Minimising makespan for single burn-in oven scheduling problems using ACO+DP approach

Shao, Hao; Chen, Hua ping

doi:10.1504/ijmr.2010.033467

Cited by 2 publications

(6 citation statements)

References 24 publications

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“…For example, both Refs. [40,41] utilized the ACO to deal with the 1 p − batch, r j , a j , B C max . Hence, it is suggested that a set of benchmark problems could be established so that the performance of different methods can be compared.…”

Section: New Methodologies Orientedmentioning

confidence: 99%

“…When jobs have unequal release times, a machine could be deliberately left idle as waiting for impending jobs to build fuller batches may bring benefits. The 1 p − batch, r j , a j , B C max was studied in [40][41][42]. Shao and Chen [40] applied an ACO algorithm to generate job sequences and a DP algorithm to form batches, in which the ACO uses information of job processing and release times to update pheromone trails.…”

Section: Batch Machine Scheduling Problemsmentioning

confidence: 99%

“…The 1 p − batch, r j , a j , B C max was studied in [40][41][42]. Shao and Chen [40] applied an ACO algorithm to generate job sequences and a DP algorithm to form batches, in which the ACO uses information of job processing and release times to update pheromone trails. By extending the WRB definition, Xu et al [41] defined the waste and idle space (WIS), which includes the waste space from non-identical job sizes or processing times and the idle space from unequal job release times.…”

Section: Batch Machine Scheduling Problemsmentioning

confidence: 99%

“…The other exact solution technique is the DP. Shao and Chen tackled the 1 p − batch, r j , a j , B C max with a hybrid method that uses an ACO to generate job sequences and a DP to form batches [40]. Yao et al proposed DP algorithms for the F 2 r j , p − batch → discrete, B, tw j , (incompatible) C max and proved their related time complexity [119].…”

Section: Deterministic Scheduling Problemsmentioning

confidence: 99%

See 3 more Smart Citations

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Fang,

Cheang,

Lim

2023

Sustainability

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Machine scheduling problems associated with semiconductor manufacturing operations (SMOs) are one of the major research topics in the scheduling literature. Lots of papers have dealt with different variants of SMOs’ scheduling problems, which are generally difficult to tackle theoretically and computationally. In this paper, the single machine, parallel machines, flow shops, and job shops scheduling problems from SMOs have been reviewed, based on different processing constraints, e.g., batch processing, auxiliary resources, incompatible job families, and reentrant flow, etc., with the cycle time, flow time, and throughput-related performance measures. Given the vast and diverse nature of the current literature, it is urgently needed to make a systematic survey in order to identify the important research problems, research trends, and the progress of the related solution methods, as well as clarify future research perspectives. We hope the findings and observations could provide some insights to the researchers and practitioners in this domain.

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Section: New Methodologies Orientedmentioning

confidence: 99%

Section: Batch Machine Scheduling Problemsmentioning

confidence: 99%

Section: Batch Machine Scheduling Problemsmentioning

confidence: 99%

Section: Deterministic Scheduling Problemsmentioning

confidence: 99%

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Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Fang,

Cheang,

Lim

2023

Sustainability

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“…The research on dynamic dispatching includes the composite priority dispatching strategy in feeding control (Wang et al 2007), a hybrid ACO approach for a BPM (Guo et al 2010), dynamic bottleneck dispatching (DBD, Zhang et al 2009), the heuristic dispatching rules forecasting the movement of jobs to reduce waiting time (Solomon et al 2002;Wang 2009), the ACO combining dynamic programming method to minimizing the makespan (Shao 2010), and ACO-based predictive analytics for case-oriented (Lakshmanan et al 2011), dispatching rules considering machines and emergency jobs (Li et al 2011) as well as adaptive learning in scheduling model (Noroozi et al 2013) in the aspect of production line dispatching rules.…”

Section: Introductionmentioning

confidence: 99%

Learning-based adaptive dispatching method for batch processing machines

Chen

et al. 2013

2013 Winter Simulations Conference (WSC)

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This study aims to solve the scheduling problem of batch processing machines (BPMs) in semiconductor manufacturing by using a learning-based adaptive dispatching method (LBADM). First, an adaptive ant system algorithm (AAS) is proposed to solve the scheduling problem of BPMs according to their characteristics. Then AAS generates a lot of solutions for the jobs with different distribution of arrival time and due date. These solutions are taken as learning samples. Second, we analyze influencing factors by sample learning method from those solutions. With the help of linear regression, the coefficients of influencing factors can be calculated to build a dynamic dispatching rule adaptive to running environments. Finally, simulation results based on a Minifab model show that the proposed method is better than traditional ways (such as FIFO and EDD with maximum batchsize) with lower makespan and weighted tardiness.

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Minimising makespan for single burn-in oven scheduling problems using ACO+DP approach

Cited by 2 publications

References 24 publications

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Learning-based adaptive dispatching method for batch processing machines

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