A two-stage flow-shop scheduling problem with incompatible job families and limited waiting time

Li, Yonglin; Dai, Zhenhua

doi:10.1080/0305215x.2019.1593974

Cited by 13 publications

(3 citation statements)

References 48 publications

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“…The study conducted by Wang et al 9 concentrates on achieving both makespan and total energy consumption in a twostage hybrid flow shop where parallel machines exist in both the first and second stages. Li et al 10 addresses the issue of limited waiting time in a two-stage hybrid flow shop problem and proposes a hybrid membrane computing metaheuristic to minimize makespan. The research by Fan et al 11 aims to simultaneously minimize the arrival rate of different parts at the assembly stage and the on-time delivery rate in a two-stage hybrid flow shop scheduling problem.…”

Section: Literature Reviewmentioning

confidence: 99%

A Q-learning Algorithm for Two-Stage Hybrid Flow Shop Scheduling

Lu,

Liu,

Zhang

2024

Preprint

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With the increasing demand for customization, flow shop scheduling tends to process multi-variety small batch products. Thus, there are frequent switches between batches. Frequent switchovers between different sets are required during batch changeover, where machine setups may be necessary. This paper investigates scheduling optimization and work-in-process inventory optimization problems in a hybrid flow shop with rolling processing requirements. Developing continuous processing conditions to meet rolling processing requirements presents a significant challenge. In this work, continuous processing conditions are derived. Subsequently, a linear programming model is designed to find an optimal solution for this scheduling problem. Formulas for calculating work-in-process inventory are presented. To enhance work-in-process storage, a greedy algorithm with bubble sort and a Q-learning algorithm with a modified ε-policy are proposed to find an optimized product sequence and reduce work-in-process inventory, respectively. Numerical experiments evaluate the effectiveness and efficiency of the proposed methods.

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Section: Literature Reviewmentioning

confidence: 99%

A Q-learning Algorithm for Two-Stage Hybrid Flow Shop Scheduling

Lu,

Liu,

Zhang

2024

Preprint

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“…In addition, a case in which several jobs can skip the first stage was considered in [33], and mathematical properties for the problem were proposed. In addition, for a problem with a batch machine followed by a discrete machine, a hybrid membrane computing metaheuristic algorithm was developed in [34] to minimize the makespan. Additionally, there are recent studies considering flow shop problems with various scheduling requirements as well as limited waiting times [35][36][37][38][39][40][41].…”

Section: Previous Studiesmentioning

confidence: 99%

Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times

2021

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This study considers a scheduling problem for a flow shop with urgent jobs and limited waiting times. The urgent jobs and limited waiting times are major considerations for scheduling in semiconductor manufacturing systems. The objective function is to minimize a weighted sum of total tardiness of urgent jobs and the makespan of normal jobs. This problem is formulated in mixed integer programming (MIP). By using a commercial optimization solver, the MIP can be used to find an optimal solution. However, because this problem is proved to be NP-hard, solving to optimality requires a significantly long computation time for a practical size problem. Therefore, this study adopts metaheuristic algorithms to obtain a good solution quickly. To complete this, two metaheuristic algorithms (an iterated greedy algorithm and a simulated annealing algorithm) are proposed, and a series of computational experiments were performed to examine the effectiveness and efficiency of the proposed algorithms.

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“…Also, Wu et al 25 proposed a simulation-based control policy that minimizes work-in-process and rework rate for the problem in which a job is reworked if the queue time of the job between two stages exceeds a limit. See Su, 26 Ham et al, 27 Yu et al, 28 Wang et al, 29 Lee, 30 Han and Lee, 31 Jeong et al, 32 and Li and Dai 33 for other studies on two-stage flow shops. Also, see Chen and Yang, 34 Ham, 35 Klemmt and Mo¨nch, 36 Wang et al, 37 and Kim and Lee 38 for extensions to multi-stage flow shops.…”

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 two-stage flow-shop scheduling problem with incompatible job families and limited waiting time

Cited by 13 publications

References 48 publications

A Q-learning Algorithm for Two-Stage Hybrid Flow Shop Scheduling

A Q-learning Algorithm for Two-Stage Hybrid Flow Shop Scheduling

Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times

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

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