The control of the environmental impacts is a considerable challenge to the daily operations of modern logistics companies, especially under the current trend of increasing carbon dioxide emission. ...This paper focusses on freight distribution, introduces a transportation resource sharing strategy to address the multi-depot green vehicle routing problem, and incorporates the time-dependency of speed as well as piecewise penalty costs for earliness and tardiness of deliveries. Transportation resource sharing is proposed to eliminate long and empty-vehicle trips, improve the network's fluidity and the efficiency of resource management. A bi-objective model is proposed to minimize total carbon emission and operating cost, while enforcing piecewise penalty costs on earliness and tardiness to reduce waiting time and improve customer satisfaction. Further, we combine the Clarke and Wright Savings Heuristic Algorithm (CWSHA), the Sweep Algorithm (SwA) and the Multi-Objective Particle Swarm Optimization algorithm (MOPSO) to design a hybrid heuristic algorithm for the vehicle routing optimization. CWSHA and SwA are consecutively used to generate the initial population, and MOPSO is employed for local and global solution search. Computational experiments reveal that sharing transportation resource reduces the total travelled distance, the number of vehicles, and facilitates a cost effective and environment-friendly distribution network. In addition, we also observe that the shortest path sometimes undermines minimum cost and carbon emission objectives. Moreover, sensitivity analyses reveal that vehicle routes are less influenced by piecewise penalty costs under unimodal traffic flows, while bimodal traffic flows would require more investment to reduce carbon emission.
•Time-dependent speed and piecewise penalty cost are integrated to optimize MDGVRP.•A method combining bi-objective mathematical model and hybrid heuristic is designed.•Transportation resource sharing reduces travelled distance and carbon emission.•Vehicle routes are less influenced by piecewise penalty cost under unimodal traffic.•The shortest path sometimes undermines minimum cost and carbon emission objectives.
Traditional Internet of Things (IoT) can achieve computing resource sharing (CRS) between edge devices and end devices. However, computing resources (CR) are not fully utilized, and the heterogeneity ...of CR cannot meet the demand for high-quality services; meanwhile, the distrust between computing nodes or with CRS platforms may hinder the implementation of CRS. In this paper, we propose a CRS framework based on blockchain and collaborative offloading of edge computing. In this framework, to achieve dynamic and efficient CRS among computing nodes, each computing resource requester (CRR) can freely contribute the CR obtained from the computing resource provider (CRP) for blockchain mining and AI services. We also propose a new honestly based distributed PoA via scalable work (HDPoA), in which the honesty of each computing node is considered. CR heterogeneity is formulated as a transaction probability problem. The CRS interactions between CRPs and CRRs are modeled as a multi-leader and multi-follower Stackelberg game, and an efficient method is developed to find the game's equilibrium point. Then, extensive simulations prove the correctness and effectiveness of the proposed framework and interaction model. Finally, we build a prototype of the CRS framework using Python and quantitatively measure and evaluate the performance of the proposed framework in terms of transaction latency.
Since 1960s, with the development of societal and related technologies, many advanced manufacturing systems (AMSs) and modes have been put forward, and they have attracted the attention of a large ...number of researchers in manufacturing, information and management fields. However, existing studies are mainly focused on the specific theoretical research of each AMS, and the horizontal comparison of the difference and evolution of these AMSs are not significant. Furthermore, most of the existing studies try to realize concrete technical implementation, and discussions on the relationship among these AMSs and social factors are relatively rare. Therefore, this paper aims to address this issue, and a brief overview of the development process of AMSs is first presented. Next, a tri-view model is established to analyze the evolution and socialization characteristics of AMSs. It is found that the sharing of manufacturing resources and capabilities, the value creation carriers, the value measuring criteria, the composition of the value chain and enterprise collaboration, and the user participation in manufacturing are all moving towards socialization. It is essential that the evolution and development of AMSs should also adapt this trend towards socialization in order to achieve better sharing of limited resources and efficient adding of value.
Cloud manufacturing (CMfg) aims to realise the sharing of manufacturing resources amongst different stakeholders. Resource-sharing strategies of suppliers are essential in achieving this aim. ...However, current studies on this topic rarely focus on the sharing of management rights over manufacturing resources. To fill this research gap, this study investigates three resource-sharing strategies: independently, as an alliance and by cooperating with a cloud platform operator. This study explores the effects of these different strategies on meeting client requirements in CMfg. The interactions between the operator and suppliers are modelled as a two-stage Stackelberg game that contains a simultaneous subgame. The equilibrium results indicate that players achieve a lower system profit when suppliers share as an alliance rather than independently, which is why the cloud platform operator strongly opposes it. The extended analysis indicates that considering multiple suppliers complicates the allocation of tasks and profits. Low-cost suppliers have significant advantages in terms of tasks and profits when considering different marginal costs. These findings provide insights for suppliers to select appropriate resource-sharing strategies in CMfg.
