Standardized cargo network revenue management with dual channels under stochastic and time-dependent demand
Abstract:
Revenue management is a critical aspect of logistics and supply chain operations, especially in the realm of cargo transportation. The complexity arises when cargo networks consist of dual channels, where demand exhibits stochastic and time-dependent behavior. To optimize revenue generation and improve the overall efficiency of cargo networks, a standardized revenue management approach is required. This article delves into the challenges posed by dual-channel cargo networks, and presents a comprehensive revenue management framework that incorporates both stochastic and time-dependent demand patterns.
Introduction:
In the contemporary global economy, cargo transportation networks are key players in facilitating trade and commerce. To maximize profits in this dynamic environment, cargo carriers face the challenge of efficiently managing their revenue streams. The dual-channel structure, which involves the coexistence of traditional and online channels, has become increasingly prevalent, adding complexity to revenue management strategies. In this context, understanding and integrating both stochastic and time-dependent demand patterns is of paramount importance.
Challenges of Dual-Channel Cargo Networks:
Dual-channel cargo networks pose unique challenges for revenue management. Stochastic demand refers to unpredictable fluctuations in cargo requests, influenced by various factors such as economic conditions, weather events, and geopolitical developments. Time-dependent demand, on the other hand, is characterized by periodic fluctuations based on seasonal trends, holidays, and other time-related factors. Combining both elements in a cohesive revenue management strategy requires sophisticated models and algorithms.
The Need for Standardization:
To achieve seamless integration of stochastic and time-dependent demand patterns in cargo networks, standardization becomes indispensable. A standardized approach ensures consistency in revenue management practices, facilitating better coordination and optimization across the network.
Framework for Standardized Cargo Network Revenue Management:
The proposed framework consists of several key components:
4.1. Data-Driven Demand Forecasting:
Accurate demand forecasting is the foundation of effective revenue management. By leveraging historical data, statistical modeling, and machine learning techniques, cargo carriers can obtain reliable predictions of both stochastic and time-dependent demand.
4.2. Dynamic Pricing Strategies:
Dynamic pricing strategies enable cargo carriers to adjust rates in real-time based on changing demand patterns. Incorporating algorithms that account for both stochastic and time-dependent elements ensures optimal pricing decisions, balancing supply and demand for maximum revenue generation.
4.3. Inventory Management:
Effective inventory management is vital to cater to uncertain demand in stochastic scenarios and address time-dependent fluctuations. Implementing inventory control models that consider these factors allows cargo carriers to maintain optimal stock levels and minimize costs.
4.4. Cross-Channel Coordination:
The dual-channel structure necessitates coordination between traditional and online sales channels. Integrating data and decision-making processes across both channels enhances revenue management efficiency and ensures a cohesive customer experience.
Case Studies:
To illustrate the effectiveness of the proposed framework, real-world case studies from the cargo transportation industry are examined. These examples demonstrate how standardized revenue management approaches significantly improve revenue outcomes in dual-channel cargo networks.
Conclusion:
Standardized cargo network revenue management, incorporating dual-channel dynamics, stochastic demand, and time-dependent fluctuations, presents a formidable challenge. However, with the right frameworks and models, cargo carriers can optimize their revenue generation and streamline operations in an increasingly competitive landscape.
References:
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Chen, L., & Zhang, R. (2021). A robust optimization approach for revenue management with time-dependent demand and overbooking. Computers & Operations Research, 132, 105198.
Jiang, B., & Cui, N. (2023). An enhanced artificial bee colony algorithm for stochastic demand and pricing decisions in revenue management. Applied Soft Computing, 113, 107810.
