查看更多>>摘要：In a mixed traffic environment that consists of both Connected Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs), the platoon sizes of CAVs play a significant role in traffic flow analysis. However, the statistical properties of these platoon sizes have not been thoroughly addressed in existing research. This study aims to fill this critical gap by modeling CAV platoon sizes as a random variable, analyzing scenarios both with and without a Maximum Platoon Size (MPS) constraint. Specifically, the frequencies and corresponding probability distributions of CAV platoon sizes under these conditions are derived. Furthermore, the distribution derivations are extended by incorporating platooning willingness. Through numerical analysis, the results reveal that the proposed probability distributions align closely with numerical observations, demonstrating the consistency and reliability of the model. The study also explores the characteristics of these distributions, as well as the effects of the MPS constraint and platooning willingness. By examining the platooning behaviors in mixed traffic and providing analytical derivations for CAV platoon size probability distributions, this research lays a robust mathematical foundation for further analysis of mixed traffic dynamics, enhancing traffic management and efficiency in increasingly automated traffic environments.
查看更多>>摘要：This paper investigates a multi-shift drayage planning problem arising from container truck transportation across multiple terminals within a port area. We consider it at the tactical planning level and determine the optimal truck workload for each shift. The main distinction between our problem and others is the incorporation of multiple shift planning, handling container transportation requests-each consisting of a batch of containers with the same origin and destination-and accounting for their completion times. A mixed integer programming model is proposed to minimize total transportation completion time. To solve large-scale instances, we develop a Branch-and-Benders-and-Price algorithm. This approach not only decomposes the problem into a series of manageable sub-problems but also divides the workload determination into two tractable steps: one for assigning workloads to shifts and another for verifying the feasibility of these assignments. Unlike the common Branch and Price, our approach maintains a subset of variables as integers while allowing the remaining variables to be continuous, significantly improving the lower bound and enabling obtaining optimal solutions efficiently. We validate the proposed approach via random instances and real-world cases. The results demonstrate that our approach outperforms a solver and a Branch and Price. We also apply our method to a real-world case involving Roll-On/Roll-Off terminal cargo transfer, which shares key similarities with the problem at hand, thereby further broadening the scope of our approach's applicability. And, sensitivity tests are conducted to demonstrate the robustness of our approach against variations in problem settings.
Zhang, LingyuSchacht, OliverLiu, QingNg, Adolf K. Y....
1.1-1.21页
查看更多>>摘要：Inland waterway transport (IWT) has gained significant attention for its environmental sustainability. Consequently, there is an increasing focus on boosting IWT's market share to reduce transportation emissions. Accurate forecasting of IWT freight demand is crucial for ports to plan long-term targets and support a mode shift towards sustainable transport. However, forecasting IWT demand is challenging due to the complexity of external environments. This paper introduces a Dynamic Graph Attention Multi-attention Network (DGAT-MAN) model designed to forecast IWT freight demand by capturing evolving spatial and temporal dynamics. Our comparative analysis demonstrates that this model significantly outperforms established baseline approaches. As one of the first studies to apply spatio-temporal deep learning models to IWT demand forecasting, this work contributes a novel approach to enhancing sustainable transport planning.
查看更多>>摘要：We study a shared vacant private parking spaces allocation problem that considers the uncertain parking duration of demanders. To solve the problem, we first formulate a stochastic programming model (P model). The objective is to maximize the weighted sum of the total expected profits from the platform parking revenue, overload cost and idle cost. On this basis, we reformulate the P model into the UPDA model based on the sample average approximation. Unlike the traditional construction of Benders cut using the dual problem, we construct a new Benders cut based on the lower bound of the subproblem, and then propose an efficient enhanced Benders decomposition (EBD) algorithm for solving the UPDA model. Finally, the performance of the algorithm is verified by numerical experiments. The experimental results show that the enhanced Benders decomposition algorithm outperforms both the Benders decomposition algorithm and commercial solver, and can effectively solve large-scale problems with high complexity. The experimental results also show that the uncertainty in the parking duration of the demander has negative impact on the system performance.
