integer linear programming

The oil and gas industry is probably the most important industry in the world. By growing demands of energy, the need for executing oil and gas projects becomes more than ever. Mega projects in this industry have certain characteristic such as being investment intensive, multi objective, owners, investors, vendors and contracts, risk and uncertainties and etc. Nowadays, knowledge-based organizations play important role in oil and gas industry. Due to the expansion and growth of project-oriented knowledge-based organizations, one of the important issues in these organizations is the optimal selection of the project portfolio. The problem is how to choose the optimal project portfolio. In this research you will find how to establish an optimal project portfolio and with respect to organization constraints. At the end, the methodology is applied as a case study in TEC company- an active project-oriented knowledge-based organization in upstream oil and gas industry in Iran.

This study developed and validated a formalized and robust integer linear programming (ILP) model to optimize the lecturer-to-course assignment problem (concerning balancing workload) for a university department that offers engineering programs. Questionnaire surveys with 4 groups of a total of 159 informants (10 lecturers, 1 head of department, 1 program coordinator, and 147 mechanical engineering students) were conducted. Enumeration was used for lecturers, the head of the department, and the program coordinator, whilst convenience sampling was used for students, with a response rate of 60%. A binary integer linear programming (ILP) model was developed by considering workload-related constraints such as class capacity, course contact hours, course credits, and the number of courses per lecturer. The ILP model was implemented in optimization software and the results were validated using the Delphi method. The results demonstrate the robustness and efficiency of the model in balancing workload by objectively (reducing biases) assigning under-utilized lecturers to more courses and over-utilized lecturers to fewer courses, in terms of simultaneously considering other workload-related variables, unlike existing studies. These results were used to instill a timely, formal, and consistent assignment approach that is fair and free from biases. The proposed model contributes to enhancing fairness and hence collective satisfaction of lecturers, program coordinators, and students, given a formalized, consistent, and timesaving assignment approach that considers other workload-related variables other than the number of courses per lecturer. Another contribution lies in a deeper understanding of a comprehensive range of factors that play a role in lecturer-to-course assignments for higher education institutions. Moreover, this study has implications for practice, given that other academic institutions may benefit from this work, in terms of policy considerations.

In this paper, Shortest Path Design Problem (SPDP) in which the path is incident to all cells is considered. The bi-directional path is one of the known types of configuration of networks for Automated Guided Vehi-cles (AGV).To solve this problem, two algorithms are developed. For each algorithm an Integer Linear Pro-gramming (ILP) is determined. The objective functions of both algorithms are to find the shortest path. The path must be connected and incident to all cells at least in one edge or node. A simple Branch-and-Cut ap-proach is used to solve the ILP models. Computational results show that the models easily can solve the prob-lem with less than 45 cells using a commercial ILP solver.