Self-adaptive memetic algorithms for multi-objective single machine learning-effect scheduling problems with release times

Deliktaş, Derya

doi:10.1007/s10696-021-09434-7

Cited by 6 publications

(2 citation statements)

References 105 publications

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“…Furthermore, despite the success and effectiveness of the meta-heuristic algorithms, their performance can vary across different problem domains (Zhang et al ., 2020). Due to the advantages of hyper-heuristics, many studies utilised the hyper-heuristic algorithms to tackle a wide range of optimisation problems such as university course timetabling (Bai et al ., 2007; Soria-Alcaraz et al ., 2016), knapsack problems (Gölcük and Ozsoydan, 2021), exam timetabling problems (Hao et al ., 2020), vehicle crash-worthiness problem (Li et al ., 2017), quadratic assignment problem (Dokeroglu and Cosar, 2016) and scheduling problems (Koulinas et al ., 2014; Hart and Sim, 2016; Lin et al ., 2017; Deliktaş, 2021). Furthermore, a few studies have explored the application of hyper-heuristic algorithms in addressing different assembly line variations, such as aircraft final ALB (Bao et al ., 2023), two-sided ALB (Rong et al ., 2023), stochastic parallel disassembly line balancing (Hu et al ., 2023), parallel ALB (Seçme and Özbakır, 2019; Özbakır and Seçme, 2022), mixed-model ALB (Ebrahimi et al ., 2023; Cano-Belmán et al ., 2010) and robotic parallel with type-II (Çil et al ., 2017).…”

Section: Solution Proceduresmentioning

confidence: 99%

An artificial bee colony based-hyper heuristic algorithm with local search for the assembly line balancing problems

Deliktaş,

Aydin

2023

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PurposeAssembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant.Design/methodology/approachThis paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms.FindingsThe experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach.Originality/valueThis research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.

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Section: Solution Proceduresmentioning

confidence: 99%

An artificial bee colony based-hyper heuristic algorithm with local search for the assembly line balancing problems

Deliktaş,

Aydin

2023

Self Cite

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“…Multi-criteria problems are usually solved using metaheuristics or hybrid metaheuristics, each with its special characteristics. The following are some of the approaches proposed by the authors: Deliktaş [114] to minimize makespan and total tardiness, Abedi et al [45] to minimize total weighted tardiness and total energy consumption, and Wang et al [115] to minimize the weighted sum of makespan and total completion time, used a memetic multiobjective algorithm to obtain a set of solutions. First, the authors develop a non-dominated sorting method, and second, the authors appeal to the Pareto front.…”

Section: Metaheuristicsmentioning

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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Modeling and Optimization Method of Laser Cladding Based on GA-ACO-RFR and GNSGA-II

Qiang

et al. 2022

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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Self-adaptive memetic algorithms for multi-objective single machine learning-effect scheduling problems with release times

Cited by 6 publications

References 105 publications

An artificial bee colony based-hyper heuristic algorithm with local search for the assembly line balancing problems

An artificial bee colony based-hyper heuristic algorithm with local search for the assembly line balancing problems

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

Modeling and Optimization Method of Laser Cladding Based on GA-ACO-RFR and GNSGA-II

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