Improved Bounds for Flow Shop Scheduling

Mastrolilli, Monaldo; Svensson, Ola

doi:10.1007/978-3-642-02927-1_56

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…The flow‐shop scheduling problem is a classical NP‐hard combinatorial optimisation problem [28]. It has many practical applications in logistics, industrial and other fields [29].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

TLBO with variable weights applied to shop scheduling problems

Rodrigues

Gomes

2019

CAAI Transactions on Intelligence Technology

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“…The flow‐shop scheduling problem is a classical NP‐hard combinatorial optimisation problem [28]. It has many practical applications in logistics, industrial and other fields [29].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

TLBO with variable weights applied to shop scheduling problems

Rodrigues

Gomes

2019

CAAI Transactions on Intelligence Technology

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“…The problem of scheduling of workloads on heterogeneous processing fabrics (e.g., GPUs, FPGAs, which are becoming the norm in datacenters [1,2]), is at its core an intractable NP-hard problem [3,4]. The current scheduling approaches rely on application-and system-specific heuristics with extensive domain-expert driven tuning of scheduling policies which have to be reinvented on a case-bycase basis (e.g., [5][6][7][8][9][10][11][12][13][14][15][16][17]).…”

Section: Introductionmentioning

confidence: 99%

Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters

Banerjee¹,

Kalbarczyk²,

Iyer³

2019

Preprint

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The problem of scheduling of workloads onto heterogeneous processors (e.g., CPUs, GPUs, FPGAs) is of fundamental importance in modern datacenters. Most current approaches rely on building application/system-specific heuristics that have to be reinvented on a case-by-case basis. This can be prohibitively expensive and is untenable going forward. In this paper, we propose a domain-driven reinforcement learning (RL) model for scheduling that can be broadly applied to a large class of heterogeneous processors. The key novelty of our approach is (i) the RL model; and (ii) the significant reduction of training-data (using domain knowledge) and -time (using sampling based end-to-end gradient propagation). We demonstrate the approach using real world GPU and FPGA accelerated applications to produce scheduling policies that significantly outperform hand-tuned heuristics.Preprint. Under review.

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“…The objective is to minimize the makespan of processing all jobs. On one hand, we can think of this problem as a generalized flowshop (Mastrolilli and Svensson 2011) or a flowshop with jumps (Mastrolilli and Svensson 2009) since a job does not need to be processed on all machines. On the other hand, since we have alternative machine sets for each job, the problem can also be considered a flexible flowshop (Behnke and Geiger 2012).…”

Section: Introductionmentioning

confidence: 99%

Scheduling Methods for Efficient Stamping Operations at an Automotive Company

Gençosman

Begen

Özmutlu

et al. 2016

Production and Operations Management

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We consider scheduling issues at Beyçelik, a Turkish automotive stamping company that uses presses to give shape to metal sheets in order to produce auto parts. The problem concerns the minimization of the total completion time of job orders (i.e., makespan) during a planning horizon. This problem may be classified as a combined generalized flowshop and flexible flowshop problem with special characteristics. We show that the Stamping Scheduling Problem is NP‐Hard. We develop an integer programming‐based method to build realistic and usable schedules. Our results show that the proposed method is able to find higher quality schedules (i.e., shorter makespan values) than both the company's current process and a model from the literature. However, the proposed method has a relatively long run time, which is not practical for the company in situations when a (new) schedule is needed quickly (e.g., when there is a machine breakdown or a rush order). To improve the solution time, we develop a second method that is inspired by decomposition. We show that the second method provides higher‐quality solutions—and in most cases optimal solutions—in a shorter time. We compare the performance of all three methods with the company's schedules. The second method finds a solution in minutes compared to Beyçelik's current process, which takes 28 hours. Further, the makespan values of the second method are about 6.1% shorter than the company's schedules. We estimate that the company can save over €187,000 annually by using the second method. We believe that the models and methods developed in this study can be used in similar companies and industries.

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Improved Bounds for Flow Shop Scheduling

Cited by 4 publications

References 20 publications

TLBO with variable weights applied to shop scheduling problems

TLBO with variable weights applied to shop scheduling problems

Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters

Scheduling Methods for Efficient Stamping Operations at an Automotive Company

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