Decomposition based multiobjective evolutionary algorithm with adaptive resource allocation for energy-aware welding shop scheduling problem

Wang, Sheng; Wang, Jing-jing; Jiang, Enda

doi:10.1016/j.cie.2021.107778

Cited by 11 publications

(5 citation statements)

References 41 publications

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“…Wang [19] constructed a welding shop inverse scheduling problem with dynamic events and developed an enhanced gray wolf optimizer for solving it. Wang [11] proposed MOEA/D with adaptive resource allocation for solving energy-efficient WSP. Wang [9] designed a cooperative memetic algorithm for energy-efficient DHWSP and achieved favorable results.…”

Section: Welding Shop Schedulingmentioning

confidence: 99%

“…Equations ( 9) and ( 10) specify that each job cannot be dispatched to two factories simultaneously. Equation (11) imposes a constraint ensuring that the number of welders used for each job does not exceed the capacity limit. Equation ( 12) defines the finish time for the first job at each factory.…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…The WSP is an extension of the permutation flow shop scheduling problem [10], taking into account that a job can be processed by multiple welders in parallel [11]. The DHWSP further extends the WSP by considering multiple heterogeneous factories to handle a substantial volume of orders [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

Zhang,

Li,

et al. 2024

Complex Intell. Syst.

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This research is focused on addressing the energy-aware distributed heterogeneous welding shop scheduling (EADHWS) problem. Our primary objectives are to minimize the maximum finish time and total energy consumption. To accomplish this, we introduce a learning-based cooperative and competitive multi-objective optimization method, which we refer to as LCCMO. We begin by presenting a multi-rule cooperative initialization approach to create a population that combines strong convergence and diversity. This diverse population forms the foundation for our optimization process. Next, we develop a multi-level cooperative global search strategy that explores effective genes within solutions from different angles and sub-problems. This approach enhances our search for optimal solutions. Moreover, we design a competition and cooperation strategy for different populations to expedite convergence. This strategy encourages the exchange of information and ideas among diverse populations, thereby accelerating our progress. We also introduce a multi-operator cooperative local search technique, which investigates elite solutions from various directions, leading to improved convergence and diversity. In addition, we integrate Q-learning into our competitive swarm optimizer to explore different regions of the objective space, enhancing the diversity of the elite archive. Q-learning guides the selection of operators within the small-size population, contributing to more efficient optimization. To evaluate the effectiveness of LCCMO, we conduct numerical experiments on 20 instances. The experimental results unequivocally demonstrate that LCCMO outperforms six state-of-the-art algorithms. This underscores the potential of our learning and knowledge-driven evolutionary framework in enhancing performance and autonomy when it comes to solving EADHWS.

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Section: Welding Shop Schedulingmentioning

confidence: 99%

Section: Mathematical Formulationmentioning

confidence: 99%

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Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

Zhang,

Li,

et al. 2024

Complex Intell. Syst.

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“…In recent years, evolutionary algorithms (EAs) have been widely used to solve various multi-objective problems [14][15][16]. These multi-objective EAs (MOEAs) can be roughly divided into two categories according to the fitness evaluation mechanism (FEM).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Multi-Objective Evolutionary Algorithm for Green Scheduling of Heterogeneous Quay Cranes Considering Cooperative Movement and Safety

Zhong,

He,

et al. 2023

JMSE

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Heterogeneous quay cranes (HQCs) are the main energy-consuming equipment of automated container terminals, and they need to move from one bay to another along the rail and maintain a safe distance from one another. Improving the operational efficiency of HQCs and reducing the ineffective walking distance of HQCs are key to reducing the energy consumption of QCs. In this paper, an energy-efficient HQC cooperative scheduling problem is studied, and the HQCs are required to ensure safe and efficient operation. A multi-objective scheduling model is formulated to minimize the maximum completion time of containers, the average completion time of HQCs, and the total energy consumption of HQCs simultaneously. An Enhanced Multi-Objective Evolutionary Algorithm (EMOEA) is designed to solve this problem using a problem-feature-based encoding method to encode and initialize the population, a cooperative strategy to ensure the safe operating distance of HQCs, and a novel multi-objective evaluation mechanism with effective evolutionary operators. The results indicate that the different operational capacities of HQCs had a significant impact on the three studied objectives, especially for some large-scale problems, and that our algorithm outperforms three other well-known multi-objective algorithms in solving the EHQCCSP.

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“…They gave a research framework of EES, which classified the existing literature according to three dimensions of energy coverage, energy supply, and energy demand. Undoubtedly, it helped scholars understand this research problem more systematically [15]. So far, several studies have demonstrated that reducing energy consumption has become one of the hot spots in scheduling [16].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Hybrid Flow-Shop Scheduling under Time-of-Use and Ladder Electricity Tariffs

Chen

Wang

et al. 2022

Applied Sciences

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With the increasing influence of climate change, green development has become an important trend. Since manufacturing represents approximately one-half of total energy consumption, reducing the amount of energy consumed within this industry is imperative. This study provides a hybrid flow shop scheduling issue under a time-of-use and ladder electricity price system to reduce total energy consumption without compromising maximum completion time. An improved non-dominated sorting genetic algorithm II with some optimization strategies is proposed to solve the problem. First, an enhanced constructive heuristic algorithm is used to improve the quantity of initial solution in the initialization. Besides, an adaptive genetic operation is introduced, aiming to avoid the emergence of locally optimal solutions. In addition, the right-shift approach is developed to reduce the total energy consumption without affecting completion time. By maintaining the production efficiency and reducing the energy consumption cost by 4.33%. A trade-off proposal is made between productivity and sustainability in view of the calculation results.

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Decomposition based multiobjective evolutionary algorithm with adaptive resource allocation for energy-aware welding shop scheduling problem

Cited by 11 publications

References 41 publications

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

Enhanced Multi-Objective Evolutionary Algorithm for Green Scheduling of Heterogeneous Quay Cranes Considering Cooperative Movement and Safety

Energy-Efficient Hybrid Flow-Shop Scheduling under Time-of-Use and Ladder Electricity Tariffs

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