Considering stockers in reentrant hybrid flow shop scheduling with limited buffer capacity

Lin, Chun-Cheng; Liu, Wan-Yu; Chen, Yu-Hsiang

doi:10.1016/j.cie.2019.106154

Cited by 32 publications

(6 citation statements)

References 48 publications

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“…Experiments on real instances showed [136] generate better solutions than [135]. Lin et al studied reentrant hybrid flow shop scheduling problems considering limited inventory buffers on each workstation and a central stocker [137]. By designing a hybrid harmony search GA (HSSGA) to tackle the problem, experiments showed the manufacturing condition with both inventory buffer and stocker could improve productivity.…”

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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“…The final reward function, denoted as R, is defined as the summation of rewards across K decision moments, as shown in Equation (17).…”

Section: Rewardmentioning

confidence: 99%

“…Kong et al [16] design an improved GA to solve the FFSS problem with the objectives of minimizing the makespan, the total energy consumption, and the costs. To solve the FFSS problem, Lin et al [17] derive a hybrid optimization algorithm, which integrates the harmony search algorithm and the GA, in order to minimize the makespan and the average flow time. To solve the FFSS problem and minimize the total completion time, the total energy consumption, and carbon emissions, Shi et al [18] consider a variable-priority dynamic scheduling optimization algorithm based on the GA. Hasani et al [19] present the non-dominated sorting genetic algorithm (NSGA-II) to solve the multi-stage FFSS problem, with the objective of minimizing the production costs and the total energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Flexible Flow Shop Production Scheduling Problem Based on the Double Deep Q-Network Algorithm

Gong,

Xu,

Sun

et al. 2023

Processes

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In this paper, motivated by the production process of electronic control modules in the digital electronic detonators industry, we study a multi-objective flexible flow shop scheduling problem. The objective is to find a feasible schedule that minimizes both the makespan and the total tardiness. Considering the constraints imposed by the jobs and the machines throughout the manufacturing process, a mixed integer programming model is formulated. By transforming the scheduling problem into a Markov decision process, the agent state features and the actions are designed based on the processing status of the machines and the jobs, along with heuristic rules. Furthermore, a reward function based on the optimization objectives is designed. Based on the deep reinforcement learning algorithm, the Dueling Double Deep Q-Network (D3QN) algorithm is designed to solve the scheduling problem by incorporating the target network, the dueling network, and the experience replay buffer. The D3QN algorithm is compared with heuristic rules, the genetic algorithm (GA), and the optimal solutions generated by Gurobi. The ablation experiments are designed. The experimental results demonstrate the high performance of the D3QN algorithm with the target network and the dueling network proposed in this paper. The scheduling model and the algorithm proposed in this paper can provide theoretical support to make the production plan of electronic control modules reasonable and improve production efficiency.

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“…They minimized the non-processing energy objectives and total weighted tardiness. Lin et al [42], addressed the re-entrant-HFS problem with stockers, and also, they proposed a hybrid harmony search and the genetic algorithm to solve their problem by minimizing mean flow time and the makespan. They considered limited inventory buffer capacity, job permutation, and transfer time between stages.…”

Section: Hybrid Flow Shop With Buffer Capacitymentioning

confidence: 99%

A Mathematical Model for Lot-streaming Hybrid Flow Shop Scheduling Problem by Considering Learning Effect and Buffer Capacity

Ruhbakhsh¹,

Mehdizadeh²,

Adibi³

2022

Scientia Iranica

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Lot streaming, is an effective technique to increase efficiency in a production system by splitting a job into several smaller parts to overlap operations between successive machines in a multi-stage production system. In this paper, a hybrid flow shop scheduling problem with lot streaming, learning effect and buffer's limitation is considered. A bi-objective mathematical model is presented for minimizing the total tardiness and the makespan. To validate the proposed model, the bi-objective model converted to single objective model by weighting method and an example is solved using GAMS software.Since the problem is NP-hard,NSGA-II and NRGA are used to solve the large scale of the problems and the first available machine (FAM) rule is used to assign the jobs to the machines from the first stage onwards in solution representation. Also, Taguchi method is used to tune the algorithms parameters. For evaluating the performance of the algorithms, the results obtained from GAMS compared with outputs of the GA algorithms. Also, 30 instance problems are randomly generated and six indicators are used to compare the algorithms together. After performing the experiments and comparingNSGA-II and NRGAalgorithms with each other based on each index, the appropriate algorithm is selected.

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Considering stockers in reentrant hybrid flow shop scheduling with limited buffer capacity

Cited by 32 publications

References 48 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

Multi-Objective Flexible Flow Shop Production Scheduling Problem Based on the Double Deep Q-Network Algorithm

A Mathematical Model for Lot-streaming Hybrid Flow Shop Scheduling Problem by Considering Learning Effect and Buffer Capacity

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