Multi-objective flexible flow shop batch scheduling problem with renewable energy

Wu, Xiuli; Xiao, Ning; Cui, Qi

doi:10.1504/ijaac.2020.110071

Cited by 14 publications

(4 citation statements)

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“…To the best of our knowledge, Akers Jr and Friedman studied a JSP with two jobs and four machines in 1955 [16] , and the term "job shop" was first formally proposed in the research literature by Sisson in 1959 [17] . In nearly 70 years of development, the JSP has been greatly extended by considering the transportation time [18] , distributed [19] , setup time [20,21] , machine flexibility [22,23] , dynamic events [24] , fuzzy processing time [25,26] , multi-objective [27−29] and other characteristics [30,31] to meet the needs of actual production. So far, there have been many literature reviews related to the JSPs, including the study of the models [32] , the solving methods [33] , and the JSP with other constraints [34] , etc.…”

Section: Development Of Methods For Solving the Job-shop Scheduling P...mentioning

confidence: 99%

Domain Knowledge Used in Meta-Heuristic Algorithms for the Job-Shop Scheduling Problem: Review and Analysis

Gui,

Li,

Zhang

et al. 2024

Tsinghua Sci. Technol.

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Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable timeframe. By combining domain knowledge of the specific optimization problem, the search efficiency and quality of meta-heuristic algorithms can be significantly improved, making it crucial to identify and summarize domain knowledge within the problem. In this paper, we summarize and analyze domain knowledge that can be applied to meta-heuristic algorithms in the job-shop scheduling problem (JSP). Firstly, this paper delves into the importance of domain knowledge in optimization algorithm design. After that, the development of different methods for the JSP are reviewed, and the domain knowledge in it for meta-heuristic algorithms is summarized and classified. Applications of this domain knowledge are analyzed, showing it is indispensable in ensuring the optimization performance of meta-heuristic algorithms. Finally, this paper analyzes the relationship among domain knowledge, optimization problems, and optimization algorithms, and points out the shortcomings of the existing research and puts forward research prospects. This paper comprehensively summarizes the domain knowledge in the JSP, and discusses the relationship between the optimization problems, optimization algorithms and domain knowledge, which provides a research direction for the metaheuristic algorithm design for solving the JSP in the future.

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Section: Development Of Methods For Solving the Job-shop Scheduling P...mentioning

confidence: 99%

Domain Knowledge Used in Meta-Heuristic Algorithms for the Job-Shop Scheduling Problem: Review and Analysis

Gui,

Li,

Zhang

et al. 2024

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Some future works may be done from following aspects: (1) Regarding the future research of cuckoo algorithm in complex production scheduling, we can consider extending it to more complex scheduling problems than MOPFSP, especially the uncertain green scheduling problem. (2) It is interesting to present some heuristic algorithms to generate initial population instead of random procedure used in this paper so as to further improve the algorithms performance.…”

Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

“…Recently, with the increasingly severe environmental problems in the manufacturing industry, the green manufacturing has received considerable attention. 1,2 The problem model studied in this paper contains environmental indicators, which meets the current needs of ecological environment governance. According to a survey by Fang et al, 3 most of the world's energy consumption comes from the industrial sector, and manufacturing companies have become one of the main factors for global warming.…”

Section: Introductionmentioning

confidence: 99%

An energy-efficient multi-objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm

Dai

et al. 2021

Advances in Mechanical Engineering

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The flow shop scheduling problem has been widely studied in recent years, but the research on multi-objective flow shop scheduling with green indicators is still relatively limited. It is urgent to strengthen the research on effective methods to solve such interesting problems. To consider the economic and environmental factors simultaneously, the paper investigates the multi-objective permutation flow shop scheduling problems (MOPFSP) which minimizes the makespan and total carbon emissions. Since MOPFSP is proved to be a NP-hard problem for more than two machines. A hybrid cuckoo search algorithm (HCSA) is proposed to solve the problems. Firstly, a largest-order-value method is proposed to enhance the performance of HCS algorithm in the solution space of MOPFSP. Then, an adaptive factor of step size is designed to control the search scopes in the evolution phases. Finally, a multi-neighborhood local search rule is addressed in order to find the optimal sub-regions obtained by the HCSA. Numerical experiments show that HCSA can solve MOPFSP efficiently.

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“…Peng et al [30] considered standby energy consumption. Wu et al [31] studied a renewable energy scheduling problem of a flow shop and established a multi-objective renewable energy power supply model, intending to reduce the processing and idle energy consumption during processing. Gilles et al [32] investigated the impacts of batch production on energy consumption and order completion time.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Machining Workshop Batch Scheduling Incorporating the Completion Time and Non-Processing Energy Consumption Considering Product Structure

Feng

2021

Energies

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Energy-saving scheduling is a well-known issue in the manufacturing system. The flexibility of the workshop increases the difficulty of scheduling. In the workshop schedule, considering the collaborative optimization of multi-level structure product production and energy consumption has certain practical significance. The process sequence of parts and components should be consistent with the assembly sequence. Additionally, the non-production energy consumption (NPEC) (such as the energy consumption of workpiece handling, equipment standby, and workpiece conversion) generated by the auxiliary machining operations, which make up the majority of the total energy consumption, should not be ignored. A sub-batch priority is set according to the upper and lower coupling relationship in the product structure. A bi-objective batch scheduling model that minimizes the total energy consumption and the total completion time is developed, and the multi-objective gray wolf optimizer (MOGWO) is employed as the solution to obtain the optimal schedule scheme. A case study is performed to demonstrate the potential possibilities concerning NPEC in regard to reducing the total energy consumption and to show the effectiveness of the algorithm. Compared with the traditional optimization model, the joint optimization of NPEC and PEC can reduce the energy consumption of standby and handling by 9.95% and 22.28%, respectively.

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Multi-objective flexible flow shop batch scheduling problem with renewable energy

Cited by 14 publications

References 0 publications

Domain Knowledge Used in Meta-Heuristic Algorithms for the Job-Shop Scheduling Problem: Review and Analysis

Domain Knowledge Used in Meta-Heuristic Algorithms for the Job-Shop Scheduling Problem: Review and Analysis

An energy-efficient multi-objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm

Research on Machining Workshop Batch Scheduling Incorporating the Completion Time and Non-Processing Energy Consumption Considering Product Structure

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