A hyper-heuristic approach for stochastic parallel assembly line balancing problems with equipment costs

Özbakır, Lale; Seçme, Gökhan

doi:10.1007/s12351-020-00561-x

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…A hyper-heuristic is a methodology that combines different heuristic algorithms, chosen from a pool of heuristic algorithms utilising a low-level heuristic (LLH) selection operator. The algorithm has two components responsible for the selection of a heuristic algorithm: (1) the LLH selection and (2) acceptance (Özbakır and Seçme, 2022). Within a hyper-heuristic framework, score tables may be utilised to evaluate the performance of the LLHs.…”

Section: Solution Proceduresmentioning

confidence: 99%

“…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). The existing literature lacks sufficient studies that investigate the use of hyper-heuristic algorithms for solving the simple assembly line problem with type-I.…”

Section: Solution Proceduresmentioning

confidence: 99%

“…The 10 LLHs (SPT, LPT, MiTNST, MaTNST, MiTTST, MaTTST, MiTNPT, MATNPT, MiTTPT and MaTTPT), are derived from a COMSOAL technique for assembly lines (Arcus, 1966). The remaining ones (RP and STN) are randomly generated by taking into account the list of predecessors of assignable tasks (Özbakır and Seçme, 2022) and (Baykasoğlu and Özbakır, 2015) implemented some of the task assignment rules used in this study to the ALB problems due to their simple implementation and structures, lower computation times and ease of adaptation to different types of assembly lines.…”

Section: The Proposed Abc Based-hyper Heuristic Algorithm With Local ...mentioning

confidence: 99%

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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%

Section: Solution Proceduresmentioning

confidence: 99%

Section: The Proposed Abc Based-hyper Heuristic Algorithm With Local ...mentioning

confidence: 99%

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

Deliktaş,

Aydin

2023

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Chance-constrained stochastic assembly line balancing with branch, bound and remember algorithm

Li,

Sikora,

Kucukkoc

2024

Ann Oper Res

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Assembly lines are widely used mass production techniques applied in various industries from electronics to automotive and aerospace. A branch, bound, and remember (BBR) algorithm is presented in this research to tackle the chance-constrained stochastic assembly line balancing problem (ALBP). In this problem variation, the processing times are stochastic, while the cycle time must be respected for a given probability. The proposed BBR method stores all the searched partial solutions in memory and utilizes the cyclic best-first search strategy to quickly achieve high-quality complete solutions. Meanwhile, this study also develops several new lower bounds and dominance rules by taking the stochastic task times into account. To evaluate the performance of the developed method, a large set of 1614 instances is generated and solved. The performance of the BBR algorithm is compared with two mixed-integer programming models and twenty re-implemented heuristics and metaheuristics, including the well-known genetic algorithm, ant colony optimization algorithm and simulated annealing algorithm. The comparative study demonstrates that the mathematical models cannot achieve high-quality solutions when solving large-size instances, for which the BBR algorithm shows clear superiority over the mathematical models. The developed BBR outperforms all the compared heuristic and metaheuristic methods and is the new state-of-the-art methodology for the stochastic ALBP.

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Heuristic and metaheuristic procedures for the Parallel Assembly Lines Balancing Problem with multi-line workstations and buffer sizing

Aguilar,

García-Villoria,

Pastor

2024

Computers & Operations Research

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A hyper-heuristic approach for stochastic parallel assembly line balancing problems with equipment costs

Cited by 5 publications

References 52 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

Chance-constrained stochastic assembly line balancing with branch, bound and remember algorithm

Heuristic and metaheuristic procedures for the Parallel Assembly Lines Balancing Problem with multi-line workstations and buffer sizing

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