The Resource Constrained Project Scheduling Problem, which is considered to be difficult to tackle even for small instances, is a well-known scheduling problem in the operations research domain. To solve the problem we have proposed a parallel Tabu Search algorithm to find high quality solutions in a reasonable time. We show that our parallel Tabu Search algorithm for graphics cards (GPUs) outperforms other existing Tabu Search approaches in terms of quality of solutions and the number of evaluated schedules per second. Moreover, the algorithm for graphics cards is about 10.5 42.7 times faster (J90 benchmark instances) than the optimized parallel sequential algorithm for the Central Processing Unit (CPU). The same quality of solutions is achieved up to 5.4 22 times faster in comparison to the parallel sequential CPU algorithm respectively. The advantages of the GPU version arise from the sophisticated data-structures and their suitable placement in the device memory, tailor-made methods, and last but not least the effective communication scheme. The parallel Tabu Search for the Resource Constrained Project Scheduling Problem.To accelerate the algorithm the CUDA optimized version was implemented.A new resources evaluation algorithm was proposed.Promising results in terms of performance and quality of solutions.
Relatively many authors try to use a GPU for solving combinatorial problems. For example, Czapi ' n ski and Barnes @cite_22 implemented a GPU version of TS to solve the Flowshop Scheduling Problem (FSP). The success of the implementation illustrates an achieved speedup against the CPU version. The GPU version was up to 89.01 times faster than the CPU (Intel Xeon 3.0 GHz, 2 GB memory, Nvidia Tesla C1060 GPU). Nevertheless, the quality of solutions was not investigated.
@cite_22
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The introduction of NVidia's powerful Tesla GPU hardware and Compute Unified Device Architecture (CUDA) platform enable many-core parallel programming. As a result, existing algorithms implemented on a GPU can run many times faster than on modern CPUs. Relatively little research has been done so far on GPU implementations of discrete optimisation algorithms. In this paper, two approaches to parallel GPU evaluation of the Permutation Flowshop Scheduling Problem, with makespan and total flowtime criteria, are proposed. These methods can be employed in most population-based algorithms, e.g. genetic algorithms, Ant Colony Optimisation, Particle Swarm Optimisation, and Tabu Search. Extensive computational experiments, on Tabu Search for Flowshop with both criteria, followed by statistical analysis, confirm great computational capabilities of GPU hardware. A GPU implementation of Tabu Search runs up to 89 times faster than its CPU counterpart.