A Genetic Programming-Based Scheduling Approach for Hybrid Flow Shop With a Batch Processor and Waiting Time Constraint

Qin, Ming; Shi, Zhongshun; Liu, Lingxuan; Shi, Leyuan

doi:10.1109/tase.2019.2947398

Cited by 32 publications

(4 citation statements)

References 30 publications

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“…Heuristics such as the batch first in first out (BFIFO) rule has also been applied to large instances. Qin et al designed dispatching rules based on genetic programming (GP) to examine the HF 2 r j , p − batch → discrete, B, tw j C max [130].…”

Section: Hybrid (Flexible) Flow Shopsmentioning

confidence: 99%

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: Hybrid (Flexible) Flow Shopsmentioning

confidence: 99%

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

Fang,

Cheang,

Lim

2023

Sustainability

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“…Pan et al [13] executed five meta-heuristics are executed to solve the distributed batch flow alignment process shop scheduling problem. Qin et al [14] considered the limited waiting time between batch and discrete processors to develop a learning-based scheduling method through custom genetic programming.…”

Section: Low-carbon Manufacturing Scheduling Optimizationmentioning

confidence: 99%

A Low-Carbon Scheduling Method of Flexible Manufacturing and Crane Transportation Considering Multi-State Collaborative Configuration Based on Hybrid Differential Evolution

Liu,

Xu,

Pan

et al. 2023

Processes

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With increasingly stringent carbon policies, the development of traditional heavy industries with high carbon emissions has been greatly restricted. Manufacturing companies surveyed use multifunctional machining machines and variable speed cranes, as the lack of rational planning results in high energy wastage and low productivity. Reasonable scheduling optimization is an effective way to reduce carbon emissions, which motivates us to work on this research. To reduce the comprehensive energy consumption of the machining process and transportation process in an actual manufacturing environment, this paper addresses a new low-carbon scheduling problem of flexible manufacturing and crane transportation considering multi-state collaborative configuration (LSP-FM&CT-MCC). First, an integrated energy consumption model based on multi-state machining machines and cranes is established to optimize the overall energy efficiency of the production process. Then, a new hybrid differential evolution algorithm and firefly algorithm with collaborative state optimization strategy (DE-FA-CSOS) is proposed to solve the proposed MIP model. In DE-FA-CSOS, the differential evolution algorithm (DE) is used for global search, and the firefly algorithm (FA) is used for local search. The collaborative state optimization strategy (CSOS) is proposed to guide the search direction of the DE-FA algorithm, which greatly improves the performance of the hybrid algorithm. Finally, the practicality and superiority of the solution method are verified by examples. The results show that machining and transportation energy consumption is reduced by 25.17% and 34.52%, respectively. In the context of traditional optimization methods and manual scheduling modes facing failure, the method has a broad application background for manufacturing process optimization in such workshops, which is of guiding significance for promoting the low-carbon development of traditional heavy industry manufacturing.

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“…Due to the complexity of the general case, most studies are done for the restrictive case. 23,[39][40][41][42][43][44][45][46] Unlike theses, Klemmt and Mo¨nch 36 proposed heuristics that minimize the total tardiness for the general case.…”

Section: Introductionmentioning

confidence: 99%

A scheduling mechanism for hybrid flow shops with reworks under general queue time limits

Cho¹,

Kim

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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This study addresses multi-stage hybrid flow shop scheduling in which a job is reworked if the queue time between two arbitrary stages exceeds an upper limit. The problem is to determine the allocations of jobs to machines at each stage and the start times of jobs and rework setups/operations when incurred. A mixed integer programming model is proposed for each of the makespan and the total tardiness measures. Then, because the problem is NP-hard, a scheduling mechanism is proposed that consists of three phases: (a) filtering the jobs to be delayed; (b) searching the jobs to be reworked; and (c) dispatching non-delayed and delayed jobs sequentially. Simulation results show that the mechanism proposed in this study outperforms the conventional dispatching approach in the high rework setup time case for the makespan problem and low/high setup time cases for the tardiness problem. The best priority rules of the mechanism under each of the measures are also reported.

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A Genetic Programming-Based Scheduling Approach for Hybrid Flow Shop With a Batch Processor and Waiting Time Constraint

Cited by 32 publications

References 30 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

A Low-Carbon Scheduling Method of Flexible Manufacturing and Crane Transportation Considering Multi-State Collaborative Configuration Based on Hybrid Differential Evolution

A scheduling mechanism for hybrid flow shops with reworks under general queue time limits

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