|
|
|
|
Search published articles |
|
|
Showing 2 results for Khalili
Khalili, Tavakkoli-Moghaddam, Volume 4, Issue 1 (5-2013)
Abstract
We relax some assumptions of the traditional scheduling problem and suggest an adapted meta-heuristic algorithm to optimize efficient utilization of resources and quick response to demands simultaneously. We intend to bridge the existing gap between theory and real industrial scheduling assumptions (e.g., hot metal rolling industry, chemical and pharmaceutical industries). We adapt and evaluate a well-known algorithm based on electromagnetic science. The motivation behind our proposed meta-heuristic approach has arisen from the attraction-repulsion mechanism of electromagnetic theories in physics. In this basic idea, we desire to bring our search closer to a region with a superior objective function while going away from the region with the inferior objective function in order to move the solution gradually towards optimality. The algorithm is carefully evaluated for its performance against two existing algorithms using multi-objective performance measures and statistical tools. The results show that our proposed solution method outperforms the others.
Miss Farnaz Javadigargari, Dr Hossein Amoozadkhalili, Dr Reza Tavakkoli-Mogaddam, Volume 12, Issue 2 (11-2021)
Abstract
Nowadays, the capability of cloud management suppliers is one of the important advantages for suppliers that can improve the performance and flexibility and reduce costs in companies through easy access to resources. Also, the environmental impacts of suppliers are a significant issue in today’s industrialization and globalization world. This paper analyzes these subjects by fuzzy multi-objective scenario-based stochastic model. Its objective functions are minimizing the total cost, environmental impacts of suppliers, and maximizing the capability of cloud management of suppliers. Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) and Multi-objective Simulated Annealing meta-heuristic (MOSA) are developed to settle this problem. Five computational experiments analyze the performance of the solution algorithms. The results illustrate that the NSGA-II algorithm provides better solutions than the MOSA algorithm for the presented model.
|
|
|
|
|
|