A Revenue Management Approach for Network Capacity Allocation of an Intermodal Barge Transportation System

2016 ◽  
pp. 243-257 ◽  
Author(s):  
Yunfei Wang ◽  
Ioana C. Bilegan ◽  
Teodor Gabriel Crainic ◽  
Abdelhakim Artiba
Keyword(s):  
Revenue Management ◽  
Network Capacity ◽  
Capacity Allocation ◽  
Transportation System ◽  
Management Approach

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].

scholarly journals Airline revenue planning and the COVID-19 pandemic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ben Vinod
Keyword(s):  
Revenue Management ◽  
Cash Flow ◽  
Airline Industry ◽  
Business Processes ◽  
Planning Process ◽  
Task Force ◽  
Management Approach ◽  
Content Type ◽  
Demand Patterns ◽  
Pricing And Revenue Management

PurposeThe static world of flight scheduling where schedules rarely change once published is becoming more responsive with schedule change updates leading up to the departure date due to demand volatility and unpredictable demand patterns. Innovation in cash flow generation will take center stage to operate the business in these uncertain times. Forecasting demand for future flights is a challenge since historical demand patterns are not meaningful which requires a new adaptive robust revenue management approach that monitors key metrics, detects anomalies and quickly takes corrective action when performance targets cannot be achieved.Design/methodology/approachThe novel COVID-19 pandemic decimated the travel industry in 2020 and continues to plague us with no end in sight. With the steep drop in revenues, airlines need to adapt to a new marketing planning process of scheduling, pricing and revenue management that is more nimble to adapt quickly to changing market conditions. This new approach will continue to be relevant in a post-COVID-19 world during and after economic recovery.FindingsA methodology for airline revenue planning: scheduling, airline pricing and revenue management, has been proposed that will also work in a post-COVID-19 era.Research limitations/implicationsThe limitation of the proposed model is that it needs to be applied in practice to determine the true benefits of this novel approach to airline revenue planning.Practical implicationsFlight scheduling will rely more on clean sheet scheduling, schedule revisions and close in refleeting to better match demand to supply. The office of the chief financial officer will have a permanent task force to monitor cash flow and come up with innovative solutions to generate cash flow for liquidity. Adaptive robust revenue management workflows will be integrated into traditional revenue management workflows in the future for competitive advantage.Social implicationsIn a post-COVID-19 world it is anticipated that airline business processes will transform to be nimbler and more proactive in making timely decisions at a greater velocity.Originality/valueThe approach to airline revenue planning for scheduling, pricing and revenue management is a new business process that does not exist today at scale in the airline industry.

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