Transportation Research Part E-Logistics and Transportation Review最新文献

Modular buses (MBs), which can physically dock and separate, offer enhanced flexibility and potential cost savings in urban transportation. Despite advances in scheduling, trajectory planning for the docking process of MBs is less developed. This paper addresses the two-dimensional trajectory planning for MB docking. We introduce a hierarchical docking planning model based on Nonlinear Model Predictive Control (NMPC). The upper-level model optimizes docking time and speed, while the lower-level dynamically updates trajectories. Our models integrate Frenet and Cartesian coordinates with a precise obstacle avoidance model to ensure safety and smoothness under diverse traffic conditions. We employ segmented Lagrange interpolation for discretizing the continuous NMPC model, enhancing planning accuracy with fewer points and improving solving efficiency. Additionally, a multi-task network adaptively adjusts discretization orders based on environmental data. Extensive testing demonstrates our method’s superior accuracy and efficiency in real-time performance, offering marked improvements in safety and operational smoothness compared to existing approaches.

Accurate ship trajectory prediction (STP) is crucial to realise the early warning of ship collision and ensure maritime safety. Driven by advancements in artificial intelligence technology, deep learning-based STP has become a predominant approach in the research field of ship collision avoidance. This paper, based on a state-of-the-art survey of the existing STP research progress, aims to develop a new bi-directional information fusion-driven prediction model that enables the achievement of more accurate STP results by addressing the drawbacks of the classical methods in the field. In this context, a cascading network model is developed by combining two bi-directional networks in a specific order. It incorporates the Bi-directional Long Short-Term Memory (BiLSTM) and the Bi-directional Gated Recurrent Unit (BiGRU) neural network into a single three-layer, information-enhanced network. It takes advantage of both networks to realise more accurate prediction of ship trajectories. Furthermore, the performance of the proposed model is comprehensively evaluated using Automatic Identification System (AIS) data from three water areas representing traffic scenarios of different safety concerns. The superiority of the proposed model is verified through comparative analysis with twenty other methods, including the state-of-the-art STP in the literature. The finding reveals that the new model is better than all the benchmarked ones, and thus, the new STP solution in this paper makes new contributions to improving autonomous navigation and maritime safety.

We study a three-tier closed-loop supply chain in which a supplier sells un-remanufacturable key components to an original equipment manufacturer (OEM) and a third-party remanufacturer (TPR). The supplier has two options to price the key components: the uniform pricing policy and the differential pricing policy. Additionally, the OEM has the choice to either outsource or authorize the remanufacturing business to the TPR. Using a game-theoretic framework, we analyze the equilibria of multiple games that the two pricing policies and the two remanufacturing modes are available. Among other findings, we show that compared with the authorization remanufacturing mode, the outsourcing remanufacturing mode is a win–win solution for the supplier, the OEM, the consumers, and the society, but it may be detrimental to the TPR. Compared with the uniform pricing policy, the differential pricing policy may lead to win–win situation for the supplier, the TPR, and the consumers, but it hurts the OEM and the society. We also show that price discrimination can motivate remanufacturing and improve environmental benefits under certain conditions. The analysis of social welfare and environmental impacts provides timely managerial insights for governments considering relaxing anti-price discrimination laws. To check the robustness of our results, we extend our models to incorporate the production cost of key components, the remanufacturing cost, and a two-period framework. The results indicate that all core insights remain valid and the un-remanufacturability of key components hinders remanufacturing.

The warehouse automation market has experienced significant growth due to the necessity for quick responses to customer needs. The adoption of Automated Storage and Retrieval System (AS/RS) aims to enhance operational efficiency and expedite order fulfillment, although environmental considerations are frequently overlooked. This study introduces the implementation of energy harvesting using Regenerative Braking System (RBS) on AS/RS to minimize the carbon emission impact. The best configuration of storage assignments and Input/Output (I/O) points is examined to improve travel time, response time, and carbon emission as sustainability indicators. This study employs a discrete-event simulation mimicking the AS/RS and warehouse environment under uncertainty. Simulation-based experiment was performed under 96 different scenarios and the result was assessed through statistical tests revealing the main and interaction effects between factors to performance indicators, including the trade-off between them. The result reveals that the implementation of RBS in AS/RS can result in 13% energy saving on average or equal to additional travel range of 28,800 m indicating the suitability adoption towards green operation. However, the lowest carbon emission is followed by higher travel time and response time. Thus, metamodel-based optimization was also performed via desirability function analysis. The optimization result reveals that the sustainable AS/RS configuration is obtained with a single-side for I/O point, non-class for storage classification, closest open location with column-order for slot selection, and closest open location with row-order for retrieval selection.