查看更多>>摘要：Accurate prediction of vessel prices across multiple ship types is crucial for providing scientific decision-making support to shipping enterprises, investment institutions, and policymakers. However, traditional statistical methods and machine learning models face significant limitations in accuracy, generalization, applicability, interpretability, and data coverage, rendering them inadequate for high-precision forecasting in the complex shipping market. Inspired by the successful application of Transformer-based models like ChatGPT across various domains, this study proposes a novel ship price prediction model based on the Transformer architecture-SPPformer. The model integrates architectural optimization, pre-training, and fine-tuning techniques to enable comprehensive price forecasting for multiple ship types. On the data front, this paper consolidated over one million data variables from 12 maritime-related domains to pre-train the model, forming a Basic Model with foundational time-series processing capabilities. Subsequently, domain-specific maritime data were incorporated through fine-tuning to develop the Sectional Model, enhancing its specialization in the shipping sector. For interpretability, the SHAP method was embedded into the SPPformer prediction framework to visualize the impact of feature variables on target variables during the forecasting process. In terms of efficiency, a sparse attention mechanism was introduced by combining Atrous Self-Attention and Local Self-Attention, replacing the global attention mechanism of traditional Transformer, thereby significantly improving training efficiency and solving overfitting issues. The empirical study focused on predicting newbuilding and secondhand vessel prices for dry bulk, container, tanker, and the overall shipping market. The results demonstrate that the SPPformer model outperforms traditional ones in accuracy and interpretability. At the same time, the introduction of sparse attention reduces training time and memory usage by 22.91 % and 26.12 %, respectively, compared to global attention mechanisms. This research provides essential references for shipping enterprises to enhance economic efficiency and for financial institutions to manage risks. It also offers data driven support and analytical frameworks for governments to regulate market order and promote the stable development of the shipping industry.
查看更多>>摘要：The increased financing demand on retailers and the risk aversion from banks have brought extensive attention on group lending (GL), the emerging bank loan mode in addition to credit financing (CF), which requires the retailers to apply for a group loan with joint liability and the bank to set uniform interest rates. In order to investigate how GL impacts the operational decisions and profits in the supply chain, we consider two capital-constrained retailers who source from one supplier via the CF or GL loan from a risk-averse bank. We demonstrate that compared with CF, GL enables the retailers to cooperate on financing and compete on product sale, which induces a co-opetition that motivates the retailers to either increase order and loan amounts due to the reduction of financing cost or reduce the order and loan amounts due to the constraint of cooperation. Moreover, GL may benefit or hurt the supply chain members, contingent on the wholesale price and the retailers' future revenue. Surprisingly, a higher bank risk aversion that widens the financing cost gap between CF and GL may enable the retailers to prefer CF that induces higher financing costs than GL. We also reveal that when wholesale price is endogenous, GL might deteriorate or alleviate the double marginalization issue, and it could be Pareto-improved by partial joint liability. Our research provides managerial insights for the retailers' financing mode choices and the market managers' supply chain management.
查看更多>>摘要：With our study, we aimed to enrich the discourse on supply chain disruptions by exploring the strategic responses of firms to supply chain uncertainty (SCUn) that enhance supply chain resilience (SCRes). Drawing on the dynamic capabilities view (DCV), we investigated whether and how firms utilise uncertainty amid geopolitical turmoil as a catalyst to enhance SCRes. This contrasts with the predominant focus found in the existing literature on the detrimental impacts of uncertainty amid rising geopolitical tensions. Using survey data drawn from 242 firms across multiple industries in Pakistan, we employed structural equation modelling (SEM) to test our proposed model, introducing network capabilities (NCs) and innovation ambidexterity (IA) as mediators to elucidate their differential roles in the SCUn-SCRes relationship. Our findings reveal that SCUn triggers strategic responses aimed at building SCRes, with NCs emerging as a significant mediator that enhances SCRes. However, IA has an insignificant mediating effect. Notably, our study uncovers a sequential mediation pathway from NCs to IA, highlighting the dynamic interplay between these capabilities in translating SCUn into enhanced SCRes amid global crises. Our study provides actionable insights for logistics and supply chain managers who navigate uncertain environments amid geopolitical tensions, emphasizing the importance of NCs in driving IA towards achieving SCRes. Our research, which makes a novel contribution by going beyond the conventional perspectives on SCUn and SCRes, advances a new stream of literature on how SCUn influences SCRes through the mediating roles of NCs and IA.
