A novel dynamic resource allocation model for demand-responsive city logistics distribution operations

2006 ◽  
Vol 42 (6) ◽  
pp. 445-472 ◽  
Author(s):  
Jiuh-Biing Sheu
Keyword(s):  
Resource Allocation ◽  
Dynamic Resource Allocation ◽  
City Logistics ◽  
Allocation Model ◽  
Logistics Distribution ◽  
Resource Allocation Model ◽  
Distribution Operations ◽  
Dynamic Resource

scholarly journals Dynamic Resource Allocation Model for Distribution Operations Using SDN

2021 ◽  
Vol 8 (2) ◽  
pp. 976-988
Author(s):  
Shidrokh Goudarzi ◽  
Mohammad Hossein Anisi ◽  
Hamed Ahmadi ◽  
Leila Musavian
Keyword(s):  
Resource Allocation ◽  
Dynamic Resource Allocation ◽  
Allocation Model ◽  
Resource Allocation Model ◽  
Distribution Operations ◽  
Dynamic Resource

scholarly journals Interference resilient stochastic prediction based dynamic resource allocation model for cognitive MANETs

2020 ◽  
Vol 13 (41) ◽  
pp. 4332-4350
Author(s):  
K Shashi Raj ◽  
Keyword(s):  
Resource Allocation ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Dynamic Resource Allocation ◽  
Allocation Strategy ◽  
Secondary Users ◽  
Allocation Model ◽  
Resource Allocation Strategy ◽  
Dynamic Resource ◽  
Mobile Ad Hoc

Background/Objectives: Being dynamic in nature, Mobile Ad-hoc Network (MANET) requires robust resource allocation strategy that can ensure both optimal transmission reliability and resource efficiency to meet Quality of Service (QoS) demands. The objective of this research is to address interference resilience requirement in MANETs which is must due to greedy nature of nodes especially when accessing resource or bandwidth and develop a highly robust stochastic prediction based resource allocation strategy. Methods: The proposed Interference Resilient Stochastic Prediction based Dynamic Resource Allocation model for Cognitive MANET (ISP-DRACM) intends to enable optimal resource allocation under interweave and underlay network setup with instantaneous as well as average interference conditions. It employs a joint power management and resource allocation strategy where it intends to maximize the weighted sum-rate of the secondary users under certain defined conditions like average power and stochastic interference level. Findings/Novelty: Inculcating resource allocation problem as controlled Markov Decision Process using Hidden Markov Model (HMM) and Lagrange relaxation, our proposed model achieves better resource allocation under limited noise or interference condition and hence achieves both costeffectiveness as well as QoS provision. This method has exhibited satisfactory performance towards spectrum allocation to the secondary users without imposing any significant interference for both interweave as well as underlay Cognitive Radio setup. Keywords: Cognitive mobile ad-hoc network; stochastic prediction; interference resilience; channel state information; dynamic resource allocation; underlay and overlay cognitive MANET

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