Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions

Li, Junqing; Sang, Hongshi; Han, Yuyan; Wang, Cun-gang; Gao, Kaizhou

doi:10.1016/j.jclepro.2018.02.004

Cited by 247 publications

(97 citation statements)

References 53 publications

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“…Finally, several illustrative examples are provided to demonstrate the detailed application and analyze the advantages of the proposed method. In future research, we will consider using the optimization algorithm,, to further improve the accuracy and adaptability of the proposed method. At the same time, the proposed method will be applied to other decision‐making fields, such as supplier selection, service evaluation, image pattern recognition, and so forth.…”

Section: Discussionmentioning

confidence: 99%

Some q ‐rung orthopair uncertain linguistic aggregation operators and their application to multiple attribute group decision making

Liu

et al. 2019

Int J Intell Syst

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q‐Rung orthopair fuzzy sets (q‐ROFSs), originally presented by Yager, are a powerful fuzzy information representation model, which generalize the classical intuitionistic fuzzy sets and Pythagorean fuzzy sets and provide more freedom and choice for decision makers (DMs) by allowing the sum of the qnormaltnormalh power of the membership and the qnormaltnormalh power of the nonmembership to be less than or equal to 1. In this paper, a new class of fuzzy sets called q‐rung orthopair uncertain linguistic sets (q‐ROULSs) based on the q‐ROFSs and uncertain linguistic variables (ULVs) is proposed, and this can describe the qualitative assessment of DMs and provide them more freedom in reflecting their belief about allowable membership grades. On the basis of the proposed operational rules and comparison method of q‐ROULSs, several q‐rung orthopair uncertain linguistic aggregation operators are developed, including the q‐rung orthopair uncertain linguistic weighted arithmetic average operator, the q‐rung orthopair uncertain linguistic ordered weighted average operator, the q‐rung orthopair uncertain linguistic hybrid weighted average operator, the q‐rung orthopair uncertain linguistic weighted geometric average operator, the q‐rung orthopair uncertain linguistic ordered weighted geometric operator, and the q‐rung orthopair uncertain linguistic hybrid weighted geometric operator. Then, some desirable properties and special cases of these new operators are also investigated and studied, in particular, some existing intuitionistic fuzzy aggregation operators and Pythagorean fuzzy aggregation operators are proved to be special cases of these new operators. Furthermore, based on these proposed operators, we develop an approach to solve the multiple attribute group decision making problems, in which the evaluation information is expressed as q‐rung orthopair ULVs. Finally, we provide several examples to illustrate the specific decision‐making steps and explain the validity and feasibility of two methods by comparing with other methods.

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Section: Discussionmentioning

confidence: 99%

Some q ‐rung orthopair uncertain linguistic aggregation operators and their application to multiple attribute group decision making

Liu

et al. 2019

Int J Intell Syst

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“…Lei et al [12] constructed a reformulated bounds algorithm to minimize peak demands on total workload and total energy consumption. Li et al [13] presented a suggested multiobjective optimization algorithm using crossover agents to minimize energy consumption. Considering the speed level and machine turn on/off, the researchers [14][15][16][17] proposed a genetic algorithm to minimize makespan and energy consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Note that with Constraints (11) and (12), the completion time of a job on machine 2 is determined as its processing time on machine 2 plus the maximum of its completion time on machine 1, setup time for changing over from its predecessor job in machine 2 and the preventive maintenance PM j when is possible of the i th job (the possible PM time). Constraints (13) indicate the initial system status. Constraint Sets (14) and (15) specify the machine's age before and after each job i, respectively.…”

Section: Problem Description and Modellingmentioning

confidence: 99%

Green Scheduling of Jobs and Flexible Periods of Maintenance in a Two-Machine Flowshop to Minimize Makespan, a Measure of Service Level and Total Energy Consumption

Assia

Abbassi

Barkany

et al. 2020

Advances in Operations Research

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e success of an industry today depends on its ability to innovate. In terms of energy performance, this innovation is reflected in the ability of manufacturers to implement new solutions or technologies that enable better energy management. In this regard, this paper aims to address this gap by incorporating energy consumption as an explicit criterion in flowshop scheduling of jobs and flexible preventive maintenance. Leveraging the variable speed of machining operations leading to different energy consumption levels, we explore the potential for energy saving in manufacturing. We develop a mixed integer linear multiobjective optimization model for minimizing the makespan and the total energy consumption. In the literature, no papers considering both production scheduling and flexible periods of maintenance with minimizing both objective the total of energy consumption in flowshop and makespan. e performance of the proposed mixed binary integer programming model is evaluated based on the exact method of branch and bound algorithm. A study of the results proved the performance of the model developed.

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“…Li et al. () proposed a multiobjective optimization algorithm called energy aware for solving MOHFS problems. Two objectives are considered simultaneously: the minimization of makespan and the minimization of setup consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

An MO‐GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem

Siqueira

Souza

2019

Int Trans Operational Res

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This paper addresses the multiobjective hybrid flow shop (MOHFS) scheduling problem. In the MOHFS problem considered here, we have a set of jobs that must be performed in a set of stages. At each stage, we have a set of unrelated parallel machines. Some jobs may skip stages. The evaluation criteria are the minimizations of makespan, the weighted sum of the tardiness, and the weighted sum of the earliness. For solving it, an algorithm based on the multiobjective general variable neighborhood search (MO‐GVNS) metaheuristic, named adapted MO‐GVNS, is proposed. This work also presents and compares the results obtained by the adapted MO‐GVNS with those of four other algorithms: multiobjective reduced variable neighborhood search, nondominated sorting genetic algorithm II (NSGA‐II), and NSGA‐III, and another MO‐GVNS from the literature. The results were evaluated based on the Hypervolume, Epsilon, and Spacing metrics, and statistically validated by the Levene test and confidence interval charts. The results showed the efficiency of the proposed algorithm for solving the MOHFS problem.

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Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions

Cited by 247 publications

References 53 publications

Some q ‐rung orthopair uncertain linguistic aggregation operators and their application to multiple attribute group decision making

Some q ‐rung orthopair uncertain linguistic aggregation operators and their application to multiple attribute group decision making

Green Scheduling of Jobs and Flexible Periods of Maintenance in a Two-Machine Flowshop to Minimize Makespan, a Measure of Service Level and Total Energy Consumption

An MO‐GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem

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