Using the analytic hierarchy process and goal programming for information system project selection
Information and Management
A Co-evolutionist Meta-heuristic for the Assignment of the Frequencies in Cellular Networks
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Compaction of Symbolic Layout Using Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Formulating the multi-segment goal programming
Computers and Industrial Engineering
Computers and Industrial Engineering
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In recent years, steering a quality-management system (QMS) has become a key strategic consideration in businesses. Indeed, companies constantly need to optimize their industrial tools to increase their productivity and to permanently improve the effectiveness and efficiency of their systems. To solve such problems, two approaches were developed: the Pareto Analytical-Hierarchy Process (PAHP) and the Multichoice Goal Programming (MCGP) methods. The first integrates the Pareto concept and Analytical-Hierarchy Process (AHP) methods and the second combines the MCGP model with AHP methods. The goal was to determine the best solution while simultaneously verifying multiobjective-optimization functions and satisfying different constraints for a real-world case study. The latter was chosen because it presents a major problem for controlling the quality levels of production lines. A comparative study between the two approaches provides a path for designing a tool for decision support to ensure the effectiveness of a corporate QMS.