Besides distribution services, retail platforms are beginning to provide unobservable promotion services to enhance traffic and demand. Thus, suppliers are incentive to consider the impact of platform promotion on distribution channel strategies. Hence, considering a retail platform supply chain in which the supplier uses an outcome-based contract to incentivize the retail platform’s hidden promotion effort, this paper investigates the implications of unobservable platform promotion on the supplier’s distribution channel strategies. We develop a Stackelberg game model to examine the equilibrium outcomes of reselling and agency selling under unobservable promotion action and find that the retail platform may volunteer to promote in both channels when the market potential difference is large. Interestingly, voluntary promotion may motivate the supplier to increase the wholesale price. Furthermore, we derive conditions under which the supplier prefers reselling and agency selling. When the market potential difference is small, the distribution channel strategy is a threshold policy of the commission rate, below which agency selling is preferred, and reselling is preferred otherwise. It is counterintuitive that when the market potential difference is large, the commission rate will not influence the preference of channel selection and the reselling channel is always preferred by the supplier. Interestingly, the impact of platform promotion on the threshold of distribution channel strategies is non-monotonic in the increment probability of high market potential and market potential difference. Specifically, platform promotion induces the upstream supplier to select the reselling channel suppose the increment probability of high market potential is moderate, while induces the supplier to select the agency selling (reselling) channel when the market potential difference is moderate (large) suppose the increment probability of high market potential is high. Moreover, we find that enhancing the increment probability of high market potential benefits the supplier more, but only moderate increment probability of high market potential makes the retail platform better off.

The increasing focus on environmental regulations and the economic advantages of recycling has spurred interest in the design of multidepot reverse logistics networks (MDRLNs). In these networks, the growing use of intelligent recycling bins (IRBs) has been beneficial for both product recycling and standardizing recycling product pricing. Furthermore, collaboration and resource sharing enhance the efficiency of resource utilization and recycling. This study proposes a multidepot vehicle routing problem with time windows that incorporates intelligent recycling prices (IRPs) and transportation resource sharing (MDVRPTW-IRPTRS). Initially, a linear function is developed to define the relationship between the volume of returned products and IRPs. Subsequently, the problem is expressed as a mathematical model aiming to minimize total operating costs and maximize total recycling profits. Additionally, a hybrid algorithm that combines a three-dimensional (3D) k-means clustering algorithm with a self-adapting genetic algorithm-particle swarm optimization (SGA-PSO) is devised to determine the optimal solution for MDVRPTW-IRPTRS. The 3D k-means clustering algorithm is utilized to categorize IRBs within an MDRLN. The SGA-PSO algorithm incorporates elite preservation and self-adaptive update mechanisms to enhance the solution quality and algorithm convergence. A transportation resource sharing (TRS) strategy is integrated into the SGA-PSO, facilitating the allocation of shared vehicles to alternative recycling routes. A comparative analysis of SGA-PSO against other algorithms, including a hybrid genetic algorithm, an improved particle swarm optimization algorithm, and a hybrid genetic algorithm with variable neighborhood search, demonstrates its superiority in solving the MDVRPTW-IRPTRS. The model and algorithm are applied in a real-world case study in Chongqing, China, and the study discusses the optimized results under varying TRS strategies and IRP schemes, contributing to the development of an efficient and synergistic urban reverse logistics network. Moreover, the superior performance of the proposed approach is validated through the ablation experiments. This study offers valuable decision-making support for fostering an environmentally sustainable and resource-efficient city.

The efficient selection of road freight transport (RFT) firms plays a critical role in constructing well-operating logistics systems for large-scale third-party logistics (3PL) providers. However, the decision-making process for such selection is often complex and uncertain due to various influential criteria and dynamic industry nature. To address this challenge, a novel decision-making model that integrates Delphi, CRiteria Importance Through Intercriteria Correlation (CRITIC) and Combined compromise ranking solution (CoCoSo) methods based on T-Spherical Fuzzy sets has been proposed in this paper. The model integrates expert opinions with an extensive literature review to identify the influential criteria and their corresponding weights. Subsequently, it employs a comprehensive evaluation approach to rank the RFT firms based on their performance. The model accommodates uncertainties and subjectivity by utilizing T-Spherical Fuzzy Numbers, offering robustness and transparency in decision-making. A case study involving 15 evaluation criteria and 12 RFT firms were selected to demonstrate the applicability and aptness of the proposed model. Flexibility and Integrability to different transport modes emerged as the two most essential criteria, whereas RFT firm A2 emerged as the best alternative with the highest performance score of 3.0861, followed by RFT firm A7 with a score value of 3.0499.

