An Approach for Economic Analysis of Intermodal Transportation

A different intermodal transportation model based on cost analysis considering technical, economical, and operational parameters is presented. The model consists of such intermodal modes as sea-road, sea-railway, road-railway, and multimode of sea-road-railway. A case study of cargo transportation has been carried out by using the suggested model. Then, the single road transportation mode has been compared to intermodal modes in terms of transportation costs. This comparison takes into account the external costs of intermodal transportation. The research reveals that, in the short distance transportation, single transportation modes always tend to be advantageous. As the transportation distance gets longer, intermodal transportation advantages begin to be effective on the costs. In addition, the proposed method in this study leads to determining the fleet size and capacity for transportation and the appropriate transportation mode

A COST AND PIPELINE TRADE-OFF IN A TRANSPORTATION PROBLEM

Abstract: The present paper deals with a trade off between cost and pipeline at a given time in a transportation problem. The time lag between commissioning a project and the time when the last consignment of goods reaches the project site is an important factor. This motivates the study of a bi-criteria transportation problem at a pivotal time T . An exhaustive set E of all independent cost-pipeline pairs (called efficient pairs) at time T is constructed in such a way that each pair corresponds to a basic feasible solution and in turn, gives an optimal transportation schedule. A convergent algorithm has been proposed to determine non-dominated cost pipeline pairs in a criteria space instead of scanning the decision space, where the number of such pairs is large as compared to those found in the criteria space. 197 198 Vikas Sharma, Rita Malhotra, Vanita Verma / A Cost And Pipelin

Pareto optimal-based feature selection framework for biomarker identification

Numerous computational techniques have been applied to identify the vital features of gene expression datasets in aiming to increase the efficiency of biomedical applications. The classification of microarray data samples is an important task to correctly recognise diseases by identifying small but clinically meaningful genes. However, identification of disease representative genes or biomarkers in high dimensional microarray gene-expression datasets remains a challenging task. This thesis investigates the viability of Pareto optimisation in identifying relevant subsets of biomarkers in high-dimensional microarray datasets. A robust Pareto Optimal based feature selection framework for biomarker discovery is then proposed. First, a two-stage feature selection approach using ensemble filter methods and Pareto Optimality is proposed. The integration of the multi-objective approach employing Pareto Optimality starts with well-known filter methods applied to various microarray gene-expression datasets. Although filter methods provide ranked lists of features, they do not give information about optimum subsets of features, which are namely genes in this study. To address this limitation, the Pareto Optimality is incorporated along with filter methods. The robustness of the proposed framework is successfully demonstrated on several well-known microarray gene expression datasets and it is shown to achieve comparable or up to 100% predictive accuracy with comparatively fewer features. Better performance results are obtained in comparison with other approaches, which are single-objective approaches. Furthermore, cross-validation and k-fold approaches are integrated into the framework, which can enhance the over-fitting problem and the gene selection process is subsequently more accurate under various conditions. Then the proposed framework is developed in several phases. The Sequential Forward Selection method (SFS) is first used to represent wrapper techniques, and the developed Pareto Optimality based framework is applied multiple times and tested on different data types. Given the nature of most real-life data, imbalanced classes are examined using the proposed framework. The classifier achieves high performance at a similar level of different cases using the proposed Pareto Optimal based feature selection framework, which has a novel structure for imbalanced classes. Comparable or better gene subset sizes are obtained using the proposed framework. Finally, handling missing data within the proposed framework is investigated and it is demonstrated that different data imputation methods can also help in the effective integration of various feature selection methods