Optimum Project Portfolio Selection for Developmental Plans Using Goal Programming

In vision-driven development plans, such as the Kuwait Mid-Range Development Plan 2015/2016–2019/2020, themes and pillars are derived from the plan's vision, and global indices are assigned by international organizations to accurately measure the performance against the vision's themes. This allows for comparison with other countries, and it also set targets for progression over time. One or more projects are assigned to the indicators of these global indices. A Multi-Criteria Mathematical Programming Technique (e.g., Goal Programming) is used with multiple goals and priorities where an Optimal Portfolio of Projects is found that satisfied the selection criteria.

A Technique for Securing Big Data Using K-Anonymization With a Hybrid Optimization Algorithm

The recent techniques built on cloud computing for data processing is scalable and secure, which increasingly attracts the infrastructure to support big data applications. This paper proposes an effective anonymization based privacy preservation model using k-anonymization criteria and Grey wolf-Cat Swarm Optimization (GWCSO) for attaining privacy preservation in big data. The anonymization technique is processed by adapting k- anonymization criteria for duplicating k records from the original database. The proposed GWCSO is developed by integrating Grey Wolf Optimizer (GWO) and Cat Swarm Optimization (CSO) for constructing the k-anonymized database, which reveals only the essential details to the end users by hiding the confidential information. The experimental results of the proposed technique are compared with various existing techniques based on the performance metrics, such as Classification accuracy (CA) and Information loss (IL). The experimental results show that the proposed technique attains an improved CA value of 0.005 and IL value of 0.798, respectively.

Optimum Project Portfolio Selection for Developmental Plans Using Goal Programming

In vision-driven development plans, such as the Kuwait Mid-Range Development Plan 2015/2016–2019/2020, themes and pillars are derived from the plan's vision, and global indices are assigned by international organizations to accurately measure the performance against the vision's themes. This allows for comparison with other countries, and it also set targets for progression over time. One or more projects are assigned to the indicators of these global indices. A Multi-Criteria Mathematical Programming Technique (e.g., Goal Programming) is used with multiple goals and priorities where an Optimal Portfolio of Projects is found that satisfied the selection criteria.

A Methodological Approach to Assessment and Reporting of the Model Adequacy in Simulation Studies Abstract

Assessment and Reporting of Model Adequacy is an important step in the simulation modelling process. It stipulates the level of precision and accuracy, which are important features of the model predictions. In an academic research activity, an important step for model development is the process of the identification or accepting whether the model is wrong. The evaluation of the adequacy of developed models is not possible through a single statistical test. This paper delineates a technique to implement model adequacy. A live case is demonstrated on the proposed methodology by evaluating a simulation model which was designed by us to simulate a well-established mathematical model. A step by step methodological approach is delineated in this paper along with a case study of investigation of a simulation model with a mathematical model is used to demonstrate this methodology. The paper concludes with an Algorithm and a flow chart for performing model adequacy for assessing the adequacy of the developed model with existing models.

A technique for securing big data using k-anonymization with a hybrid optimization algorithm

The recent techniques built on cloud computing for data processing is scalable and secure, which increasingly attracts the infrastructure to support big data applications. This paper proposes an effective anonymization based privacy preservation model using k-anonymization criteria and Grey wolf-Cat Swarm Optimization (GWCSO) for attaining privacy preservation in big data. The anonymization technique is processed by adapting k- anonymization criteria for duplicating k records from the original database. The proposed GWCSO is developed by integrating Grey Wolf Optimizer (GWO) and Cat Swarm Optimization (CSO) for constructing the k-anonymized database, which reveals only the essential details to the end users by hiding the confidential information. The experimental results of the proposed technique are compared with various existing techniques based on the performance metrics, such as Classification accuracy (CA) and Information loss (IL). The experimental results show that the proposed technique attains an improved CA value of 0.005 and IL value of 0.798, respectively.

A Methodological Approach to Assessment and Reporting of the Model Adequacy in Simulation Studies

Assessment and Reporting of Model Adequacy is an important step in the simulation modelling process. It stipulates the level of precision and accuracy, which are important features of the model predictions. In an academic research activity, an important step for model development is the process of the identification or accepting whether the model is wrong. The evaluation of the adequacy of developed models is not possible through a single statistical test. This paper delineates a technique to implement model adequacy. A live case is demonstrated on the proposed methodology by evaluating a simulation model which was designed by us to simulate a well-established mathematical model. A step by step methodological approach is delineated in this paper along with a case study of investigation of a simulation model with a mathematical model is used to demonstrate this methodology. The paper concludes with an Algorithm and a flow chart for performing model adequacy for assessing the adequacy of the developed model with existing models.

A Scheduling and Rescheduling Decision Support System for Apparel Manufacturing

The manufacturing environment for apparel is subject to a variety of constraints, stochasticity, and unforeseen events. In order to create an accurate scheduling-system for this environment, these complexities must be considered. This article presents the development and the application of a scheduling and rescheduling decision support system for an apparel manufacturer. Furthermore, the results of applying the proposed system are presented and discussed. The scheduling and rescheduling decision support system presented in this article takes advantage of a variable neighborhood search and Monte Carlo simulation in order to minimize tardiness in the presence of different release times, sequence-based setup times, blocking, and resource constraints. The results show that the quality of the schedules generated by the proposed scheduling and rescheduling decision support system is superior to the current firm’s scheduling practice, which is based on an earliest due date heuristic. Moreover, the percentage of the realized schedule and overall equipment effectiveness were improved.

Bus Transit Network Structure Selection With Multiple Objectives

Bus transportation is the essential mode of public transportation available for intra-district movements in India. The planning of different stages of bus transportation planning is usually done in an ad-hoc manner on the basis of the experience of the operators. For a rational design of the bus transit system, it is essential to take into account the objectives of different interest groups. Selection of an appropriate network structure is an essential part of the planning process. In this paper, a model developed for generating a number of alternative network structures using link deletion concept is presented. One of these alternatives can be selected on the basis of the trade-off between the user and operator objectives. The model has been applied to a case study of bus transit network of Visakhapatnam region in Andhra Pradesh.

Secure And Dynamic Multi Keyword Ranked Search

Nowadays, information storing in third party storage is increased. Outsourcing the data to other storage device or servers which may questioned to the secure environment. However, sensitive data like medical information should need an privacy when it is stored in cloud storage. In this paper, a secure keyword search which provide the resultant data in a encrypted form where the end user can decrypt using the key given to them. It uses the Blowfish to encrypt the data and it also supports the data owner to delete or modify the content of their document. It also ensure accurate relevance score calculation between encrypted index and query vectors.

Supernetwork Representation Formulation of a Multiclass Simultaneous Transportation Equilibrium Model as a Fixed Demand User Equilibrium Problem

A multiclass simultaneous transportation equilibrium model (MSTEM) explicitly distinguishes between different user classes in terms of socioeconomic attributes, trip purpose, pure and combined transportation modes, as well as departure time, all interacting over a physically unique multimodal network. It enhances the prediction process behaviorally by combining the trip generation and departure time choices to trip distribution, modal split, and trip assignment choices in a unified and flexible framework that has many advantages from both supply and demand sides. However, the development of this concept of multiple classes increases the mathematical complexity of travel forecasting models. In this research, the authors reduce this mathematical complexity by using the supernetwork representation formulation of the diagonalized MSTEM as a fixed demand user equilibrium (FDUE) problem.