The fashion industry grapples with volatile demand, characterized by two short-selling sessions, creating an avenue for a fashion firm to receive items through single or dual shipment strategies. Single shipment brings economies of scale and reduces stockout risks. Conversely, dual shipment avoids excessive inventory buildup. To exacerbate the problem, a fashion supply chain faces uncertainties in production marked by launching-failure and order fulfillment risks and has to contend with product obsolescence. Additionally, these factors have distinct impacts on the supply chain members, which can cause inefficiencies across the entire chain. Our study aims to propose an appropriate contract for fashion chain members by considering the impact of shipment policies (single vs. dual). By comparing the benchmark cases, we first address the policy-level dilemma of a vertically integrated fashion firm. We offer a decision matrix for the optimal shipment policy. This matrix weighs production uncertainty, product obsolescence, and associated holding costs. Using the findings of the benchmark case, we investigate the effectiveness of traditional wholesale price and quantity discount contracts. We find these contracts fail to address issues pertaining to flexibility and equitable risk-sharing mechanisms. Finally, we propose a novel risk-sharing quantity discount contract to address these shortcomings. We extend our models by understanding the impact of different levels of launching-failure and order fulfillment risks on ordering decisions and analyze the impact of discounting structures and nonlinear design costs.

Parcel lockers are automated self-collection stations commonly used for e-commerce deliveries. It has emerged as a promising solution that overcomes the operational and sustainability challenges arising from Last-Mile Delivery. In collaboration with a major Singapore-based parcel locker operator and using their nationwide operational data, we study the implications of parcel locker’s spatial accessibility on their operational performance, namely demands and users’ time-to-pickup. Instead of measuring spatial accessibility by straight-line distances, we extend the concept by incorporating a more comprehensive set of spatial factors with the adoption of the 5Ds walkability framework. The framework systematically depicts the interplay between spatial factors and individuals’ walking behaviors that are directly related to their parcel locker usage. Positive correlations between population size, street connectivity, availability of living amenities, and bus stops in proximity of parcel lockers versus the demands are observed. In a similar vein, significant correlations between availabilities of living amenities and transit facilities versus consumers’ time-to-pickup are noted. The findings support the positive contribution of trip comfort to parcel lockers’ demands, while also demonstrating the paradoxical effects of trip-chaining convenience, which boosts demand but delays the pickup process. The study contributes to the literature by establishing linkage between spatial measurements and operational performance of parcel lockers with real-life operational data, which complements prior research that primarily relies on survey data. Besides, the study is the first to characterize the time-to-pickup, a critical parameter for network design and delivery operations.

One of the main challenges of one-way carsharing systems is to maximize profit by attracting potential customers and utilizing the fleet efficiently. Pricing plans are mid or long-term decisions that affect customers’ decision to join a carsharing system and may also be used to influence their travel behavior to increase fleet utilization e.g., favoring rentals on off-peak hours. These plans contain different attributes, such as registration fee, travel distance fee, and rental time fee, to attract various customer segments, considering their travel habits. This paper aims to bridge a gap between business practice and state of the art, moving from unique single-tariff plan assumptions to a realistic market offer of multi-attribute plans. To fill this gap, we develop a mixed-integer linear programming model and a solving method to optimize the value of plans’ attributes that maximize carsharing operators’ profit. Customer preferences are incorporated into the model through a discrete choice model, and the Brooklyn taxi trip dataset is used to identify specific customer segments, validate the model’s results, and deliver relevant managerial insights. The results show that developing customized plans with time- and location-dependent rates allows the operators to increase profit compared to fixed-rate plans. Sensitivity analysis reveals how key parameters impact customer choices, pricing plans, and overall profit.