Multi-objective scheduling in hybrid flow shop: Evolutionary algorithms using multi-decoding framework

Yu, Chunlong; Andreotti, Pietro; Semeraro, Quirico

doi:10.1016/j.cie.2020.106570

Cited by 25 publications

(6 citation statements)

References 47 publications

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“…The low-carbon scheduling of ship-segmented painting considers segmental sequencing along with the selection of the painting team, and the complexity of the problem is more complicated than the classical hybrid flow-shop scheduling problem (HFSP). Since the past research on HFSP mainly focuses on a single objective function and does not take into account the real problems, research on multi-objective problems (MOPs) has been increasing year by year [13,14]. Qiu et al systematically examined and evaluated a series of clustering algorithms and models in order to solve the problem of accurate customer segmentation for the financial card industry, and the experimental results showed that the method could deepen the segmentation of customers and promote economic growth [15].…”

Section: Introductionmentioning

confidence: 99%

A Decomposition-Based Multi-Objective Evolutionary Algorithm for Solving Low-Carbon Scheduling of Ship Segment Painting

Bu,

Zhu,

et al. 2024

Coatings

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Ship painting, as one of the three pillars of the shipping industry, runs through the whole process of ship construction. However, there are low scheduling efficiency and excessive carbon emissions in the segmental painting process, and optimizing the scheduling method is an important means to achieve the sustainable development of the ship manufacturing industry. To this end, firstly, a low-carbon scheduling mathematical model for the segmented painting workshop is proposed, aiming to reduce carbon emissions and improve the painting efficiency of the segmented painting workshop. Second, an artificial bee colony algorithm designed based on a decomposition strategy (MD/ABC) is proposed to solve the model. In the first stage, five neighborhood switching methods are designed to achieve the global search employed for each solution. In the second stage, the Technique of Ordering the Ideal Solutions (TOPSIS) improves the competition mechanism through the co-evolution between neighboring subproblems and designs the angle to define the relationship between neighboring subproblems to enhance the localized search and improve population quality. The solution exchange strategy is used in the third stage to improve the efficiency of the algorithm. In addition, a two-stage coding method is designed according to the characteristics of the scheduling problem. Finally, the algorithm before and after the improvement and with other algorithms is analyzed using comparative numerical experiments. The experimental results show the effectiveness of the algorithm in solving the low-carbon scheduling problem of ship segmental painting and can provide reliable guidance for the scheduling program of segmented painting workshops in shipyards.

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Section: Introductionmentioning

confidence: 99%

A Decomposition-Based Multi-Objective Evolutionary Algorithm for Solving Low-Carbon Scheduling of Ship Segment Painting

Bu,

Zhu,

et al. 2024

Coatings

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“…Several heuristic decoding methods [41] [42] have been proposed to tackle job shop problems, however, the decoding methods commonly used in TSP lack heuristic. Some algorithms employ mathematical formulas suitable for the continuous problem and need to adopt the decoding method to generate legal TSP paths.…”

Section: ) Algorithms With Decoding Methods For Tspmentioning

confidence: 99%

A Carnivorous Plant Algorithm With Heuristic Decoding Method for Traveling Salesman Problem

et al. 2022

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The traveling salesman problem (TSP) is one of the most extensively studied problems in the combinatorial optimization area and still presents unsolved challenges due to its NP-hard attribute. Although many real-coded algorithms are available for TSP, they still have some performance challenges in the switch from continuous space to discrete space and perform at low convergence speed. This paper proposes a real-coded carnivorous plant algorithm with a heuristic decoding method (CPA-HDM) to solve the traveling salesman problem (TSP), which exhibits good convergence speed and solution accuracy. In this improved method, a new heuristic decoding method (HDM) is designed, which helps to map continuous variables to discrete ones without losing information, maintain population diversity, and enhance the solution quality after decoding. To balance the algorithm's search capability and enhance the probability of preferable individuals generated, an adaptive attraction probability (AAP), an improved growth model of carnivorous plants (IGMOCP), and a position update method of prey (IPUMOP) are developed. Aiming to reduce the probability of premature and prevent search stagnation, an improved reproduction strategy (IRS) and an adaptive combination perturbation are reconstructed. Finally, a local search algorithm is employed to improve the exploitation capability. To verify its validation, CPA-HDM is compared with six algorithms, for solving 28 TSP instances. The simulation results and statistical analyses demonstrate the superior performance of the proposed algorithm.

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“…Efficient encoding and decoding approaches can increase the solution space to a certain extent to find hidden efficient solutions while reducing the search space to exclude invalid solutions, and this section focuses on the encoding and decoding methods of EADHFSP-ST. In this paper, we use a representation proposed by Naderi and Ruiz [51] , where f lists are included in the solution and each list represents the order in which jobs are processed in the first stage of the factory. Based on this, the solution is encoded as [ , ]= , where represents the order of operations in the first stage, .…”

Section: Encoding and Decodingmentioning

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.

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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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Multi-objective scheduling in hybrid flow shop: Evolutionary algorithms using multi-decoding framework

Cited by 25 publications

References 47 publications

A Decomposition-Based Multi-Objective Evolutionary Algorithm for Solving Low-Carbon Scheduling of Ship Segment Painting

A Decomposition-Based Multi-Objective Evolutionary Algorithm for Solving Low-Carbon Scheduling of Ship Segment Painting

A Carnivorous Plant Algorithm With Heuristic Decoding Method for Traveling Salesman Problem

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

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