An improved NEH heuristic to minimize makespan for flow shop scheduling problems

Sharma, Meenakshi; Sharma, Manisha; Sharma, Sameer

doi:10.5267/j.dsl.2021.2.006

Cited by 12 publications

(13 citation statements)

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“…The iterated greedy [27] consisted of improved initialization, iterative construction, and destruction procedures, along with a local search. Sharma et al [20] applied a tie-breaking rule to Nawaz-Enscore-Ham (NEH) heuristic. For the large-scale PFSS problems, the heuristics could still hardly balance the solution accuracy and runtime efficiency: random search and iterated local search obtained poor makespans, while iterated greedy and NEH algorithms were exceedingly timeconsuming.…”

Section: Related Workmentioning

confidence: 99%

“…During our model training using IL, we choose the stateof-the-art NEH heuristic [20] as the expert method. The mathematical model as formulated in the subsection III-B and solved by branch-and-bound method [47] is another ideal option as the expert method, due to its exact solutions.…”

Section: Expert-driven Imitation Learningmentioning

confidence: 99%

“…Due to the limitations, we would rather choose the NEH heuristic as the expert method. Table I shows the performance comparisons between the mathematical model [19] and the NEH algorithm [20] as one of the state-of-the-art heuristics. For each method, we randomly generate 100 instances following Gamma distribution for testing.…”

Section: Expert-driven Imitation Learningmentioning

confidence: 99%

“…From the results, the computational time of the mathematical model is much slower than the NEH algorithm, while their makespan gaps are not much too wide. Considering the great amount of training and testing instances, even some of them are large-scale, we decide to choose the state-of-the-art heuristics NEH algorithm [20] instead of the exact mathematical models as the expert method.…”

Section: Expert-driven Imitation Learningmentioning

confidence: 99%

“…However, it takes a tremendous amount of time to solve due to the NP-hardness of the PFSS. The heuristics [20] allow getting a feasible or sometimes quasi-optimal solution within a limited time but still encounter with the trade-off between the solution effectiveness and computational efficiency. Motivations.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning

Li¹,

Liang²,

Zhu³

et al. 2022

Preprint

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The permutation flow shop scheduling (PFSS), aiming at finding the optimal permutation of jobs, is widely used in manufacturing systems. When solving the large-scale PFSS problems, traditional optimization algorithms such as heuristics could hardly meet the demands of both solution accuracy and computational efficiency. Thus learning-based methods have recently garnered more attention. Some work attempts to solve the problems by reinforcement learning methods, which suffer from slow convergence issues during training and are still not accurate enough regarding the solutions. To that end, we train the model via expert-driven imitation learning, which accelerates the convergence more stably and accurately. Moreover, in order to extract better feature representations of input jobs, we incorporate the graph structure as the encoder. The extensive experiments reveal that our proposed model obtains significant promotion and presents excellent generalizability in large-scale problems with up to 1000 jobs. Compared to the state-ofthe-art reinforcement learning method, our model's network parameters are reduced to only 37% of theirs, and the solution gap of our model towards the expert solutions decreases from 6.8% to 1.3% on average. The source code is available at: https://github.com/longkang1/IL_PFSS

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Section: Related Workmentioning

confidence: 99%

Section: Expert-driven Imitation Learningmentioning

confidence: 99%