A Performance Analysis of Memetic Algorithm, Genetic Algorithm and Simulated Annealing in Production System Optimization
Alireza Noroziroshan1, Shaghayegh Habibi2
1Alireza Noroziroshan, Department of Industrial, Manufacturing & System Engineering, The University of Texas at Arlington, UTA Box 19017, Arlington, Texas 76019, USA.
2Shaghayegh Habibi, Faculty of Entrepreneurship, The University of Tehran, Tehran, Iran.
Manuscript received on August 16, 2015. | Revised Manuscript received on August 28, 2015. | Manuscript published on September 05, 2015. | PP: 24-30 | Volume-5 Issue-4, September 2015 . | Retrieval Number: D2682095415/2015©BEIESP
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©The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/

Abstract: Researchers laid the foundation of evolutionary algorithms in the late 60s and since then, heuristic algorithms have been widely applied to several complex scheduling and sequencing problems during the recent studies. In this paper, memetic algorithm (MA), genetic algorithm (GA) and simulated annealing (SA) are applied to a complex sequencing problem. The problem under study concerns about sequencing problem in mixed-shop floor environment. The main objective is to minimize the overall make-span of multiple mixed-model assembly lines by finding the best job sequence and allocation. The superiority of MA’s performance is proved by evaluating standard deviation, optimal solution and mean value of obtained solutions.
Keywords: Genetic Algorithm, Make-span, Memetic Algorithm, Simulated Annealing.