature. We formulated this problem as a mixed-integer linear programming model that captures the realistic charging behavior of the DCFC’s in association with the ambient temperature and their subsequent impact on the EV charging station location and routing decisions. Two innovative heuristics are proposed to solve this challenging model in a realistic test setting, namely, the two-phase Tabu Search-modified Clarke and Wright algorithm and the Sweep-based Iterative Greedy Adaptive Large Neighborhood algorithm. We use Fargo city in North Dakota as a testbed to visualize and validate the algorithm performances. The results clearly indicate that the EV DCFC charging station location decisions are highly sensitive to the ambient temperature, the charging time, and the initial state-of-charge. The results provide numerous managerial insights for decision-makers to efficiently design and manage the DCFC EV logistic network for cities that suffer from high-temperature fluctuations. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improving adaptive large neighborhood search: an evaluation of parallel approaches with deep learning integration(Springer Science+Business Media, 2026-02-01)This paper introduces a hybrid optimization framework that enhances vehicle routing by integrating Parallel Adaptive Large Neighborhood Search (PALNS) with deep generative modeling. Our method uses Variational Autoencoders (VAEs) to extract latent representations of routing patterns, which dynamically guide neighborhood selection during the search. Unlike conventional heuristics that rely on handcrafted rules, our approach learns from historical solution data to balance exploration and exploitation. We conduct extensive computational experiments on benchmark CVRP instances and real routing data, showing significant improvements in solution quality and steeper convergence curves compared to standalone ALNS and other metaheuristics. While there is modest runtime overhead, the latency is consistent and within practical bounds. The results also highlight interpretability of latent features and their contribution to dynamic search behavior. This work bridges machine learning and large-scale combinatorial optimization, with practical implications for logistics and supply chain applications.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An integrated analytics approach to multi-project scheduling and material procurement with coordinated hub location(Elsevier B.V., 2026-03-01)This study presents an integrated framework combining multi-project scheduling, material procurement, and the hub location problem to simultaneously minimize project completion times and overall project and logistics costs. To address the challenge of allocating high-cost renewable resources, we incorporate rental options that balance the trade-off between additional rental expenses and potential project delays. A multi-objective optimization model is developed, integrating the scheduling of multiple projects with coordinated material procurement. To reduce logistics costs and improve delivery efficiency, consolidation hubs are introduced where materials from various suppliers are aggregated before being dispatched to project sites. The model considers the availability of renewable rental resources and storage space capacity while scheduling project activities. Due to the problem's computational complexity, two metaheuristic algorithms NSGA-II (Non-dominated Sorting Genetic Algorithm II) and MOSFS (Multi-objective Stochastic Fractal Search) are employed to obtain near-optimal solutions for large-scale scenarios. The proposed approach is validated through a real-world case study involving a bridge construction project and various benchmark instances of different sizes. Results indicate that while NSGA-II performs better on one performance metric, MOSFS consistently outperforms NSGA-II across most criteria, particularly in large-scale problems. The main contributions of this research include integrating project scheduling, material procurement, and hub location within a single unified framework. The model also incorporates renewable rental resources and realistic, type-specific storage capacity constraints that directly affect material flow and the initiation of project activities.1