Nowadays, the robotic mobile fulfillment system (RMFS) has been increasingly used by online retailers. Compared with traditional picker-to-parts warehouses, the racks of RMFS do not have to return to the same location after picking, thus we can dynamically change their locations, which brings great potential to efficiently fulfill orders. Aiming at minimizing the sum of rack travel distances, the key question is how to reassign a rack to an unoccupied storage location after picking items from the rack. However, the issue involves two challenges for unidirectional RMFS, one is that huge differences may exist between the classical Manhattan distance estimation and the actual distance for the unidirectional aisles in RMFS, and the other one is that we need to account for the frequency of rack moving for multi-item orders. We thus first propose closed-form formulas to optimally estimate the cycle travel distance for each rack. Then, by overcoming the repeated counting issue for multi-item orders, we propose a novel SKU (Stock Keeping Units)-correlation-based algorithm to choose high-velocity racks, which can better fulfill multi-item orders. Finally, embedding the cycle travel distance and SKU-correlation-based velocity, we propose a Velocity-based Rack Storage Location Assignment method (VRSLA) to solve the rack storage location assignment problem by assigning high-velocity racks to the nearest storage locations. Collaborating with a large online retailer in China, we demonstrate the performance of VRSLA by using both small-scale and large-scale datasets. The computational results show that VRSLA not only can achieve near-best solutions compared with an integer programming model solved by Gurobi, but also outperforms four state-of-the-art assignment methods in literature (random, velocity-based class, shortest path, and sale-based) by reducing the rack travel distance up to 43.32%. We also found that the stronger the correlation between SKUs on the racks or the larger the size of the RMFS, the shorter the rack travel distance by the proposed VRSLA method.

The integrated development of city clusters has given rise to an increasing demand for intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading traditional intercity bus services by implementing demand-responsive enhancements. Nevertheless, its online operations suffer the inherent complexities due to the coupling of vehicle resource allocation among cities and pooled-ride vehicle routing. To tackle these challenges, this study proposes a two-level framework designed to facilitate online fleet management. Specifically, a novel multi-agent feudal reinforcement learning model is proposed at the upper level of the framework to cooperatively assign idle vehicles to different intercity lines, while the lower level updates the routes of vehicles using an adaptive large neighborhood search heuristic. Numerical studies based on the realistic dataset of Xiamen and its surrounding cities in China show that the proposed framework effectively mitigates the supply and demand imbalances, and achieves significant improvement in both the average daily system profit and order fulfillment ratio.

Autonomous trucks offer a promising avenue for enhancing the efficiency and reducing the environmental impact of road freight transportation. This paper examines a transitional phase towards a fully unmanned truck fleet, focusing on a platooning approach with a lead driver. We develop a novel optimization model for autonomous truck platooning that simultaneously considers platoon formation, scheduling, and routing to minimize costs related to labor and fuel. The model incorporates the possibility of detours, nonlinear fuel savings due to air-drag reduction, and the practical platoon size limit. We present an enhanced column generation method, termed the platoon-generation-and-updating approach, which demonstrates high effectiveness in reducing computational time and complexity. Our numerical analysis, based on the Hong Kong highway network, demonstrates the substantial cost advantages of autonomous truck platooning. It also investigates how platooning efficiency is influenced by various operating factors, including truck fleet size, platoon size restrictions, labor-to-fuel cost ratio, and the strictness of delivery time windows, with practical implications interwoven into the discussion.

