Makespan minimization for the m-machine ordered flow shop scheduling problem

Khatami, Mostafa; Salehipour, Amir; Hwang, Feng-Jang

doi:10.1016/j.cor.2019.06.012

Cited by 20 publications

(14 citation statements)

References 56 publications

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“…The pyramidal-shaped property of the ordered flow-shop problem significantly reduces the size of the set of all candidate feasible solutions, more precisely from n! to 2 n−1 as discussed in [6]. Therefore, we utilize this property in the development of the neighborhoods for the proposed R&S algorithm.…”

Section: Algorithmmentioning

confidence: 99%

“…A pyramidal-shaped sequence consists of two sub-sequences, where in the first sub-sequence the jobs are ordered by the SPT rule, and in the second sub-sequence the jobs are sequenced in the LPT order. Recently, [6] utilized the pyramidal-shaped property and proposed two heuristics and an iterated local search (ILS) algorithm for the ordered flow-shop problem. For a detailed review of earlier studies, we refer the interested reader to [4].…”

Section: Introductionmentioning

confidence: 99%

“…For a detailed review of earlier studies, we refer the interested reader to [4]. [6] provided comprehensive computational comparisons of the state-of-the-art methods.…”

Section: Introductionmentioning

confidence: 99%

“…The ILS algorithm of [6] is the only published metaheuristic for the ordered flow-shop problem with the makespan minimization. The main contribution of this study is to propose a new algorithm for the problem that uses an exact solver within a heuristic framework, and for that purpose we propose the relax-and-solve (R&S) matheuristic.…”

Section: Introductionmentioning

confidence: 99%

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A Relax-and-Solve Algorithm for the Ordered Flow-Shop Scheduling Problem

Khatami

Salehipour

2020

2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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In the ordered flow-shop scheduling problem the processing times follow specific structures. We propose a relax-and-solve matheuristic for the ordered flow-shop problem to minimize the makespan, which is proven to be NPhard. We compare the performance of our method and that of the state-of-the-art methods, and show that the proposed method is capable of reporting new best solutions for a large number of instances, and has the average gap of as low as 0.046% from the best known solutions.

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Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…For a detailed review of earlier studies, we refer the interested reader to [4]. [6] provided comprehensive computational comparisons of the state-of-the-art methods.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Relax-and-Solve Algorithm for the Ordered Flow-Shop Scheduling Problem

Khatami

Salehipour

2020

2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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“…To solve the NP-hard problem, many hybrid meta-heuristics have been proposed [ 2 , 3 ]. For the PFSP, its objective is to find a good job sequence to minimize makespan [ 4 , 5 , 6 ], flow time [ 7 , 8 , 9 , 10 ], tardiness [ 11 , 12 , 13 , 14 ], multiple objective [ 15 , 16 , 17 , 18 , 19 , 20 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

Yang

Wang

2021

Sensors

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Dynamic scheduling problems have been receiving increasing attention in recent years due to their practical implications. To realize real-time and the intelligent decision-making of dynamic scheduling, we studied dynamic permutation flowshop scheduling problem (PFSP) with new job arrival using deep reinforcement learning (DRL). A system architecture for solving dynamic PFSP using DRL is proposed, and the mathematical model to minimize total tardiness cost is established. Additionally, the intelligent scheduling system based on DRL is modeled, with state features, actions, and reward designed. Moreover, the advantage actor-critic (A2C) algorithm is adapted to train the scheduling agent. The learning curve indicates that the scheduling agent learned to generate better solutions efficiently during training. Extensive experiments are carried out to compare the A2C-based scheduling agent with every single action, other DRL algorithms, and meta-heuristics. The results show the well performance of the A2C-based scheduling agent considering solution quality, CPU times, and generalization. Notably, the trained agent generates a scheduling action only in 2.16 ms on average, which is almost instantaneous and can be used for real-time scheduling. Our work can help to build a self-learning, real-time optimizing, and intelligent decision-making scheduling system.

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