Considering and analyzing various kinds of cooperation among supply chain members is an option for better managing each channel. It is noteworthy that in many real‐world cases, each of vertical and horizontal cooperation has an important role in the success of supply chains. Nevertheless, only vertical cooperation in most previous research is considered. This paper addresses both vertical and horizontal cooperation in two competitive reverse supply chains, each of which includes one collector, one remanufacturer, and one retailer. Our primary concern is to analyze quality improvement competition between the remanufacturers. Moreover, retail price competition between the retailers and the quality competition are simultaneously considered in the extended model. In this research, the investigated system has been analyzed under different structures including decentralized, centralized, horizontal cooperation, and coordinated decision‐making models. The results show that when the remanufacturers cooperate horizontally, the profit of each collector and that of the retailer will decrease compared with those in the decentralized structure. To overcome this problem, a new coordination contract named multiple‐link two‐part tariff is proposed to simultaneously coordinate the members of each chain. The proposed contract effectively convinces the remanufacturers to participate in the coordination model instead of the horizontal cooperation. Moreover, it provides a win–win–win condition for all chain members and improves the quality level of the remanufactured products. The results indicate the proper performance of the proposed contract in improving the benefits of the competing chains, especially when there exists no intense competition between the remanufacturers (i.e., when the market sensitivity to the quality of the remanufactured products is low and consequently less effort is needed to increase the quality of the remanufactured products). Moreover, the proposed contract not only is able to simultaneously increase both remanufactured products demand and of end‐of‐life products supply but also involves both economic and environmental benefits.
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domainspecific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNNbased frameworks.
Car-following is considered as one of the most prevalent fundamental driving behaviors that substantially influences traffic performance as well as road safety and capacity. Drivers’ car-following behavior is affected by numerous factors. However, in practice, very few of these factors have been scrutinized, because of their latent essence and unavailability of appropriate data. Owing to its importance, drivers’ reaction time has attracted the attention of many researchers; nevertheless, it is considered as a fixed parameter in car-following models, which is far from reality. To take the variability of drivers’ reaction time into account, a flexible hybrid approach has been suggested in the present study. In the proposed structure, in the first step, the desirable acceleration of the driver is estimated by applying an equation-based car-following model. In the next step, the driver’s reaction delay in applying the calculated acceleration is estimated by an artificial neural network. The corresponding parameters are jointly estimated by applying an estimated distribution algorithm. Statistical tests indicate better performance of the hybrid model, which considers the variations of the driver’s reaction time, compared with a traditional model with fixed reaction time. Furthermore, the cross-validation results indicate better generalizability and transferability of the proposed model in action.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.