This study presents a data-to-decision workflow that combines surrogate modeling, multi-objective optimization, and decision-making in a single procedure. Four surrogate models, including Kolmogorov-Arnold Networks (KAN), Cat-Boost (CAT), Gradient Boosting Regressor (GBR), and LightGBM (LGB), were trained under a unified preprocessing and Bayesian tuning scheme and evaluated on held-out data. The retained models were then embedded in NSGA-III to generate the Pareto front for the trade-off between surface roughness (Ra) and material removal rate (MRR). To move from the Pareto set to a single operating condition, the candidate solutions were further assessed using multiple MCDA methods under different objective weighting schemes, and the resulting rankings were combined through rank aggregation (Borda, Copeland, Kemeny-Young, Robust Rank Aggregation). A turning case study on C3604 free-cutting brass, using cutting speed, feed, depth of cut, nose radius, and coolant condition as inputs, showed that the final recommendation remained stable across different weighting and ranking settings. Experimental verification at selected Pareto points agreed well with the predicted values, with relative errors of about 5 % for both Ra and MRR. The results show that the proposed workflow provides a practical and consistent route from limited machining data to a final operating decision.
Intelligent siting of electric vehicle (EV) fast charging stations is of great significance for the development of urban transportation. This study comprehensively considers factors related to the placement of EV fast charging stations, including differences in travel behavior between fuel vehicle travelers and EV travelers, road congestion, vehicle energy consumption, and the geographical location of charging stations. Based on these factors, an advanced bilevel programming model integrating dynamic traffic assignment (DUE) and charging station location selection is developed. Specifically, the upper level uses a genetic algorithm (GA) to solve a system optimization model aimed at minimizing total travel time while accounting for the construction and operation costs of charging stations. Corresponding to the upper-level decisions, the lower level captures the joint selection behaviors of fuel and electric vehicles and is solved by a DUE procedure combined with the method of successive averages (MSA). This lower-level model incorporates travelers’ on-route fast-charging behaviors, including departure time choice, charging facility preference, and route selection. The integrated approach provides a sophisticated framework for analyzing and optimizing the interaction between charging station placement, travel time, and associated costs. An iterative dynamic traffic flow algorithm integrating EV charging queue simulation is proposed. Finally, numerical studies conducted on an illustrative network derive optimal location schemes at different EV adoption stages and analyze the complex operational characteristics of the traffic network under the coexistence of EVs and fuel vehicles.
This study investigates the collaborative optimization of routing and charging in a distribution network comprising electric trucks (ETs) and autonomous unmanned vehicles (AUVs), supported by mobile photovoltaic (PV) storage charging. A comprehensive optimization model is developed to minimize the total daily operating cost of the logistics enterprise, encompassing vehicle acquisition, staffing, energy consumption, charging, and penalties. The model simultaneously determines coordinated ET-AUV delivery routes, mobile charging schedules, and parking node locations. The PV-storage system is incorporated as a green power-supply constraint, directly influencing charging costs. To address the model's complexity, an enhanced hybrid frog-leaping algorithm is proposed. This algorithm incorporates an initial solution construction method to improve population quality, an advanced local deep search to increase search efficiency, and diversity control strategies with clone selection programs to maintain population diversity. The effectiveness of the developed algorithm is validated through multiple case studies with varying customer sizes. Computational experiments on instances with up to 60 customers indicate that, compared with the ET-only mode, the proposed ET-AUV collaborative mode reduces total daily operating costs by 18.61 %, decreases staffing costs by 62.5 %, and lowers penalty costs by 23.21 %, thereby enhancing customer satisfaction and operational resilience. Sensitivity analysis shows that system efficiency depends on several operational parameters. Increases in ET payload and range are the main drivers of cost reduction. Additionally, the average speeds of both vehicle types have a critical U-shaped effect on total costs.
In metropolitan instant delivery systems, rising operational costs and poor delivery timeliness pose significant challenges. This study addresses these issues by investigating optimal order allocation and route optimization in a hybrid delivery system that integrates autonomous delivery vehicles (ADVs) with human riders. Analysis of historical order data indicates that inefficient order assignment between ADVs and riders is a major operational bottleneck. To address this problem, a collaborative human-ADV delivery model is formulated with the objective of minimizing total logistics costs under constraints related to delivery time windows, vehicle capacity, and routing requirements. The proposed model is applied to a real-world case involving QX Fresh Supermarket, comprising 80 orders and 16 community transfer points. An improved genetic algorithm is developed to solve the optimization problem efficiently. Empirical results for off-peak, normal, and peak periods show that the collaborative delivery approach significantly improves ADV utilization and reduces total delivery costs by more than 30 % without compromising timeliness. These findings provide both a sound theoretical basis and practical guidance for the advancement of human-machine collaborative logistics.
Reliable defect evaluation is essential in ceramic sanitaryware manufacturing, where inspection outcomes directly influence rework decisions, process control, and delivery performance. In practice, defect assessment is often affected by operational variability, class imbalance, and human-dependent inspection procedures, which limit the repeatability and consistency of quality control decisions. This study formulates multi-class defect classification as a practical quality control problem and investigates the robustness of production-data-based decision-support in an industrial environment. The analysis is based on a real-world dataset comprising 11,071 production records collected under routine operating conditions. Defect labels were assigned through a two-stage quality control procedure involving trained inspectors and supervisory verification. Multinomial logistic regression, support vector machines with radial basis function kernels, and CatBoost were evaluated as base classifiers. A probability-based voting ensemble was developed to integrate the complementary decision structures, and posterior probabilities were calibrated using Platt scaling prior to aggregation to improve decision consistency. Experimental results show that the proposed calibrated ensemble improves Macro-F1 from 0.7126 (logistic regression) to 0.7211 and enhances minority-class recall, leading to more balanced performance under severe class imbalance. The findings indicate that probability calibration and ensemble integration contribute to improved stability and interpretability of defect evaluation. Overall, the proposed framework provides a practically deployable decision-support layer that supports more consistent rework decisions and more reliable quality control in industrial production settings.