In the era of Big Data, with data growing massively in scale and velocity, cloud computing and its pay-as-you-go model continues to provide significant cost benefits and a seamless service delivery ...model for cloud consumers. The evolution of small-scale and large-scale geo-distributed datacenters operated and managed by individual Cloud Service Providers (CSPs) raises new challenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resources towards a globally efficient resource allocation model. Earlier solutions for geo-distributed clouds have focused primarily on achieving global efficiency in resource sharing, that although tries to maximize the global resource allocation, results in significant inefficiencies in local resource allocation for individual datacenters and individual cloud provi ders leading to unfairness in their revenue and profit earned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows CSPs to establish resource sharing contracts with individual datacenters apriori for defined time intervals during a 24 hour time period. Based on the established contracts, individual CSPs employ a contracts cost and duration aware job scheduling and provisioning algorithm that enables jobs to complete and meet their response time requirements while achieving both global resource allocation efficiency and local fairness in the profit earned. The proposed techniques are evaluated through extensive experiments using realistic workloads generated using the SHARCNET cluster trace. The experiments demonstrate the effectiveness, scalability and resource sharing fairness of the proposed model.
The Fragile Common Pool of Resource (CPR) Game is an instance of a resource sharing game where a common resource, which is prone to failure due to overuse, is shared among several players. Players ...have a fixed initial endowment and are faced with the task of investing in the common resource without forcing it to failure. The return from the common resource is subject to uncertainty and stochasticity, and is perceived by the players in a prospect-theoretic manner based on their behavioral characteristics. The Fragile CPR Game admits a unique Nash Equilibrium (NE), and it has been shown in the literature that the best response dynamics converge to the NE. In this article, we look at the Fragile CPR Game through the lenses of Games in Satisfaction Form . We refer to the corresponding game as the Fragile SAT-CPR Game. Our main and novel result states that the Fragile SAT-CPR Game admits the Optimal Satisfaction Equilibrium (OSE) and that the proposed Risk Minimizing Dynamics, which are based on the Best Response Dynamics, converge to the OSE. This equilibrium point results in minimizing the probability that the CPR collapses, while the players can obtain the same payoffs as in the NE point. Numerical evaluations indicate that in the OSE the probability that the common resource collapses can be decreased by approximately 95% compared to corresponding one in the NE case, while at the same time the players enjoy the same utility values. Our proofs employ and exploit concepts from the theory of Constrained S-modular Games.
This article examines resource sharing, specifically through DOCLINE interlibrary loan (ILL) network, at the Saint Mary's Medical Center (SMMC) Dr. E.H. Munro Libraries, located in Grand Junction, CO.
•A hybrid cloud manufacturing system supportive of various cloud deployment modes.•A unified ontology for cloud manufacturing based on ISO standards and existing ontologies.•A Semantic Web-based ...approach to management of dynamic resource-sharing policies.
Cloud manufacturing is emerging as a novel business paradigm for the manufacturing industry, in which dynamically scalable and virtualised resources are provided as consumable services over the Internet. A handful of cloud manufacturing systems are proposed for different business scenarios, most of which fall into one of three deployment modes, i.e. private cloud, community cloud, and public cloud. One of the challenges in the existing solutions is that few of them are capable of adapting to changes in the business environment. In fact, different companies may have different cloud requirements in different business situations; even a company at different business stages may need different cloud modes. Nevertheless, there is limited support on migrating to different cloud modes in existing solutions. This paper proposes a Hybrid Manufacturing Cloud that allows companies to deploy different cloud modes for their periodic business goals. Three typical cloud modes, i.e. private cloud, community cloud and public cloud are supported in the system. Furthermore, it enables companies to set self-defined access rules for each resource so that unauthorised companies will not have access to the resource. This self-managed mechanism gives companies full control of their businesses and boosts their trust with enhanced privacy protection. A unified ontology is developed to enhance semantic interoperability throughout the whole process of service provision in the clouds. A Cloud Management Engine is developed to manage all the user-defined clouds, in which Semantic Web technologies are used as the main toolkit. The feasibility of this approach is verified through a group of companies, each of which has complex access requirements for their resources. In addition, a use case is carried out between customers and service providers. This way, optimal service is delivered through the proposed system.
•We address integrated resource sharing and demand redistribution during a pandemic.•We propose a multi-stage stochastic program under resources’ demand uncertainty.•We propose a data-driven ...decision-making framework.•We investigate two real-life case studies for the COVID-19 pandemic.
We study resource planning strategies, including the integrated healthcare resources’ allocation and sharing as well as patients’ transfer, to improve the response of health systems to massive increases in demand during epidemics and pandemics. Our study considers various types of patients and resources to provide access to patient care with minimum capacity extension. Adding new resources takes time that most patients don't have during pandemics. The number of patients requiring scarce healthcare resources is uncertain and dependent on the speed of the pandemic's transmission through a region. We develop a multi-stage stochastic program to optimize various strategies for planning limited and necessary healthcare resources. We simulate uncertain parameters by deploying an agent-based continuous-time stochastic model, and then capture the uncertainty by a forward scenario tree construction approach. Finally, we propose a data-driven rolling horizon procedure to facilitate decision-making in real-time, which mitigates some critical limitations of stochastic programming approaches and makes the resulting strategies implementable in practice. We use two different case studies related to COVID-19 to examine our optimization and simulation tools by extensive computational results. The results highlight these strategies can significantly improve patient access to care during pandemics; their significance will vary under different situations. Our methodology is not limited to the presented setting and can be employed in other service industries where urgent access matters.
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