Minimization of maximum lateness in a flowshop learning effect scheduling with release dates

Bai, Danyu; Bai, Xiaoyuan; Yang, Jie; Zhang, Xingong; Ren, Tao; Xie, Chenxi; Liu, Bingqian

doi:10.1016/j.cie.2021.107309

Cited by 16 publications

(5 citation statements)

References 27 publications

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“…Modeling has mainly used mixed-integer linear programming (MILP), mixed-integer programming (MIP), mixed-integer non-linear programming (MINLP) or fuzzy mixed-integer non-linear programming (FMINLP) [94]. In general, the solvers used to deal with these models are CPLEX (e.g., Bai et al [95]), GAMS (e.g., Eren [96]) and LINGO (e.g., Ghodratnama et al [97]). Almost 11.2% of short-listed articles propose an exact method.…”

Section: Exact Methodsmentioning

confidence: 99%

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

2022

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In traditional scheduling problems, job processing times are considered constant and known in advance. This assumption is, however, a simplification when it comes to hand-intensive real-life production contexts because workers usually induce variability in the job processing times due to several factors such as learning, monotony, fatigue, psychological factors, etc. These effects can decrease or increase the actual processing time when workers execute a job. The academic literature has reported several modeling and resolution approaches to deal with the phenomenon in a variety of configurations. However, there is no comprehensive review of these research outputs to the best of our knowledge. In this paper, we follow a systematic approach to review relevant contributions addressing the scheduling problem with learning and deterioration effects. Modeling approaches for learning and deterioration effects, objective functions, and solution methods employed in the literature are the main topics for the taxonomy proposed in this review. A total of 455 papers from 1999 to 2021 are included and analyzed. Different areas of interest are presented, and some opportunities for future research are identified.

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Section: Exact Methodsmentioning

confidence: 99%

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

2022

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“…Also, Eren and Güner [22], addressed the learning effect in bi-criteria parallel machine scheduling problem to minimize the total tardiness and total completion time.Cheng et al [23], adopted the learning effect in the scheduling area just in recent years.Gao et al [24], considered a No-wait twomachine permutation flow shop scheduling problem with learning effect and common due date where the processing time of a job is given as a function of its position in the sequence and its amount of resource allocated to this job. Sun et al [25] considered the flow shop scheduling problem of minimising the total weighted completion time in which the processing times of jobs are variable according to general position weighted learning effects.Xin et al [26], studied on a permutation flow shop energy-efficient scheduling problem that considers multiple criteria aiming to find the optimal job processing sequence and conveyor speed that minimise both the makespan and total energy consumption.Bai et al [27], addressed a flow shop scheduling problem to optimize maximum lateness, where learning effect is introduced for each task. They used effective branch and bound (B&B), a discrete artificial bee colony algorithm with hybrid neighbourhood search mechanism algorithms for small and medium-scale instances.…”

Section: Scheduling Problem and Learning Effectmentioning

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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“…The two problems have different decision processes, and different algorithms are proposed for the different problems. Maximum tardiness is a crucial indicator of performance in meeting customer due dates in various manufacturing and service businesses (Allahverdi, 2004;Bai et al 2021;Chen et al 2021). The maximum tardiness reflects the worst level of service associated with customer needs (Aydilek et al, 2022;Pundoor and Chen, 2005).…”

Section: Introductionmentioning

confidence: 99%

To reduce maximum tardiness by Seru Production: model, cooperative algorithm combining reinforcement learning and insights

Fu¹,

Yu²,

Sun³

et al. 2023

10.5267/j.ijiec

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The maximum tardiness reflects the worst level of service associated with customer needs; thus, the principle that seru production reduces the maximum tardiness is investigated, and a model to minimize the maximum tardiness of the seru production system is established. In order to obtain the exact solution, the non-linear seru production model with minimizing the maximum tardiness is split into a seru formation model and a linear seru scheduling model. We propose an efficient cooperative algorithm using a genetic algorithm and an innovative reinforcement learning algorithm (CAGARL) for large-scale problems. Specifically, the GA is designed for the seru formation problem. Moreover, the QL-seru algorithm (QLSA) is designed for the seru scheduling problem by combining the features of meta-heuristics and reinforcement learning. In the QLSA, we design an innovative QL-seru table and two state trimming rules to save computational time. After extensive experiments, compared with the previous algorithm, CAGARL improved by an average of 56.6%. Finally, several managerial insights on reducing maximum tardiness are proposed.

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Minimization of maximum lateness in a flowshop learning effect scheduling with release dates

Cited by 16 publications

References 27 publications

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

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

To reduce maximum tardiness by Seru Production: model, cooperative algorithm combining reinforcement learning and insights

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