Analysis of Forecasting Methods and Applications of System Dynamics and Genetic Programming: Case Studies on Country Throughput
Independent thesis Advanced level (degree of Master (Two Years))Student thesisAlternative title
Analysis of Forecasting Methods and Applications of System Dynamics and Genetic Programming : Case Studies on Country Throughput (Swedish)
Objectives. In this study we review previous attempts in forecasting country seaborne container throughput, analyze them and then classify in form of table to provide a concrete base for researchers in this field. Another aim of this study is to provide a Decision Support System (DSS) to assist experts in port management and forecast their country seaborne container demand. It will lead to reasonable decisions so as to provide sufficient supply which handles containers demand. This DSS, is a global forecasting model which can be applied to every country, independently of their specific parameters. Methods. In theoretical phase a number of scientific databases such as: Google Scholar, ACM, SCOPUS, IEEE, SpringerLink and some other are used to collect previous studies. After review and analysis, selected papers are classified in a form of table to provide a complete resource for us as well as future researchers in this field. In order to provide appropriate model, we combine System Dynamics modeling with Genetic Programming to provide an accurate and reliable model. This model is the result of the analysis of previous studies and applied in this study for the first time. Results. Our final model was applied to two cases (Sweden and China) and provides provided reliable results for both countries. To analyze the uncertain variables in the model, Monte Carlo simulation was used to assess the sensitivity of our model. In order to compare with other methods, we conducted a case study with Artificial Neural Network (ANN) and compared the results of our model and ANN. The results show the disadvantages of statistical methods to system dynamics. Additionally to compare with other attempts, our model was confronted with another study which provided a model for Finland. By comparing and considering their advantages and disadvantages we found out that our simplified model could be applied as a global model to other countries. Conclusions. We conclude that our model is an appropriate DSS to assist experts, forecast their country throughput and make appropriate decisions so as to invest, extending their ports in right time. The application of Genetic Programming in our model provides accurate mathematical equations for the influencing variables which even may not need to calibrate the model. It is a global model which can be applied to different countries but still requires more experiments to prove this claim.
This research aims to provide a decision support system to assist experts in port management to forecast future trends of cargo demand. By forecasting the future demand, decision makers will be able to decide on sufficient supply. For example, in case of necessity, based on forecasting results, the infrastructure can be expanded and also the capacity of ports can be managed. This will help not only to invest in right place and time, but also to balance their demand between ports in a country. The majority of previous researches considered only statistical methods to forecast the future cargo demand. Sometimes the previous research studies applied only one method and then compared it with others and provided advantages and disadvantages of each methods. In some other cases the previous research studies were combining statistical methods to analyze linear and non linear behavior of influencing parameters in cargo demand to conduct a forecast later and its future demand. All the research studies that were collected were analyzed and then classified into a table (c.f., chapter 4). Recently, some studies applied system dynamics to analyze all interactions in the system and forecast the future cargo demand like (Ruutu 2008) and (E. Suryani et al. 2012). In this research we combined system dynamics with genetic programming to benefit from the advantages of each method. By using the system dynamics modeling technique, we defined all influencing parameters and their interactions in the system. By use of genetic programming we provided accurate equations between different parameters and country demand. In Genetic Programming, all the equations can be fitted into data. At last, even we do not need to calibrate the equations to fit into historical data. This will provide a reliable model to forecast demand and align the supply with it. To validate our model, it is applied on two different countries and the results from the analysis indicate that the simplified model provides an acceptable model and it follows the trend of historical data. To compare our model with previous statistical methods the results of our model in Sweden and China were compared with the result of neural network in another case study with the same data. To compare our model with other similar studies, it turned out that it is closely related to the model for Finland. After comparison and analysis of their advantages and disadvantages, we concluded that our simplified model can apply as a global model to other countries, but it needs to prove with a number of different case studies (different countries with different situations). To analyze the uncertain variables, which can affect the model, we used Monte Carlo simulation. It assesses the sensitivity of our model to changes in input variables. The final model is applicable to every country, but it needs to apply the local econometric parameters, which affect the country throughput. By considering the share of each port in total demand of the country, we can apply the model to each port and forecast the future trends in order to find the right date to invest and extend the capacity to handle Demand.
Place, publisher, year, edition, pages
2012. , 90 p.
Global Forecasting Model, Country Throughput, System Dynamics, Genetic Programming.
IdentifiersURN: urn:nbn:se:bth-2140Local ID: oai:bth.se:arkivexED9DCDEFFF9586B7C1257AB0004FE328OAI: oai:DiVA.org:bth-2140DiVA: diva2:829407
Henesey, Dr. Lawrence