Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system

Fu, Yaping; Ding, Jinliang; Wang, Hongfeng; Wang, Junwei

doi:10.1016/j.asoc.2017.12.009

Cited by 126 publications

(39 citation statements)

References 36 publications

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“…integrate process planning and scheduling problem [29] and network design problem [30]. Comparison results demonstrate its strong ability in searching for a global optimum, which motivates us to choose it as an optimizer for the investigated problem [31,32].…”

Section: The Proposed H-np Methodsmentioning

confidence: 99%

Job scheduling and resource allocation in parallel‐machine system via a hybrid nested partition method

Jiang

Tian

et al. 2018

IEEJ Transactions Elec Engng

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Scheduling problems with resource allocation have received much attention in the academic and industrial communities in recent years. This study addresses a parallel-machine scheduling problem with resource allocation under the objective of minimizing the makespan, where jobs need an additional resource that is allocated to machines in advance. The processing time of jobs is a function of the amount of allocated resources on their corresponding machines. In order to deal with this problem, three decisions must be made, i.e. resource allocation among machines, job assignment on machines, and job sequence on each machine. To solve this problem efficiently, a constructive heuristic approach and a hybrid nested partition method are designed to make these three decisions. Simulation experiments are carried out on a set of instances, and two existing algorithms are chosen to be compared with the proposed algorithms. Experimental results show that the proposed algorithms are promising in coping with this problem.

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Section: The Proposed H-np Methodsmentioning

confidence: 99%

Job scheduling and resource allocation in parallel‐machine system via a hybrid nested partition method

Jiang

Tian

et al. 2018

IEEJ Transactions Elec Engng

Self Cite

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“…Recently, combinations of decomposition mechanism and NSGA-II components were successfully used to solve combinatorial problems [47]. Latest research studies also suggest the effectiveness of NSGA-II for solving similar scheduling problems [48][49][50]. us, in this paper, we choose NSGA-II as the baseline algorithm for comparison.…”

Section: Effect Of Algorithm Componentsmentioning

confidence: 99%

Weapon Selection and Planning Problems Using MOEA/D with Distance-Based Divided Neighborhoods

2019

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Real-world multiobjective optimization problems are characterized by multiple types of decision variables. In this paper, we address weapon selection and planning problems (WSPPs), which include decision variables of weapon-type selection and weapon amount determination. Large solution space and discontinuous, nonconvex Pareto front increase the difficulty of problem solving. This paper solves the addressed problem by means of a multiobjective evolutionary algorithm based on decomposition (MOEA/D). Two mechanisms are designed for the complex combinatorial characteristic of WSPPs. The first is that the neighborhood of each individual is divided as selection and replacement neighborhoods. The second is that the neighborhood size is changing during the evolution by introducing a distance parameter to constrain the search scope of each subproblem. The proposed algorithm is termed as MOEA/D with distance-based divided neighborhoods (MOEA/D-DDNs) which can overcome possible drawbacks of original MOEA/D with weighted sum approach for complex combinatorial problems. Benchmark instances are generated to verify the proposed approach. Experimental results suggest the effectiveness of the proposed algorithm.

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“…On this account, an approach with regard to machine scheduling, was employed to analyze the required time and electricity consumption data for a series of specified machines. Fu et al [4] presented a novel flow shop scheduling model with consideration of multiple objectives, uncertainty, deteriorating and learning effect in an in an industry 4.0-based manufacturing environment. Wang et al [5] was concentrated on the modelling of a sequence, which was dependent upon the two machine flow shops to supply a low-consumption of the energy.…”

Section: Literature Review and Research Backgroundmentioning

confidence: 99%

Optimal Operation and Scheduling of Parallel Machines in Jobshop Environments

Faccio¹,

Nedaei²

2018

Management of Sustainable Development

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Scheduling of machines together with the optimization of the energy consumption (EC) play a crucial role in every manufacturing system. The current paper aims to present a multi-objective optimization approach for planning and scheduling of a manufacturing system in a job-shop environment. First, an inclusive review and comparison of the latest advancements in this field was performed. Then, a manufacturing system in a job-shop environment, which consists of six designed machines with zero buffer capacity, and 53 jobs, was simulated. Four heuristics for the purpose of sequencing are adopted during the analysis process. A comparison of the optimum results suggested the most efficient values for the EC, make-span, and tardiness. The EC variations and processing time of the machines were studied under sequencing rules to let the designers exploring more efficient solutions, and planning strategies towards enhancing the performance and reliability of studied machines. Next step of this study was to estimate the optimum completion time of each process in the considered manufacturing system and with consideration of the proposed scheduling methods.

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Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system

Cited by 126 publications

References 36 publications

Job scheduling and resource allocation in parallel‐machine system via a hybrid nested partition method

Job scheduling and resource allocation in parallel‐machine system via a hybrid nested partition method

Weapon Selection and Planning Problems Using MOEA/D with Distance-Based Divided Neighborhoods

Optimal Operation and Scheduling of Parallel Machines in Jobshop Environments

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