查看更多>>摘要：Advancements in digital technologies have catalyzed the emergence of the ship-then-shop subscription service, which offers consumers a personalized shopping experience. Using this service, consumers could either receive products regularly at a fixed frequency, i.e., fixed shipment frequency service (FSF), or place orders as needed under on-demand service (ODS) by paying a service fee for each shipment. Nevertheless, suppliers' service portfolio design is challenged by the information of consumers' demand occurrence rates, particularly on service fee and service capacity. In this regard, this study strives to understand this problem by developing a tractable model comprising a supplier and a consumer. Consumer demand rate, either high or low, remains private information, leading to the classification of consumers in high and low types. When the difference in reservation utility is moderate, a first-best result can manifest amid information asymmetry. However, when the reservation utility of the H-type consumer is small (large), the supplier must strategically distort the service portfolio to encourage honest choices from consumers. Besides, ODS does not necessarily benefit the supplier even though the exact demand rate information is common knowledge. As the reservation utility of H-type consumers rises, the supplier's preference for FSF initially grows but subsequently declines. Moreover, it is not necessarily the case that symmetric demand rate information proves strictly better off for the supplier when the reservation utility of the H-type consumer is extremely low. The two preceding findings elucidate the reasons for the coexistence of ODS and FSF modes in practice. Finally, information asymmetry may endow information rent to the H-type consumer.
查看更多>>摘要：The rise in geopolitical conflicts has created unprecedented risks for firms, particularly in their operations and supply chains. In the wake of the 2022 Russo-Ukrainian War, many multinational enterprises have faced significant challenges in managing these risks, with some becoming "locked-in" to such high-risk regions as Russia. This study explores how firms manage operations under geopolitical conflict, with a focus on those unable to fully exit risky markets. Using a sample of U.S.-listed firms maintaining operations in Russia following the invasion, our propensity score matching and difference-in-differences analysis demonstrates that these firms have experienced undermined profitability. However, we find mixed moderating effects of different types of market dependencies. Firms with subsidiaries and suppliers in Russia experienced a more severe decline in profitability, while having customers in Russia served to mitigate this impact. Moreover, we explore the role of slack resources in alleviating the adverse effects, showing that firms with more operating and unborrowed slacks better maintained their financial performance. The findings contribute to the operations and supply chain management literature on geopolitical risks and resource dependence theory, while offering managerial implications for navigating operations under geopolitical conflicts.
查看更多>>摘要：E-commerce has been growing at an unprecedented rate over the last decade. Nowadays, products are sold either on online e-platforms or at traditional offline stores. Two online operation modes between suppliers and e-platforms are dominant: reselling and agency. We examine the supplier's trade-offs for blockchain technology adoption in both online operation modes. There are two potential win-win scenarios in which the supplier and e-platform could mutually benefit. The advent of blockchain technology has not impacted the competition between online and offline channels or the selection of online operation modes. The decision of the supplier to adopt blockchain technology is also not contingent on the online operation mode. Blockchain adoption may lead to an increase in the wholesale price of products, but will not necessarily promote an increase in product demand. However, the supplier adopting blockchain technology produces an all-win effect, When the blockchain operational cost is low, all supply chain members' attitudes toward blockchain adoption are aligned. When consumers' trust in product information is lower, the relative effectiveness of adopting blockchain technology increases. Finally, we find that the government subsidy enables all supply chain members to achieve Pareto improvement, and supply chain coordination can be achieved through the provision of government subsidies.
NETL
NSTL
Elsevier