Evolutionary algorithms for multi-objective flexible job shop cell scheduling

Deliktaş, Derya; Özcan, Ender; Üstün, Özden; Torkul, Orhan

doi:10.1016/j.asoc.2021.107890

Cited by 24 publications

(11 citation statements)

References 90 publications

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“…Solving real-world high-dimensional production scheduling problems are computationally expensive and rarely used as benchmarks to test algorithms. A few studies applied these state-ofart multi-objective algorithms to solve scheduling problems [46][47][48][49][50][51] . In Annex A, the algorithms are briefly described.…”

Section: Subject Tomentioning

confidence: 99%

Modeling and optimization of bakery production scheduling to minimize makespan and oven idle time

Babor

Emire

Kohlus

et al. 2023

Sci Rep

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Makespan dominates the manufacturing expenses in bakery production. The high energy consumption of ovens also has a substantial impact, which bakers may overlook. Bakers leave ovens running until the final product is baked, allowing them to consume energy even when not in use. It results in energy waste, increased manufacturing costs, and CO2 emissions. This paper investigates three manufacturing lines from small and medium-sized bakeries to find optimum makespan and ovens’ idle time (OIDT). A hybrid no-wait flow shop scheduling model considering the constraints that are most common in bakeries is proposed. To find optimal solutions, non-dominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA2), generalized differential evolution (GDE3), improved multi-objective particle swarm optimization (OMOPSO), and speed-constrained multi-objective particle swarm optimization (SMPSO) were used. The experimental results show that the shortest makespan does not always imply the lowest OIDT. Even the optimized solutions have up to 231 min of excess OIDT, while the makespan is the shortest. Pareto solutions provide promising trade-offs between makespan and OIDT, with the best-case scenario reducing OIDT by 1348 min while increasing makespan only by 61 min from the minimum possible makespan. NSGA-II outperforms all other algorithms in obtaining a high number of good-quality solutions and a small number of poor-quality solutions, followed by SPEA2 and GDE3. In contrast, OMOPSO and SMPSO deliver the worst solutions, which become pronounced as the problem complexity grows.

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Section: Subject Tomentioning

confidence: 99%

Modeling and optimization of bakery production scheduling to minimize makespan and oven idle time

Babor

Emire

Kohlus

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Chiang and Lin [47] applied an evolutionary algorithm for multi-objective flexible scheduling to get a set of Pareto optimal solutions with diverse populations. Deliktas et al [48] applied evolutionary algorithm along with hill climbing approach for job scheduling. In their model, the objective function was guided weighted sum while searching for the optimum J o u r n a l P r e -p r o o f solution.…”

Section: Related Workmentioning

confidence: 99%

A multi-objective evolutionary optimisation model for heterogeneous vehicles routing and relief items scheduling in humanitarian crises

Mishra

Dahal

Pervez

et al. 2022

Decision Analytics Journal

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“…For the type of setup time, Refs. [6,14,18,22,24,30,34] consider the family job sequencedependent setup time; Refs. [19,28] consider two types of setup time, but machines do not have multiple patterns; others consider the job sequence-dependent setup time.…”

Section: Hfs With Sequence-dependent Setup Time (Hfs-sdst)mentioning

confidence: 99%

“…Constraint (6) calculates the completion time of every job at each stage, which is equal to the starting time of job j at stage s plus the processing time of job j allocated on machine h at stage s plus the setup time from its predecessor to j. Because there are two types of setup time, then the completion time of job j at stage s is calculated by the upper formulation of Constraint (6) when the setup time depends on the sequence of jobs, while it is calculated by the formulation of Constraint (6) when the setup time depends on both the sequence of jobs and the machine in which the jobs are allocated on. Constraint (7) defines the starting time of job j at stage s, which is set to the maximum between the completion time of job j at the previous stage and the available time of the machine processing job j.…”

Section: Mathematical Modelmentioning

confidence: 99%

Heuristics for the Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup times

Yong

et al. 2022

Mathematical Problems in Engineering

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This paper considers the hybrid flow shop scheduling problem with two types of sequence-dependent setup times, one depending on the order of jobs and the other depending on both the order of jobs and the machines where the jobs are allocated. Three constructive heuristic algorithms based on the NEH algorithm are developed with the object of minimizing the total energy consumption cost. The first algorithm, called EPRA, obtains the order of jobs using the energy consumption cost based on the processing time. The second algorithm, called ESRA, obtains the order of jobs using the energy consumption cost based on the setup time. The third algorithm, called ESPRA, takes advantage of both EPRA and ESRA by constructing fictitious jobs with ESRA and acquiring the sequence of fictitious jobs with EPRA. Due to the drawback of NEH algorithm for the FFS problem proposed in this paper, all three heuristic algorithms use EST rules to allocate the jobs to the machines. Two lower bounds are developed to evaluate the heuristic algorithms proposed in this paper. A computational experiment based on the split-plot design is developed to carry out the factor analysis, algorithm analysis, lower bounds analysis, and CPU running time analysis. The analysis reveals that all main factor interactions except the algorithm are significant; the ESPRA algorithm is generally outstanding among the three algorithms; LB1 is better than LB2; the CPU running time of ESPRA algorithm is significantly reduced compared to those of EPRA and ESST algorithms.

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Evolutionary algorithms for multi-objective flexible job shop cell scheduling

Cited by 24 publications

References 90 publications

Modeling and optimization of bakery production scheduling to minimize makespan and oven idle time

Modeling and optimization of bakery production scheduling to minimize makespan and oven idle time

A multi-objective evolutionary optimisation model for heterogeneous vehicles routing and relief items scheduling in humanitarian crises

Heuristics for the Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup times

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