Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function |
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| Authors: | Pandian Vasant Nader Barsoum |
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| Affiliation: | 1. Electrical & Electronic Engineering Department, University Technology Petronas, Malaysia;2. Department of Electrical Engineering, Curtin University of Technology, Malaysia;1. School of Resources and Safety Engineering, Central South University, Changsha 410083, China;2. Hunan Key Lab of Resources Exploitation and Hazard Control for Deep Metal Mines, Changsha 410083, China;3. Department of Mining and Materials Engineering, McGill University, Montreal H3A 2A7, Canada;1. Department of Mathematics, National Institute of Technology, Durgapur 713209, India;2. Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapur 721102, India;1. Department of Knowledge-based Mathematical Systems/Fuzzy Logic Laboratory Linz-Hagenberg, Johannes Kepler University of Linz, Austria;2. Institute for Electrical Drives and Power Electronics, Johannes Kepler University of Linz, Austria;3. ACCM, Austrian Center of Competence in Mechatronics, Linz, Austria;1. Signal Processing and Systems, Eindhoven University of Technology, Den Dolech 2, Eindhoven, The Netherlands;2. Bloom Technologies, Agoralaan Building Abis 2.13, 3590 Diepenbeek, Belgium;3. imec The Netherlands, High Tech Campus 31, 5656 AE Eindhoven, The Netherlands;4. Chair of Sensor Technology, University of Passau, Innstrasse 41, 94032 Passau, Germany;1. South Asian University, New Delhi, India;2. Liverpool Hope University, UK;1. Department of Mechanical Engineering, IIT (ISM) Dhanbad, India;2. Department of Mechanical Engineering, NIT Silchar, India;3. Department of Mechanical Engineering, NIT Agartala, India |
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| Abstract: | Many engineering, science, information technology and management optimization problems can be considered as non-linear programming real-world problems where all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research paper is to solve non-linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers, which was represented by logistic membership functions using the hybrid evolutionary optimization approach. To explore the applicability of the present study, a numerical example is considered to determine the production planning for the decision variables and profit of the company. |
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