The purpose of this paper is to analyze the value of an optimized liner-operating transshipment formulation for real world applications. In this context the impact of constraints in the optimum flow solutions and the impact of internal and external competition on the total costs will be identified and discussed.
Table of Contents
1. Introduction
2. Transshipment Problem
2.1 Network-Characteristics
2.2 Network-Specifications
2.3 Model 1
2.3.1 Task
2.3.2 Optimum Flow
2.4 Model 2
2.4.1 Task
2.4.2 Optimum Flow
2.5 Model 3
2.5.1 Task
2.5.2 Optimum Flow
2.6 Evaluation
3. Real World Application
3.1 Problems solved
3.2 Problems not addressed
4. Impact of Constraints
4.1 Capacity Constraints
4.2 Environmental Constraints
5. Complex Model
5.1 Task
5.2 Optimum Flow
6. Conclusion
Research Objectives and Key Topics
The primary objective of this paper is to analyze the value of an optimized liner-operating transshipment formulation within real-world logistical applications, specifically assessing how various constraints and competitive factors influence optimum flow solutions and total operational costs.
- Application of computerized linear optimization models for liner-shipping.
- Assessment of capacity constraints and their impact on network flow.
- Analysis of environmental policies (such as ECAs) on operational expenditure.
- Evaluation of internal and external competitive factors in liner-shipping.
- Identification of gaps between theoretical modeling and operational reality.
Excerpt from the Book
3.2 Problems not addressed
Nevertheless several important real world issues are missed out in the models. Some of the following problems are not always relevant but they offer at least potential risks that should be taken into account for strategic and operational planning. The transshipment model used in this paper probably refers to the handling of empty containers, therefore parameters like “profit” would not be of interest for the real world application but since this characteristics of the network are not given in detail also “more general” missed out parameters will be identified.
In the formulation only costs are considered but profit is not addressed. Whereas the total cost has a direct influence on the profit, profit alone would be an important parameter, especially if further of the following parameters would be taken into account.
The topic of dynamic pricing for fuels is not observed. Fuel prices are constantly changing and even marginal increases could lead to operational decisions like for example “slow steaming” while adding further ships to the cycle (MAERSK 2011).
The model does not address the problem of congestion. Especially in the container-business port-congestion is a big topic (Rodrigue, Ports and Maritime Trade 2009). Sudden increase in demand for container shipping, port constraints, poor terminal management or strikes and other work related problems could lead to delays and affect the schedule of a liner-operator (Rodrigue, Comtois und Slack, The Geography of Transport Systems 2006, 248). These delays can lead to losses in the business.
Summary of Chapters
1. Introduction: Discusses the growing relevance of computerized methods for optimizing logistical processes in the shipping industry and outlines the paper's focus on transshipment formulations.
2. Transshipment Problem: Defines a fictitious liner-shipping network and provides numerical solutions for three distinct models based on varying constraints and cost parameters.
3. Real World Application: Critically evaluates the utility of the preceding models in a real-world business context and identifies parameters, such as profit and congestion, that are excluded.
4. Impact of Constraints: Examines how capacity limits and environmental regulations, particularly Emission Control Areas, fundamentally alter network flows and operational costs.
5. Complex Model: Analyzes the cost implications of internal and external competition, demonstrating how competitive dynamics impact long-term operator success.
6. Conclusion: Summarizes that while computerized methods are valuable assistance tools, they must be supplemented with robust risk management to account for complex real-world variables.
Keywords
Liner-shipping, Transshipment, Logistics, Optimization, Network-flow, Operational Expenditures, OPEX, Capacity constraints, Emission Control Area, ECA, Supply chain, Intermodal transport, Risk management, Competitive strategy, Freight transport.
Frequently Asked Questions
What is the core focus of this assignment?
The assignment explores the utility of computerized linear optimization models in determining the most efficient transshipment flows for a liner-shipping operator under various constraints.
What are the primary themes addressed in the work?
Key themes include network flow optimization, the impact of infrastructure capacity, environmental regulation, and the influence of competition on shipping costs.
What is the main research objective?
The goal is to determine how well theoretical transshipment models can reflect real-world logistics challenges, particularly regarding cost-minimization and operational flexibility.
Which scientific methodology is utilized?
The author employs mathematical modeling translated into systems of linear equations, utilizing the MS Excel "Solver" function to estimate optimal shipping flows.
What does the main body of the paper cover?
The main body systematically applies different constraints—such as capacity limits and cost increases due to environmental regulations—to a network model and evaluates the resulting shifts in flow and cost.
Which keywords best characterize the work?
The work is best characterized by terms like liner-shipping, transshipment, optimization, and operational constraints.
How does the introduction of an ECA impact the network?
The model shows that an ECA increases specific route costs by 20%, which significantly raises the total cost of the network, though in the tested scenario, it did not alter the optimal flow path itself.
Why are factors like profit and congestion excluded from the model?
These factors are excluded as they are complex to quantify within the provided static linear framework, highlighting the limitation of such models for comprehensive real-world decision-making.
- Quote paper
- Diplom Kaufmann Hans-Christian Stockfisch (Author), 2014, Networks and Logistics in Shipping, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/284799
