scholarly journals Maximization of Service Flows Rates as a Solution of Network Capacity Allocation Problem

2018 ◽  
Vol 3 (1) ◽  
pp. 1-10
Author(s):  
Bohdan Buhyl
Keyword(s):  
Network Capacity ◽  
Capacity Allocation ◽  
Allocation Problem

Distributed task allocation with critical tasks and limited capacity

Robotica ◽  
2021 ◽  
pp. 1-25
Author(s):  
An Zhang ◽  
Mi Yang ◽  
Bi Wenhao ◽  
Fei Gao
Keyword(s):  
Numerical Simulations ◽  
Unmanned Aerial Vehicle ◽  
Task Allocation ◽  
Network Capacity ◽  
Allocation Problem ◽  
Limited Capacity ◽  
Performance Impact ◽  
Task List ◽  
Aerial Vehicle ◽  
Traditional Performance

Abstract This paper considers the task allocation problem under the requirement that the assignments of some critical tasks must be maximized when the network capacity cannot accommodate all tasks due to the limited capacity for each unmanned aerial vehicle (UAV). To solve this problem, this paper proposes an extended performance impact algorithm with critical tasks (EPIAC) based on the traditional performance impact algorithm. A novel task list resizing phase is developed in EPIAC to deal with the constraint on the limited capacity of each UAV and maximize the assignments of critical tasks. Numerical simulations demonstrate the outstanding performance of EPIAC compared with other algorithms.

Heuristic algorithms to solve the capacity allocation problem in photolithography area (CAPPA)

OR Spectrum ◽  
2007 ◽  
Vol 30 (3) ◽  
pp. 431-452 ◽  
Author(s):  
Shu-Hsing Chung ◽  
Chun-Ying Huang ◽  
Amy H. I. Lee
Keyword(s):  
Heuristic Algorithms ◽  
Capacity Allocation ◽  
Allocation Problem

Optimal resource allocation strategy for two-layer complex networks

2018 ◽  
Vol 32 (05) ◽  
pp. 1850054 ◽  
Author(s):  
Jinlong Ma ◽  
Lixin Wang ◽  
Sufeng Li ◽  
Congwen Duan ◽  
Yu Liu
Keyword(s):  
Complex Networks ◽  
Service Providers ◽  
Network Capacity ◽  
Capacity Allocation ◽  
Allocation Strategy ◽  
Traffic Dynamics ◽  
Traffic Capacity ◽  
Optimal Value ◽  
Optimal Resource ◽  
Resource Allocation Strategy

We study the traffic dynamics on two-layer complex networks, and focus on its delivery capacity allocation strategy to enhance traffic capacity measured by the critical value [Formula: see text]. With the limited packet-delivering capacity, we propose a delivery capacity allocation strategy which can balance the capacities of non-hub nodes and hub nodes to optimize the data flow. With the optimal value of parameter [Formula: see text], the maximal network capacity is reached because most of the nodes have shared the appropriate delivery capacity by the proposed delivery capacity allocation strategy. Our work will be beneficial to network service providers to design optimal networked traffic dynamics.

Limited static and dynamic delivering capacity allocations in scale-free networks

2017 ◽  
Vol 28 (11) ◽  
pp. 1750140 ◽  
Author(s):  
N. Ben Haddou ◽  
H. Ez-Zahraouy ◽  
A. Rachadi
Keyword(s):  
Dynamic Properties ◽  
Minimum Cost ◽  
Network Capacity ◽  
Capacity Allocation ◽  
Traffic Information ◽  
Node Degree ◽  
Scale Free ◽  
Routing Strategy ◽  
The Impact ◽  
Allocation Strategies

In traffic networks, it is quite important to assign proper packet delivering capacities to the routers with minimum cost. In this respect, many allocation models based on static and dynamic properties have been proposed. In this paper, we are interested in the impact of limiting the packet delivering capacities already allocated to the routers; each node is assigned a packet delivering capacity limited by the maximal capacity [Formula: see text] of the routers. To study the limitation effect, we use two basic delivering capacity allocation models; static delivering capacity allocation (SDCA) and dynamic delivering capacity allocation (DDCA). In the SDCA, the capacity allocated is proportional to the node degree, and for DDCA, it is proportional to its queue length. We have studied and compared the limitation of both allocation models under the shortest path (SP) routing strategy as well as the efficient path (EP) routing protocol. In the SP case, we noted a similarity in the results; the network capacity increases with increasing [Formula: see text]. For the EP scheme, the network capacity stops increasing for relatively small packet delivering capability limit [Formula: see text] for both allocation strategies. However, it reaches high values under the limited DDCA before the saturation. We also find that in the DDCA case, the network capacity remains constant when the traffic information available to each router was updated after long period times [Formula: see text].

Short-term capacity allocation problem with tool and setup constraints

2005 ◽  
Vol 52 (8) ◽  
pp. 754-764 ◽  
Author(s):  
Elif Akçalı ◽  
Alper Üngör ◽  
Reha Uzsoy
Keyword(s):  
Capacity Allocation ◽  
Allocation Problem ◽  
Short Term
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