A two-stage cooperative evolutionary algorithm for energy-efficient distributed group blocking flow shop with setup carryover in precast systems

Niu, Wei; Li, Junqing

doi:10.1016/j.knosys.2022.109890

Cited by 14 publications

(5 citation statements)

References 36 publications

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“…Duan et al [10] proposed an energy-saving optimization model for a flexible job shop and applied the NSGA-II algorithm to solve it. Niu et al [7] focused on the distributed group flow shop scheduling problem and designed a two-stage cooperative evolutionary algorithm to address it. Saber et al [11] developed two multi-objective algorithms to minimize the total tardiness and carbon emissions in a permutation flow shop.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective Evolutionary Algorithm with Variable Neighborhood Search for Optimizing Green Scheduling in a Re-Entrant Hybrid Flow Shop with Dynamic Events

Tong,

Yan,

Tong

et al. 2024

J. Phys.: Conf. Ser.

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In previous studies on re-entrant hybrid flow shops, the impact of dynamic events was often ignored despite being a common occurrence in practical production. To address this issue and simultaneously reduce energy consumption, a multi-objective evolutionary algorithm with variable neighborhood search (MOEA-VNS) has been proposed to optimize the green scheduling problem in a re-entrant hybrid flow shop with dynamic events (RHFS-GDS).The approach involves creating a green dynamic scheduling optimization model, which aims to minimize the makespan, total energy consumption, and stability of rescheduling solutions. A hybrid green scheduling decoding method is then employed to select machines for each operation and calculate fitness. Additionally, three neighborhood structures are designed to improve the population diversity and optimality of the MOEA-VNS algorithm. Two rescheduling strategies are also adopted to handle dynamic events that may occur during production. Experimental results demonstrate that these approaches are effective in solving the RHFS-GDS problem and can guide actual production. By incorporating dynamic events and rescheduling strategies into the optimization process, the proposed MOEA-VNS algorithm provides a comprehensive solution to the complex challenges faced by re-entrant hybrid flow shops in practical production environments.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-objective Evolutionary Algorithm with Variable Neighborhood Search for Optimizing Green Scheduling in a Re-Entrant Hybrid Flow Shop with Dynamic Events

Tong,

Yan,

Tong

et al. 2024

J. Phys.: Conf. Ser.

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show abstract

“…How to apply formal methods to improve the quality of the distributed system has aroused widespread concern in the society. Now the distributed architecture has been applied in various fields: in the production of prefabricated components in the construction industry, Niu et al [11] proposed a two-stage coevolutionary algorithm to minimize manufacturing span and total energy consumption, which solves the distributed group flow workshop scheduling problem with time constraints related to blocking and continuation order in prefabricated systems. Li et al [12] solved the data placement problem by introducing dynamic weights in the multi task scheduling problem of geographically distributed cloud systems.…”

Section: Related Workmentioning

confidence: 99%

Traffic Carrying and Delay Response Scheduling Algorithm for Distributed E- commerce Platforms

Gao,

Yang,

Chen

et al. 2024

Preprint

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The popularization of digitalization, informatization and the Internet has given birth to the rapid development of e-commerce. Faced with the rapidly expanding user traffic, there are still technical bottlenecks in how e-commerce platforms can carry more user traffic and improve server response performance. This article conducts system optimization performance analysis from both hardware and software aspects, and constructs a high-performance distributed AR-AFSA system. (1) The AR (Application Router, AR) architecture is configured with three JobManager server nodes, each receiving three types of user access requests. A traffic allocation mechanism is used to distribute the system's traffic carrying pressure, and user requests are divided into four traffic queues for scheduling according to different access methods. (2) Improve AFSA for container scheduling, re plan the execution order of various behaviors of artificial fish, reduce ineffective search steps, and influence the direction of artificial fish's movement through the global optimal solution, increasing the possibility of finding the optimal solution and accelerating local convergence speed. (3) Using the CPU, memory performance, and load balancing parameters of the container as the parameters and evaluation indicators for artificial fish, matching sufficient resource containers for user requests while ensuring container resource conservation and system load balancing. Finally, the traffic carrying capacity of the AR system and the single JobManager system was validated using the Taobao user behavior dataset and multiple control experiments. The AR system can withstand three times the traffic pressure of traditional servers. The improved AFSA algorithm can converge to a more optimal solution compared to the control algorithm, and in more complex server resource sizes, it consumes lower latency, reduces iteration times, schedules and uses more reasonable resources, demonstrating greater advantages.

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“…, [23,5,19,12,13], [10,16,11,14] 325 2218 m1 = 5, m2 = 5 [23,5,27,24,13,29], [26,6,11,17,30,14,9], [25,12,21,28,8], [2,15,16,7,1], [10,19,22,20,3,4,18] [26, 15, 16, 7, 1, 18], [23,5,6,22,21,3,8], [25,19,20,13], [10,12,…”

Section: Machine Scheduling Solutionmentioning

confidence: 99%

“…They introduced a constructive heuristic algorithm and a water wave optimization algorithm based on problem-specific knowledge. Niu et al [7] addressed the distributed group BFSP with carryover sequence-dependent setup time constraints. They proposed a twostage cooperative coevolutionary algorithm aiming to minimize the makespan and total energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning-Based Multi-Objective of Two-Stage Blocking Hybrid Flow Shop Scheduling Problem

Xu,

Ye,

Gong

et al. 2023

Processes

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Consideration of upstream congestion caused by busy downstream machinery, as well as transportation time between different production stages, is critical for improving production efficiency and reducing energy consumption in process industries. A two-stage hybrid flow shop scheduling problem is studied with the objective of the makespan and the total energy consumption while taking into consideration blocking and transportation restrictions. An adaptive objective selection-based Q-learning algorithm is designed to solve the problem. Nine state characteristics are extracted from real-time information about jobs, machines, and waiting processing queues. As scheduling actions, eight heuristic rules are used, including SPT, FCFS, Johnson, and others. To address the multi-objective optimization problem, an adaptive objective selection strategy based on t-tests is designed for making action decisions. This strategy can determine the optimization objective based on the confidence of the objective function under the current job and machine state, achieving coordinated optimization for multiple objectives. The experimental results indicate that the proposed algorithm, in comparison to Q-learning and the non-dominated sorting genetic algorithm, has shown an average improvement of 4.19% and 22.7% in the makespan, as well as 5.03% and 9.8% in the total energy consumption, respectively. The generated scheduling solutions provide theoretical guidance for production scheduling in process industries such as steel manufacturing. This contributes to helping enterprises reduce blocking and transportation energy consumption between upstream and downstream.

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A two-stage cooperative evolutionary algorithm for energy-efficient distributed group blocking flow shop with setup carryover in precast systems

Cited by 14 publications

References 36 publications

Multi-objective Evolutionary Algorithm with Variable Neighborhood Search for Optimizing Green Scheduling in a Re-Entrant Hybrid Flow Shop with Dynamic Events

Multi-objective Evolutionary Algorithm with Variable Neighborhood Search for Optimizing Green Scheduling in a Re-Entrant Hybrid Flow Shop with Dynamic Events

Traffic Carrying and Delay Response Scheduling Algorithm for Distributed E- commerce Platforms

Reinforcement Learning-Based Multi-Objective of Two-Stage Blocking Hybrid Flow Shop Scheduling Problem

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