Comparisons of some improving strategies on MOPSO for multi-objective (r, Q) inventory system |
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| Authors: | H Moslemi M Zandieh |
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| Affiliation: | 1. Department of Mechanical and Industrial Engineering, Qazvin Islamic Azad University, Qazvin, Iran;2. Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran;1. Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, University of Malaya, Kuala Lumpur, Malaysia;2. Department of IT, Faculty of Engineering & Technology, SRM University, 603203 Chennai, India;3. UM Power Energy Dedicated Advanced Centre (UMPEDAC), Level 4, Wisma R&D UM, Jalan Pantai Baharu, 59990 Kuala Lumpur, Malaysia;4. Department of Engineering, Design and Manufacture, University of Malaya, 50603 Kuala Lumpur, Malaysia;1. Lappeenranta University of Technology, Skinnarilankatu 34, 53850 Lappeenranta, Finland;2. Department of Renewable Energies, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran;1. Department of Mechanical Engineering, Sistan and Baluchestan University, Zahedan, Iran;2. Department of Mechanical Engineering, Bozorgmehr University of Qaenat, Qaen, Iran;3. Department of Telecommunications Engineering, Sistan and Baluchestan University, Zahedan, Iran;1. Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran;2. Department of Civil Engineering and Center for Environmental Resource Management, University of Texas at El Paso, El Paso, USA;3. Centre for Environmental Policy, Imperial College London, London, UK |
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| Abstract: | This paper presents comparisons of some recent improving strategies on multi-objective particle swarm optimization (MOPSO) algorithm which is based on Pareto dominance for handling multiple objective in continuous review stochastic inventory control system. The complexity of considering conflict objectives such as cost minimization and service level maximization in the real-world inventory control problem needs to employ more exact optimizers generating more diverse and better non-dominated solutions of a reorder point and order size system. At first, we apply the original MOPSO employed for the multi-objective inventory control problem. Then we incorporate the mutation operator to maintain diversity in the swarm and explore all the search space into the MOPSO. Next we change the leader selection strategy used that called geographically-based system (Grids) and instead of that, crowding distance factor is also applied to select the global optimal particle as a leader. Also we use ε-dominance concept to bound archive size and maintain more diversity and convergence in the MOPSO for optimizing the inventory control problem. Finally, the MOPSO algorithms created using these strategies are evaluated and compared with each other in terms of some performance metrics taken from the literature. The results indicate that these strategies have significant influences on computational time, convergence, and diversity of generated Pareto optimal solutions. |
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