Abstract
Facility layout problem (FLP) is one of the hottest research areas in industrial engineering. A good facility layout can achieve efficient production management, improve production efficiency, and create high economic values. Because FLP is an NP-hard problem, meaning it is impossible to find the optimal solution when problem becomes sufficiently large, various evolutionary algorithms (EAs) have been proposed to find a sub-optimal solution within a reasonable time interval. Recently, a genetic algorithm (GA) was proposed for unequal area FLP (UA-FLP), where the areas of facilities are not identical. More precisely, the GA is an island model based, which is called IMGA. Since EAs are still very time consuming, many efforts have been devoted to how to parallelize various EAs including IMGA. In recent work, Steffen and Dietmar proposed how to parallelize island models of EAs. However, their parallelization approaches are preliminary because they focused mainly on comparing the performances between different parallel architectures. In addition, they used one mathematical function to model the problem. To further investigate on how to parallelize the IMGA by GPU, in this paper we propose multiple parallel algorithms, for each individual step in the IMGA when solving the industrial engineering problem, UA-FLP, and conduct experiments to compare their performances. After integrating better algorithms for all steps into the IMGA, our GPU implementation outperforms the CPU counterpart and the best speedup can be as high as 84.
Highlights
In manufacturing industries, facility layout problems (FLP) are one of the most important issues among the various aspects of manufacturing system management
The methods of island model of genetic algorithm (IMGA) on graphic process units (GPUs) are based on the work reported in the work [26], and a common Weirerstrass function is used to compare the performances between GPU and CPU
We propose multiple parallel algorithms to implement each step of IMGA and compare the performance ratios between them
Summary
Facility layout problems (FLP) are one of the most important issues among the various aspects of manufacturing system management. We pay close attention to how to use GPU to parallelize island model of genetic algorithm (IMGA). Talbi [26] proposed three different general schemes for building efficient island models for GA on GPU Their experiments indicated that GPU computing can speed up the search process, and exploit parallelism to improve the quality of the obtained solutions. The methods of IMGA on GPU are based on the work reported in the work [26], and a common Weirerstrass function is used to compare the performances between GPU and CPU Their parallelization approaches are preliminary because they focused mainly on comparing the performances between different parallel architectures. We propose multiple parallel algorithms, for each individual step in the IMGA when solving UA-FLP, and conduct experiments to compare their performances.
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