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https://ptsldigital.ukm.my/jspui/handle/123456789/487269Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Mohd Nizam Ab. Rahman, Prof. Dr. | - |
| dc.contributor.author | Saeid Jafarzadeh (P52204) | - |
| dc.date.accessioned | 2023-10-11T02:31:39Z | - |
| dc.date.available | 2023-10-11T02:31:39Z | - |
| dc.date.issued | 2014-09-06 | - |
| dc.identifier.other | ukmvital:74853 | - |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/487269 | - |
| dc.description | The increasing competition in the global economy has led organisations and companies to take advantage of supply chain and logistics management to enhance competitiveness, performance and systems development. However, the benefits of logistics cannot be achieved completely without well-developed transportation systems. Therefore, modelling and optimisation of transportation systems is of great importance. This study aims to develop and implement an efficient method to optimise the performance of transportation systems and reduce costs. The Iran Khodro Company (IKCO) transportation system was selected as a case study. To optimise the IKCO transportation system, 50 sets of design of experiment (DOE) were performed with the use of central composite design (CCD) in response surface methodology (RSM) on the basis of five impact factors (independent variables) which directly affect transportation, namely, van, lorry, truck, labour and fuel consumption. However, the application of RSM as an analytical tool to determine the optimum conditions for large-scale multivariable systems, such as the IKCO, is difficult and impracticable because conducting many experiments to explore all scenarios is expensive and time consuming. Therefore, the artificial neural network (ANN) as advanced modelling and predicting tool was applied to model and predict the responses (carried load) for the experiments designed with the use of RSM. Three algorithms, namely, batch backpropagation, quick propagation (QP) and Levenberg-Marquardt, were evaluated. The QP algorithm with 7-4-1 network topology exhibited the highest predictive power with its highest coefficient of determination (R2) value and lowest root-mean-square error (RMSE). The responses of the experiments (carried load in summer 2013) were predicted with the use of the selected model in ANN (QP-7-4-1). To evaluate the ANN (QP-7-4-1) model, the predicted values were validated against the actual values obtained for summer 2013, and a good agreement (89.62%) was found. The optimum conditions of the transportation factors were determined with the best-fitted model (RSM-2FI). Meanwhile to evaluate the RSM-2FI model, the optimum conditions obtained for summer 2013 were compared with the actual data for summer 2013. The evaluation and comparison between the two models showed that the amount of carried load increased from 118,344.4 tons to 123,211.3 tons (4.11% increase), and the cost decreased from USD 2,015,849 to USD 1,685,550 (16.38% decrease) under optimum conditions. These results show the remarkable improvement in the transportation system with the application of the combined RSM and ANN model. The developed method and the findings of this study can be used by managers and industry owners, particularly practitioners in transportation systems, as efficient tools to optimise their respective systems. This study also contributes to the development in systems modelling and optimisation to increase efficiency and output, for optimisation of transportation system in supply chain management.,PhD | - |
| dc.language.iso | eng | - |
| dc.publisher | UKM, Bangi | - |
| dc.relation | Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina | - |
| dc.rights | UKM | - |
| dc.subject | Optimisation of transportation system | - |
| dc.subject | Transportation system | - |
| dc.subject | Cost efficiency | - |
| dc.subject | Logistics performance | - |
| dc.subject | Automotive company | - |
| dc.subject | Universiti Kebangsaan Malaysia -- Dissertations | - |
| dc.title | Optimisation of transportation system to improve cost efficiency and logistics performance in automotive company | - |
| dc.type | Theses | - |
| dc.format.pages | 211 | - |
| Appears in Collections: | Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_74853+Source01+Source010.PDF Restricted Access | 8.98 MB | Adobe PDF |  View/Open |
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