A branch and bound and parallel genetic algorithm for the job shop scheduling problem with blocking

AitZai, Abdelhakim; Benmedjdoub, Brahim; Boudhar, Mourad

doi:10.1504/ijor.2012.047094

Cited by 17 publications

(6 citation statements)

References 32 publications

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“…The BJSS is a version of the classical JSSP with no intermediate buffers, where a job has to wait on the current machine until the next machine becomes available for processing. This problem can be modelled as an alternative graph representation introduced by Mascis et al [1] which is a generalization of the disjunctive graph of Roy and Sussman [4]. This model can be defined as G = (N, F, A).…”

Section: Problem Formulationmentioning

confidence: 99%

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Hybrid multi-core CPU and GPU-based B&B approaches for the blocking job shop scheduling problem

Dabah

Bendjoudi

AitZai

et al. 2018

Journal of Parallel and Distributed Computing

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The Branch and Bound algorithm (B&B) is a well known method for solving optimally Combinatorial Optimization Problems (COPs). This method is based on intelligent enumeration of all feasible solutions which reduce considerably the search space. Nevertheless, it remains inefficient when using the sequential approach to deal with large problem instances due to its huge resolutions time. However, the execution time can be reduced considerably by using parallel computing architectures. With the huge evolution of the multi-cores CPUs and GPUs, It is quite hard to design schemes that efficiently exploit the different hardware architectures simultaneously. As a result, most of the existing works focus on exploiting one hardware architecture at a time. In this paper, we propose five parallel approaches to accelerate the B&B execution time using Multi and Many-core systems at the same time. Our goal is to solve optimally the Blocking Job Shop Scheduling problem (BJSS) which is one of the hardest scheduling problem. The first proposed approach is a multi-search parallelization based on Master/Worker paradigm, exploiting the Multi-Core CPU-processors. The second and the third approaches represent a GPU based parallelization schemes having different level of parallelism and GPU occupancy. The forth and fifth approaches represent a hybridization between the Muli-core approach and the GPU based parallelization approaches. The goal of this hybridization is to benefit from the power of both the CPU-cores and the GPU at the same time. This hybridization is based on concurrent kernels execution provided by Nvidia Multi process Service (MPS) which allows multiple host processes (Master and workers) to use at the same time the GPU to launch their kernels in order to accelerate the bounding of one or several nodes at a time. Experiments using the well known Taillard instances confirm the efficiency of our proposals and show a relative speedup of 160x as compared to an optimized sequential B&B algorithm.

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Section: Problem Formulationmentioning

confidence: 99%

“…Ait Zai et al [1], proposed an original B&B method based on graph theory to solve the BJSS problem. The idea of its branching scheme relies on the implicit enumeration of all possible combinations on a given machine.…”

Section: Related Workmentioning

confidence: 99%

Hybrid multi-core CPU and GPU-based B&B approaches for the blocking job shop scheduling problem

Dabah

Bendjoudi

AitZai

et al. 2018

Journal of Parallel and Distributed Computing

Self Cite

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“…With a population size 1056 and a limited total execution time 300s, the master-slave GA using GPU could get maximum 15 times more explored solutions than the GA using CPU. Moreover, a related earlier work was introduced by AitZai in [15]. In order to improve the solution of job shop scheduling problems, Somani et al [16] imposed a topological sorting step to the GA before the fitness value calculation, which was used to generate the topological sequence of directed acyclic graph.…”

Section: )Job Shop Scheduling Problemsmentioning

confidence: 99%

A Survey on Parallel Genetic Algorithms for Shop Scheduling Problems

Luo

Baz

2018

2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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There have been extensive works dealing with genetic algorithms (GAs) for seeking optimal solutions of shop scheduling problems. Due to the NP hardness, the time cost is always heavy. With the development of high performance computing (HPC) in last decades, the interest has been focused on parallel GAs for shop scheduling problems. In this paper, we present the state of the art with respect to the recent works on solving shop scheduling problems using parallel GAs. It showcases the most representative publications in this field by the categorization of parallel GAs and analyzes their designs based on the frameworks.

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“…Based on this model, they solve optimally the 10 × 10 benchmark instances of this problem. Ait Zai et al [1], proposed an original B&B method based on graph theory to solve the BJSS. The idea of his branching scheme relies on the implicit enumeration of all possible combinations on a given machine.…”

Section: B Related Workmentioning

confidence: 99%

Multi and many-core parallel B&B approaches for the Blocking Job Shop Scheduling problem

Dabah

Bendjoudi

AitZai

et al. 2016

2016 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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In this paper, we propose three approaches to accelerate the B&B execution time using Multi and Manycore systems to solve the NP-hard Blocking Job Shop Scheduling problem (BJSS). The first approach is based on Master/Worker paradigm where the workers independently explore the branches sent by the master. The second approach is a node-based parallelization that does not change the design of the B&B algorithm, except that the bounding process is faster since it is calculated in parallel using several threads organized in one GPU block. The third approach is a Multi-Core CPU/GPU hybridization that benefits from the power of both the CPU-cores and the GPU at the same time. This hybridization is based on concurrent kernels execution provided by Nvidia Multi process Service (MPS) i.e. each host process (Master or Worker) launches his own kernel to accelerate the bounding process on GPU. The obtained results using Taillard instances confirm the efficiency of our proposals. The first two approaches are respectively three and eighteen times faster compared to the sequential version. The results of the hybrid approach show a relative speedup over ninety times as compared to the sequential approach and therefore prove the advantage of using both the CPU-cores and the GPU at the same time. Keywords-Job shop; blocking with swap; GPGPU; Multi-core CPU; parallel computing; Branch-and-Bound.

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A branch and bound and parallel genetic algorithm for the job shop scheduling problem with blocking

Cited by 17 publications

References 32 publications

Hybrid multi-core CPU and GPU-based B&B approaches for the blocking job shop scheduling problem

Hybrid multi-core CPU and GPU-based B&B approaches for the blocking job shop scheduling problem

A Survey on Parallel Genetic Algorithms for Shop Scheduling Problems

Multi and many-core parallel B&B approaches for the Blocking Job Shop Scheduling problem

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