Automobile traffic accidents represent a significant threat to global public safety, resulting in numerous injuries and fatalities annually. This paper introduces a comprehensive, explainable artificial intelligence (XAI) artifact design, integrating accident data for utilization by diverse stakeholders and decision-makers. It proposes responsible, explanatory, and interpretable models with a systems-level taxonomy categorizing aspects of driver-related behaviors associated with varying injury severity levels, thereby contributing theoretically to explainable analytics. In the initial phase, we employed various advanced techniques such as data missing at random (MAR) with Bayesian dynamic conditional imputation for addressing missing records, synthetic minority oversampling technique for data imbalance issues, and categorical boosting (CatBoost) combined with SHapley Additive exPlanations (SHAP) for determining and analyzing the importance and dependence of risk factors on injury severity. Additionally, exploratory feature analysis was conducted to uncover hidden spatiotemporal elements influencing traffic accidents and injury severity levels. We developed several predictive models in the second phase, including eXtreme Gradient Boosting (XGBoost), random forest (RF), deep neural networks (DNN), and fine-tuned parameters. Using the SHAP approach, we employed model-agnostic interpretation techniques to separate explanations from models. In the final phase, we provided an analysis and summary of the system-level taxonomy across feature categories. This involved classifying crash data into high-level causal factors using aggregate SHAP scores, illustrating how each risk factor contributes to different injury severity levels.

Governments and manufacturers are starting to enforce the European transport industry’s transition to sustainable mobility. Meanwhile, transport companies have begun to set their own emissions goals. To achieve these sustainably, they must develop efficient policies to renew their fleets with alternative-fuel vehicles. However, since future trends in relevant parameters are highly uncertain, fleet managers struggle to make informed decisions. We formulate fleet renewal as a sequential optimization problem, considering multiple technologies and operational clusters. Vehicle purchase, sales, depreciation, fuel, carbon, and electric battery prices are modeled as stochastic variables. We propose approximate dynamic programming to calculate fleet renewal policies that achieve emissions goals while optimizing total costs of ownership. This approach is tested in a case study of a German logistics service provider. We investigate optimal timings of purchases and sales for a heavy-duty truck fleet, considering four drivetrain technologies. Our approach can guide decision making in various fleet renewal settings. By applying it to the case study, we derive important managerial implications. The mobility transition will significantly increase transport fleets’ total cost of ownership. To minimize costs, companies should not move prematurely to low-emissions technologies, but hold vehicles for as long as possible to benefit from fewer purchases and sinking prices. The optimal policy depends on the distance driven. For short-distance operations, diesel trucks will remain the dominant technology in the next years, but will be replaced by battery electric trucks in the medium term. In the far future, trucks powered by electricity and hydrogen will be equally important.

In the wake of seismic events, prompt and accurate building damage assessment is crucial to inform post-disaster interventions and recovery efforts. This paper advances a novel multi-period planning strategy for post-earthquake building inspections, conceptualizing the task as a multi-period orienteering problem (MPOP). In this framework, each building selected for inspection hosts a specific reward indicating its informative value for damage assessment. The objective is to design inspection routes that maximize damage information acquisition while adhering to time constraints. After data collection, we utilize a Gaussian process regression (GPR) model to estimate the damage in uninspected buildings. To validate our approach, we conduct an earthquake simulation with realistic building information from San Francisco. The experimental outcomes reveal that our multi-period damage assessment framework maintains robust performance across diverse scenarios and consistently surpasses conventional period-by-period inspection strategies, yielding enhanced damage information acquisition and greater precision in damage estimation. This outcome underscores the effectiveness of our proposed method in strengthening post-earthquake damage assessment and improving recovery planning.

Scrubbers are the commonly used green technologies for a ship to reduce sulfur emissions. Various speed adjustment behaviors would distort the cost and desulfurization efficiency of scrubbers for ships, which causes a discrepancy between the government and the carrier to install scrubbers. This paper addresses the issue by considering three speed adjustment behaviors: rigidity, partial flexibility, and full flexibility. Under rigid behavior, the carrier maintains a constant sailing speed when adopting low-sulfur fuel oil (LSFO) and installing a scrubber. Partially flexible behavior only involves speed adjustments when adopting LSFO, while fully flexible behavior includes speed adjustments when adopting LSFO and installing a scrubber. Our analysis indicates that scrubber installation consistently precedes adopting LSFO in speed. Full flexibility behavior is the least costly speed adjustment behavior, while partial flexibility behavior results in the lowest emissions. The investment discrepancy on scrubbers between the carrier and the government would occur on whether or not to install a scrubber and which level of the scrubber’s quality since the two parties are concerned about the sulfur emissions and cost, respectively. Government subsidies address these discrepancies, leading to improved emission reduction.