Purpose – The purpose of this paper is to develop a framework for route selection in multimodal transportation which can reduce cost, lead time, risk and CO2 emission in multimodal transportation systems. Design/methodology/approach – This research proposes the development of a framework for route selection in multimodal transportation that includes a six-phase framework to select an optimal multimodal transportation route. The first phase is to collect the data of each route and select the origin and destination. The second phase is to calculate time and cost of each route by using a multimodal transport cost-model. In the third phase, the CO2 emissions are calculated based upon the 2006 guidelines of Intergovernmental Panel on Climate Change. The fourth phase proposes an integrated quantitative risk assessment, analytic hierarchy process (AHP) and data envelopment analysis methodology to evaluate the multimodal transportation risk. The fifth phase is to prioritize criteria by using the AHP which can be used in the objective function. The final phase is to calculate the optimal route by using the zero-one goal programming. Findings – The aims of the model are to minimize transportation costs, transportation time, risk and CO2 emission. Practical implications – The approach has been tested on a realistic multimodal transportation service, originating from Bangkok in Thailand to a destination at Da Nang port in Vietnam. The results have shown that the approach can provide guidance in choosing the lowest cost route in accordance with other criteria, and to minimize the CO2 emission effectively. Originality/value – The contribution of this research lies in the development of a new decision support approach that is flexible and applicable to logistics service providers, in selecting multimodal transportation route under the multi-criteria in term of cost, time, risk and importantly the environmental impact.
The objective of this research was to develop a decision support framework (DSF) to assess quantitative risk in multimodal green logistics. This risk assessment is the combination of a number of models, the failure mode and effects analysis, the risk contour plot, the quantitative risk assessment, the analytic hierarchy process and the data envelopment analysis which can support a user to perform risk assessment in various decisions. The contribution of this research is that the risk assessment model can generate an optimal green logistics route in accordance with weight from the user. The highlight of this DSF is that the quantitative assessment model can reduce bias on risk assessment of logistics route. An in-depth case study, recommendations, limitations and further research are also provided.
The objective of this research is to select health beverage flavour appropriate for the ageing people with the aid of a decision support framework by using the artificial neural network. The decision support framework's role is to gather information between consumers and manufacturers. The framework has the capability to compile the collected data and form the suitable model for selecting beverage flavouring of the products. In order to identify the preference of consumer, the artificial neural network has been applied to classify the beverage preference, i.e. taste, colour and odour of health beverages as well as the consumer groups. The questionnaire is used to gather the preference for taste, colour and odour from consumer groups which are separated into four groups such as the gender (male or female), age (60-65 years or over 65 years) health condition (healthy or unhealthy) and symptoms. The results of this research can benefit to consumers and manufacturers. The consumers can know the most preferred health beverage. In addition, the manufacturers can produce products that can match the consumer's preference.
The objective of research is to design and develop a Decision Support System (DSS) software to assess customer satisfaction on functional beverage flavor notes. Questionnaire is launched to gather data of costumer preference. The taste, color and odor are the subjects of questionnaire. Data is acquired from 400 customers in six groups from North, North Eastern, Central, Southern, and Bangkok of Thailand that have different gender and age. The survey shows that there are 5 well known tastes of functional drinks which are mango, passion fruit, Thai blueberry, linhzhi and mangosteen in both level of concentration (100% and mixed). The DSS is analyzed by using ANN in comparison with hybrid Artificial Neural Network and Particle Swarm Optimization (ANN-PSO). Both models give the same results shown in structure (6-18-30). The minimum MSE is 0.0054784 at 6 epochs. As a result of comparison between two models, the result shows that minimum speed time of ANN-PSO faster than ANNs. Hence, ANN-PSO is an appropriate system for using in the DSS software.
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