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

Khatami, Mostafa; Salehipour, Amir

doi:10.1109/ieem45057.2020.9309970

Cited by 2 publications

(2 citation statements)

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“…In this section, we propose an R&S matheuristic algorithm for solving problem

1|(a_j, L_j, b_j)|C_{\max }

. The R&S has been shown to deliver quality solutions for challenging scheduling problems (Ahmadian et al., 2020; Khatami and Salehipour, 2020; Ahmadian and Salehipour, 2021; Ahmadian et al., 2021; Ahmadian and Salehipour, 2022). The operation of the R&S algorithm is similar to that of the fix‐and‐optimize algorithm of Helber and Sahling (2010).…”

Section: The Solution Methodsmentioning

confidence: 99%

The coupled task scheduling problem: an improved mathematical program and a new solution algorithm

Khatami

Salehipour

2022

Int Trans Operational Res

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The general single machine coupled task scheduling problem with the objective function of minimizing the makespan, which is strongly NP‐hard, aims to schedule a set of coupled task jobs on one machine such that the completion time of the last job is minimized. We propose a new mixed‐integer program (MIP) for the problem. We also propose a relax‐and‐solve (R&S) matheuristic algorithm as the solution method. We show that the new MIP outperforms the available models and improves the quality of solutions. Also, the proposed MIP significantly improves the average gap to the best known feasible solution of an existing binary search algorithm. We show that our R&S matheuristic produces new best solutions for almost 50% of the instances.

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“…In this section, we propose an R&S matheuristic algorithm for solving problem

1|(a_j, L_j, b_j)|C_{\max }

Section: The Solution Methodsmentioning

confidence: 99%

The coupled task scheduling problem: an improved mathematical program and a new solution algorithm

Khatami

Salehipour

2022

Int Trans Operational Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…Firstly, we evaluate our BDS on a set of benchmarks [28] consisting of 8 sub-datasets, as shown in Table 2. The job numbers range from 100 to 800.…”

Section: Setting Upmentioning

confidence: 99%

Bilevel Learning Model Towards Industrial Scheduling

Li¹,

Zhen²,

Yuan³

et al. 2020

Preprint

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Automatic industrial scheduling, aiming at optimizing the sequence of jobs over limited resources, is widely needed in manufacturing industries. However, existing scheduling systems heavily rely on heuristic algorithms, which either generate ineffective solutions or compute inefficiently when job scale increases. Thus, it is of great importance to develop new large-scale algorithms that are not only efficient and effective, but also capable of satisfying complex constraints in practice. In this paper, we propose a Bilevel Deep reinforcement learning Scheduler, BDS, in which the higher level is responsible for exploring an initial global sequence, whereas the lower level is aiming at exploitation for partial sequence refinements, and the two levels are connected by a slidingwindow sampling mechanism. In the implementation, a Double Deep Q Network (DDQN) is used in the upper level and Graph Pointer Network (GPN) lies within the lower level. After the theoretical guarantee for the convergence of BDS, we evaluate it in an industrial automatic warehouse scenario, with job number up to 5000 in each production line. It is shown that our proposed BDS significantly outperforms two most used heuristics, three strong deep networks, and another bilevel baseline approach. In particular, compared with the most used greedy-based heuristic algorithm in real world which takes nearly an hour, our BDS can decrease the makespan by 27.5%, 28.6% and 22.1% for 3 largest datasets respectively, with computational time less than 200 seconds.

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

Cited by 2 publications

References 10 publications

The coupled task scheduling problem: an improved mathematical program and a new solution algorithm

The coupled task scheduling problem: an improved mathematical program and a new solution algorithm

Bilevel Learning Model Towards Industrial Scheduling

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