Discrete differential evolution metaheuristics for permutation flow shop scheduling problems

Morais, Márcia de Fátima; Ribeiro, Matheus Henrique Dal Molin; Silva, Ramon Gomes da; Mariani, Viviana Cocco; Coelho, Leandro dos Santos

doi:10.1016/j.cie.2022.107956

Cited by 28 publications

(11 citation statements)

References 62 publications

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“…for each job at each stage is a discrete value sampled uniformly from Zhang and Chiong [58] and Wu et al [59] Based on Ref. [16], the setup time between each job pair in each stage (including the virtual job ) is designed to follow a uniform distribution u [1,50]. According to the knowledge, wait for the energy rate to be set to 1 and set the energy rate to 1.5.…”

Section: Experimental Instancementioning

confidence: 99%

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Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Chen,

Luo,

Gong

et al. 2024

Complex Syst. Model. Simul.

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This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time (EADHFSP-ST) that simultaneously optimizes the makespan and the energy consumption. We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm (TAMA) with a surprisingly popular mechanism. First, a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions. Second, multiple population coevolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation. Third, considering that the memetic algorithm (MA) framework is less efficient due to the randomness in the selection of local search operators, TAMA is proposed to balance the local and global searches. The first stage accumulates more experience for updating the surprisingly popular algorithm (SPA) model to guide the second stage operator selection and ensures population convergence. The second stage gets rid of local optimization and designs an elite archive to ensure population diversity. Fourth, five problem-specific operators are designed, and non-critical path deceleration and right-shift strategies are designed for energy efficiency. Finally, to evaluate the performance of the proposed algorithm, multiple experiments are performed on a benchmark with 45 instances. The experimental results show that the proposed TAMA can solve the problem effectively.

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Section: Experimental Instancementioning

confidence: 99%

“…As intelligent manufacturing is developing [1] , the pursuit of high efficiency produces huge economic effects while also bringing many challenges to traditional production. Flow shop, as a class of classical shop scheduling problems, is widely used in real production [2] .…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Chen,

Luo,

Gong

et al. 2024

Complex Syst. Model. Simul.

View full text Add to dashboard Cite

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“…F � 0.5, Cr � 0.9. Tis is a recommend parameters setting for DE/Rand/1 in most of the references [1][2][3][4][5][6][7][8][9][10][11][12][13]; Strategy 2(DEG): F ∼ N(0, 1), C r � 0.9 [11]; Strategy 3(DE0.4): F � 0.4 + 0.4 • rand(0, 1), C r � 0.9 [12] Strategy 4(DEM): C r � 0.5 and F is calculated by the following formula [13]:…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…Te researchers proposed three discrete DEs for the scheduling problems in the permutation fow shop environment [3]. Tese approaches focus on converting vectors of the continuous domain into permutation vectors of the discrete domain and self-adjusting the control parameters of these algorithms based on JADE [4] and SADE [5].…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution without the Scale Factor and the Crossover Probability

Zhang

2023

Journal of Mathematics

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Differential evolution has made great achievements in various fields such as computational sciences, engineering optimization, and operations management in the past decades. It is well known that the control parameter setting plays a very important role in terms of the performance improvement of differential evolution. In this paper, a differential evolution without the scale factor and the crossover probability is presented, which eliminates almost all control parameters except for the population size. The proposed algorithm looks upon each individual as a charged particle to decide on the shift of the individual in the direction of the difference based on the attraction-repulsion mechanism in Coulomb’s Law. Moreover, Taguchi’s parameter design method with the two-level orthogonal array is merged into the crossover operation in order to obtain better individuals in the next generation by means of better combination of factor levels. What is more, a new ratio of the signal-to-noise is proposed for the purpose of fair comparison of the numerical experiment for the tested functions which have an optimal value with 0. Numerical experiments show that the proposed algorithm outperforms the other 5 compared algorithms for the 10 benchmark functions.

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“…Heuristic algorithms are widely used in similar research fields. The first subcategory of heuristics consists of algorithms inspired by evolution concepts, referred to as Evolutionary Algorithms (EAs) [41], which include Genetic Algorithms (GA) [42,43] and Differential Evolution [44,45]. The other type of algorithms is swarm-based or population-based algorithms, which are inspired by animal behaviors [46], Particle Swarm Optimization (PSO) [47,48], Gray-Wolf Optimization (GWO) [49,50], Cuckoo Search Algorithm [51,52] are just a few examples of swarm intelligence algorithms.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Location Optimization of VTS Radar Stations Considering Environmental Occlusion and Radar Attenuation

Huang

2022

IJGI

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Waterway traffic monitoring is an important content in waterway traffic management. Taking into account that the number of monitored water areas is growing and that waterway traffic management capabilities are insufficient in the current situation in China, this paper investigates the location optimization of the vessel traffic service (VTS) radar station. During the research process, radar attenuation and environmental occlusion, as well as variable coverage radius and multiple covering are all considered. In terms of the radar attenuation phenomenon in the propagation process and obstacles such as mountains and islands in the real world, judgment and evaluation methods in a three-dimensional space are proposed. Moreover, a bi-objective mathematical model is then developed, as well as a modified adaptive strategy particle swarm optimization algorithm. Finally, a numerical example and a case are given to verify the effectiveness of the proposed methods, model, and algorithm. The results show the methods, model, and algorithm proposed in this paper can solve the model efficiently and provide a method to optimize the VTS radar station location in practice.

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Discrete differential evolution metaheuristics for permutation flow shop scheduling problems

Cited by 28 publications

References 62 publications

Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Differential Evolution without the Scale Factor and the Crossover Probability

Location Optimization of VTS Radar Stations Considering Environmental Occlusion and Radar Attenuation

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