Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization

Luo, Shouhua; Zhang, Linxuan; Fan, Yushun

doi:10.1016/j.jclepro.2019.06.151

Cited by 117 publications

(33 citation statements)

References 40 publications

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“…Therefore, it shall not apply to solve large-scale problems [18] . The approximation methods, especially the meta-heuristic algorithms such as genetic algorithm (GA) [3,6,[30][31] , tabu search(TS) algorithm [22] , grey wolf optimization algorithm [32] and virus optimization algorithm(VOA) [33] have proven to be effective for solving the scheduling problems, particularly for large-size problems. To optimize makespan of FJSP-SDST, Shen et al [22] proposed a tabu search algorithm, and Zhang et al [34] proposed an improved genetic algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Meng et al [19] designed a VNS for FJSP with considering worker flexibility and Turn Off/On strategy. For FJSP with controllable processing times (FJSP-CPT), Gong et al [36] proposed a hybrid GA to simultaneously minimize makespan, worker cost and green objective, Luo et al [32] designed a multi-objective grey wolf optimization algorithm for simultaneously minimizing makespan and total energy consumption, and Wu and Sun [3] designed a NSGA-II to optimize makespan, energy consumption and the number of Turn Off/On strategy simultaneously.…”

Section: Literature Reviewmentioning

confidence: 99%

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Mathematical Modeling and Optimization Of Flexible Job Shop Problem with Sequence-Dependent Setup and Transportation Energy Consumptions

Meng

2021

Preprint

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As environmental awareness grows, energy-aware scheduling is attracting increasing attention. This paper investigates the flexible job shop scheduling problem with sequence-dependent setup times and transportation times (FJSP-SDST-T) and the objective is to minimize total energy consumption. To begin with, the total energy consumption of the workshop is analyzed and a novel mixed integer linear programming (MILP) model is formulated. Due to that FJSP-SDST-T is NP-hard, an effective hybrid algorithm (HGA) that hybridizes the genetic algorithm (GA) and variable neighborhood search (VNS) algorithm is proposed to solve the problem specifically for that with large size. HGA takes advantage of the good global searching ability of GA and the powerful local searching ability of VNS, and it can have a good balance of intensification and diversification. Then, four energy-conscious decoding methods are designed, in which two energy-saving strategies namely postponing strategy and Turn Off/On strategy are specially designed according to the characteristics of FJSP-SDST-T. Finally, experiments are carried out and the results show the effectiveness of the MILP model, the energy-conscious decoding methods and HGA.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Mathematical Modeling and Optimization Of Flexible Job Shop Problem with Sequence-Dependent Setup and Transportation Energy Consumptions

Meng

2021

Preprint

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“…The authors of [45] considered the makespan, tardiness, and energy consumption and assumed that the third objective is less important than other ones. The authors of [46] proposed an adaptive multi-objective variable neighborhood search algorithm to solve the no-wait flow shop problem, and the authors of [47] designed a multi-objective grey wolf optimization algorithm to solve the flexible job shop problem. The authors of [48] studied the flexible job shop scheduling problem considering the machines' on/off and speed level simultaneously.…”

Section: Literature Reviewmentioning

confidence: 99%

A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint

Cui

2020

Sustainability

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With the growing concern of energy shortage and environment pollution, the energy aware operation management problem has emerged as a hot topic in industrial engineering recently. An integrated model consisting of production scheduling, preventive maintenance (PM) planning, and energy controlling is established for the flow shops with the PM constraint and peak demand constraint. The machine’s on/off and the speed level selection are considered to save the energy consumption in this problem. To minimize the makespan and the total energy consumption simultaneously, a multi-objective algorithm founded on NSGA-II is designed to solve the model effectively. The key decision variables are coded into the chromosome, while the others are obtained heuristically using the proposed decoding method when evaluating the chromosome. Numerical experiments were conducted to validate the effectiveness and efficiency by comparing the proposed algorithm and the traditional rules in manufacturing plant. The impacts of constraints on the Pareto frontier are also shown when analyzing the tradeoff between two objectives, which can be used to explicitly assess the energy consumption.

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“…Compared with the genetic algorithm (GA) and particle swarm optimization (PSO), GWO algorithm results are more competitive [11]. At present, the gray wolf algorithm has been widely applied in thermodynamics [12], power systems [13], energy and fuels [14], cloud technology [15], and workshop scheduling [16][17][18]. Lu [16] embedded genetic operators into the multi-objective GWO to enhance the searchability of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Qin [17] used the improved multi-objective gray wolf algorithm to solve the casting shop scheduling to minimize the production cycle, total production cost, and total delivery delay. Lu [18] added a random search model based on traditional GWO search to enhance global search capability. Although GWO has been successfully used in many different types of production environments, there is limited literature on GWO to solve energy-saving scheduling problems in a machine-shop, especially to optimize auxiliary production energy consumption.…”

Section: Introductionmentioning

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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Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization

Cited by 117 publications

References 40 publications

Mathematical Modeling and Optimization Of Flexible Job Shop Problem with Sequence-Dependent Setup and Transportation Energy Consumptions

Mathematical Modeling and Optimization Of Flexible Job Shop Problem with Sequence-Dependent Setup and Transportation Energy Consumptions

A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint

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

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