A hybrid genetic algorithm based on a two-level hypervolume contribution measure selection strategy for bi-objective flexible job shop problem

Türkyılmaz, Alper; Şenvar, Özlem; Ünal, İrem; Bulkan, Serol

doi:10.1016/j.cor.2021.105694

Cited by 16 publications

(2 citation statements)

References 43 publications

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“…Li et al [22] proposed an efficient optimization algorithm that is a hybrid of the iterated greedy and simulated annealing algorithms to solve the FJSP with two objectives, which are simultaneously considered, namely, the minimization of the maximum completion time and the energy consumption during machine processing and material transportation. Türkyılmaz et al [23] introduced a biobjective hybrid GA-hypervolume contribution measure that integrates GA with a multisearch algorithm and uses a hypervolume contribution measure (∆s) in its two-level selection strategy.…”

Section: Literature Reviewmentioning

confidence: 99%

The Problem of Machine Part Operations Optimal Scheduling in the Production Industry Based on a Customer’s Order

Mitić,

Petrović Savić,

Djordjevic

et al. 2023

Applied Sciences

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This research focuses on small- and medium-sized businesses that provide machining or other process services but do not produce their own products. Their daily manufacturing schedule varies according to client needs. Small- and medium-sized businesses strive to operate in these circumstances by extending their customer base and creating adequate production planning targets. Their resources are limited, including the technical and technological components of their equipment, tools, people resources, time, and capacities. As a result, planning operations with the present resources of small- and medium-sized businesses in the midst of the global economic crisis is a widespread issue that must be addressed. This study seeks to offer a novel mathematical optimization model based on a genetic algorithm to address job shop scheduling and capacity planning difficulties in small- and medium-sized businesses, therefore improving performance management and production planning procedures. On the basis of the created optimization model, an appropriate software solution, and quantitative data concerning the job shop scheduling and capacity planning challenges of manufacturing operations in small- and medium-sized businesses, the study findings will be obtained. The practical implications include the establishment and development of a decision support system based on the genetic algorithm optimization method, which may improve the effectiveness of the flexible job shop scheduling problem and capacity planning in the production planning process. The given model and the application of the differential precedence preservative crossover operator within genetic algorithms are what constitute the novelty of this study.

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Section: Literature Reviewmentioning

confidence: 99%

The Problem of Machine Part Operations Optimal Scheduling in the Production Industry Based on a Customer’s Order

Mitić,

Petrović Savić,

Djordjevic

et al. 2023

Applied Sciences

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“…Mousakhani [2] Minimize total tardiness Iterated local search Shen et al [3] Minimize makespan Tabu search with specific neighborhood search function Bagheri and Zandieh [4] Minimize makespan and mean tardiness Variable neighborhood search Abdelmaguid [5] Minimize makespan Tabu search with specific neighborhood functions Naderi et al [6] Minimize makespan Genetic algorithm Li and Lei [7] Minimize makespan, total tardiness, and total energy consumption Imperialist competitive algorithm with feedback Defersha and Chen [8] Minimize makespan Parallel genetic algorithm Azzouz et al [9] Minimize makespan and bi-criteria objective function Hybrid genetic algorithm and variable neighborhood search Wang and Zhu [10] Minimize makespan Hybrid genetic algorithm and tabu search Li et al [11] Minimize makespan and total setup costs Elitist nondominated sorting hybrid algorithm Azzouz et al [12] Minimize makespan Hybrid genetic algorithm and iterated local search Azzouz et al [13] Minimize makespan Adaptive genetic algorithm Abderrabi et al [14] Minimize total flow time Genetic algorithm and iterated local search Parjapati and Ajai [15] Minimize makespan Genetic algorithm Sadrzadeh [16] Minimize makespan and mean tardiness Artificial immune system and particle swarm optimization Tayebi Araghi et al [17] Minimize makespan Genetic variable neighborhood search with affinity function Sun et al [18] Minimize makespan, total workload, workload of critical machine, and penalties of earliness/tardiness Hybrid many-objective evolutionary algorithm Li et al [19] Minimize energy consumption and makespan Improved Jaya algorithm Müller et al [20] Minimize makespan Decision trees and deep neural networks Wei et al [21] Minimize the makespan and total energy consumption Energy-aware estimation model Li et al [22] Minimize the makespan and the total workload Hybrid self-adaptive multi-objective evolutionary algorithm Türkyılmaz et al [23] Minimize makespan Hybrid Genetic Algorithm-hypervolume contribution measure Jiang et al [24] Handle the issues of low production efficiency, high energy consumption and processing cost A novel improved crossover artificial bee colony algorithm Exploration and exploitation are treated as the most important features of heuristic algorithms. The trade-off between the two features is crucial to the computational performance.…”

Section: Literature Objective Function Algorithmsmentioning

confidence: 99%

A Study on the Optimal Flexible Job-Shop Scheduling with Sequence-Dependent Setup Time Based on a Hybrid Algorithm of Improved Quantum Cat Swarm Optimization

Song

Liu

2022

Sustainability

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Multi-item and small-lot-size production modes lead to frequent setup, which involves significant setup times and has a substantial impact on productivity. In this study, we investigated the optimal flexible job-shop scheduling problem with a sequence-dependent setup time. We built a mathematical model with the optimal objective of minimization of the maximum completion time (makespan). Considering the process sequence, which is influenced by setup time, processing time, and machine load limitations, first, processing machinery is chosen based on machine load and processing time, and then processing tasks are scheduled based on setup time and processing time. An improved quantum cat swarm optimization (QCSO) algorithm is proposed to solve the problem, a quantum coding method is introduced, the quantum bit (Q-bit) and cat swarm algorithm (CSO) are combined, and the cats are iteratively updated by quantum rotation angle position; then, the dynamic mixture ratio (MR) value is selected according to the number of algorithm iterations. The use of this method expands our understanding of space and increases operation efficiency and speed. Finally, the improved QCSO algorithm and parallel genetic algorithm (PGA) are compared through simulation experiments. The results show that the improved QCSO algorithm has better results, and the robustness of the algorithm is improved.

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Identification of twin-shaft gas turbine based on hybrid decoupled state multiple model approach

et al. 2023

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The work presented in this paper focuses on presenting an hybrid identification method for a nonlinear dynamic gas turbine, from a real time input and outputs data exploitation, with the fuel flow as the input and the rotational speed of high pressure and low pressure turbine as outputs. The multi model, which are in the form of a weighted combination of local linear state space models, offer an interesting alternative of the nonlinear models because it takes into account a several operating modes. The models are identified with the help of decoupled models using a hybrid approach between parametric estimation using artificial intelligence algorithms.

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A hybrid genetic algorithm based on a two-level hypervolume contribution measure selection strategy for bi-objective flexible job shop problem

Cited by 16 publications

References 43 publications

The Problem of Machine Part Operations Optimal Scheduling in the Production Industry Based on a Customer’s Order

The Problem of Machine Part Operations Optimal Scheduling in the Production Industry Based on a Customer’s Order

A Study on the Optimal Flexible Job-Shop Scheduling with Sequence-Dependent Setup Time Based on a Hybrid Algorithm of Improved Quantum Cat Swarm Optimization

Identification of twin-shaft gas turbine based on hybrid decoupled state multiple model approach

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