Integer Batch Scheduling Problems for a Single-Machine to Minimize Total Actual Flow Time

Yusriski, Rinto; Astuti, Budi; Sukoyo,; Samadhi, T.M.A. Ari; Halim, Abdul Hakim

doi:10.1016/j.promfg.2015.07.021

Cited by 7 publications

(3 citation statements)

References 1 publication

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“…There are numerous studies [11][12][13][14][15][16] on batch-scheduling problems, not for heat treatment, but for other batch-machine manufacturing processes (e.g., glass, semiconductor, and aluminum manufacturing). These batch-scheduling problems assume that all jobs in a batch are completed simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Heat Treatment Scheduling for Hot Press Forging Using Data-Driven Models

Kim¹,

Choi²,

Ryu³

2022

Intelligent Automation &Amp; Soft Computing

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Scheduling heat treatment jobs in a hot press forging factory involves forming batches of multiple workpieces for the given furnaces, determining the start time of heating each batch, and sorting out the order of cooling the heated workpieces. Among these, forming batches is particularly difficult because of the various constraints that must be satisfied. This paper proposes an optimization method based on an evolutionary algorithm to search for a heat treatment schedule of maximum productivity with minimum energy cost, satisfying various constraints imposed on the batches. Our method encodes a candidate solution as a permutation of heat treatment jobs and decodes it such that the jobs are grouped into batches satisfying all constraints. Each candidate schedule is evaluated by simulating the heating and cooling processes using cost models for processing time and energy consumption, which are learned from historical process data. Simulation experiments reveal that the schedules built using the proposed method achieve higher productivity with lower energy costs than those built by human experts.

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Section: Related Workmentioning

confidence: 99%

Optimization of Heat Treatment Scheduling for Hot Press Forging Using Data-Driven Models

Kim¹,

Choi²,

Ryu³

2022

Intelligent Automation &Amp; Soft Computing

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“…While the solution in Halim and Ohta [32] does not guarantee to produce an optimal solution, it [32] has inspired and been developed for other flow shop problems, including in [33] and [34]. Suryadhini et al [33] discussed batch scheduling problems assuming two product types in a two-stage flow shop, while Maulidya et al [34] developed a two-stage flow shop model with continuous batch sizes [35]- [37].…”

Section: Introductionmentioning

confidence: 99%

Two-Machine Flow Shop Batch Scheduling Model to Minimize Total Actual Flow Time

Yusriski,

Nasution,

Lukas

et al. 2023

Jurnal Teknik Industri

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This study introduces a scheduling model for a two-machine flow shop batch system to minimize the actual flow time. In this system, two machines are responsible for processing raw materials and producing finished products, with a single bottleneck machine. The entity overseeing the manufacturing process organizes demand units into batches, ensures the accurate and timely arrival of raw materials, and delivers all finished products punctually to meet an expected due date. The study addresses crucial challenges, including determining the optimal number of batches, sizes, and sequences to achieve the specified objective. The analysis adopted an algorithm grounded in the Lagrange relaxation method to tackle these challenges. Moreover, the algorithm is operated by identifying the bottleneck machine as a scheduling reference and determining the appropriate number of batches and sizes. The analysis showed the efficacy of the developed algorithm by using Johnson's rule for making batch sequence decisions through numerical experiments conducted across 1000 cases. The results showed a 1.44% to 4.43% improvement in efficiency compared to previous research, accompanied by a 2–8 times reduction in computational time.

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“…The prediction of cycle times based on learning has many applications. One of the most found applications are optimization techniques for inventory cost [5], optimal batch sizes [6][7][8] and workforce management [9,10]. These applications need a long term prediction.…”

Section: Introductionmentioning

confidence: 99%

Real Time Implementation of Learning-Forgetting Models for Cycle Time Predictions of Manual Assembly Tasks after a Break

et al. 2020

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Industry 4.0 provides a tremendous potential of data from the work floor. For manufacturing companies, these data can be very useful in order to support assembly operators. In literature, a lot of contributions can be found that present models to describe both the learning and forgetting effect of manual assembly operations. In this study, different existing models were compared in order to predict the cycle time after a break. As these models are not created for a real time prediction purpose, some adaptations are presented in order to improve the robustness and efficiency of the models. Results show that the MLFCM (modified learn-forget curve model) and the PID (power integration diffusion) model have the greatest potential. Further research will be performed to test both models and implement contextual factors. In addition, since these models only consider one fixed repetitive task, they don’t target mixed-model assembly operations. The learning and forgetting effect that executing each assembly task has on the other task executions differs based on the job similarity between tasks. Further research opportunities to implement this job similarity are listed.

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Integer Batch Scheduling Problems for a Single-Machine to Minimize Total Actual Flow Time

Cited by 7 publications

References 1 publication

Optimization of Heat Treatment Scheduling for Hot Press Forging Using Data-Driven Models

Optimization of Heat Treatment Scheduling for Hot Press Forging Using Data-Driven Models

Two-Machine Flow Shop Batch Scheduling Model to Minimize Total Actual Flow Time

Real Time Implementation of Learning-Forgetting Models for Cycle Time Predictions of Manual Assembly Tasks after a Break

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