A hybrid genetic algorithm for job sequencing and worker allocation in parallel unrelated machines with sequence-dependent setup times

Costa, Antonio; Cappadonna, Fulvio Antonio; Fichera, Sergio

doi:10.1007/s00170-013-5221-5

Cited by 42 publications

(19 citation statements)

References 30 publications

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“…This occurs in manufacturing systems where there are alternative operators to perform each operation, and different operator assignments may lead to different processing times. This situation has been investigated by Costa et al (2013) who proposed a job scheduling model in a single-stage system with different operator's set up times to minimize makespan. Different with skill levels, processing times can change over time because of the so-called time-changing effects (Strusevich & Rustogi, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The batch scheduling model in Halim et al (1994) assumed fixed processing times. In practical situations, processing times can vary, such as from the existence of alternative operators studied in Costa, Cappadonna and Fichera (2013) and from time-changing effects of learning and forgetting studied in Jaber and Bonney (1996) and Yusriski et al (2015). Nevertheless, Costa et al (2013) and Yusriski et al (2015) only considered single-machine systems, while Jaber and Bonney (1996) have not considered batch scheduling.…”

Section: Introductionmentioning

confidence: 99%

“…This paper extends the batch scheduling model in Halim et al (1994) by considering the existence of alternative operators to perform each operation proposed in Costa et al (2013) and time-changing effects of learning and forgetting studied in Jaber and Bonney (1996) and Yusriski et al (2015). The effects of learning and forgetting to the objective function and decision variables will also be studied.…”

Section: Introductionmentioning

confidence: 99%

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A flow shop batch scheduling and operator assignment model with time-changing effects of learning and forgetting to minimize total actual flow time

Kurniawan

Raja

Suprayogi

et al. 2020

JIEM

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Purpose: This paper aims to investigate simultaneous problems of batch scheduling and operator assignment with time-changing effects caused by learning and forgetting.Design/methodology/approach: A number of parts will be processed in batches, each of which will be processed through a number of operations where there are alternative operators for each operation bringing different set up and processing times as operators experience different degree of learning and forgetting. A mathematical model is developed for the problems, and the decision variables are operator assignment, the number of batches, batch sizes and the schedule of the resulting batches. A proposed algorithm works by trying different number of batches, starting from one, and increasing the number of batches one by one until the objective function value does not improve anymore.Findings: We show both mathematically and numerically that the closest batch to the due date always becomes the largest batch in the schedule, and the faster operators learn, the larger the difference between the closest batch to the due date and the other batches, the lower optimal number of batches, and the lower the total actual flow time.Originality/value: Previous papers have considered the existence of alternative operators but have not considered learning and forgetting, or have considered learning and forgetting but only in a single-stage system and without considering alternative operators.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A flow shop batch scheduling and operator assignment model with time-changing effects of learning and forgetting to minimize total actual flow time

Kurniawan

Raja

Suprayogi

et al. 2020

JIEM

View full text Add to dashboard Cite

show abstract

“…Following this pattern, [14] use a MIP formulation for an unrelated parallel machine scheduling problem with limited resources. An operator is needed for each setup activity between two consecutive jobs on the same machine.…”

Section: Related Workmentioning

confidence: 99%

Workforce influence on manufacturing machines schedules

Caricato

Grieco

Arigliano

et al. 2020

Int J Adv Manuf Technol

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This study addresses a parallel machines scheduling problem with sequence-dependent setup times and additional resource constraints related to workforce management. In most industrial cases, the execution of jobs requires the involvement of human resources in addition to machines: this work addresses the many complications due to workforce-specific issues that arise in a real industrial application. This is achieved separating the complex yet classical parallel machines scheduling problem with sequence-dependent setup times from the additional human resources planning problem: the former is formulated and solved through constraint programming, while an ad hoc procedure is provided for the latter. An Italian specialized firm, Prosino S.r.l., provides the industrial case to both validate the adequacy of the adopted method to the actual industrial problem and test the effectiveness of the proposed approach. Computational results obtained over six months of experimentation at the partner firm are presented.

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“…Whenever a real problem should be addressed through an evolutionary algorithm, the choice of a proper encoding scheme (i.e., the way a solution is represented by a string of genes) plays a key role under both the quality of solutions and the computational burden viewpoints [31]. In addition, a valid decoding procedure able to transform a given string into a feasible solution should be provided.…”

Section: The Proposed Optimization Proceduresmentioning

confidence: 99%

A Hybrid Metaheuristic Approach for Minimizing the Total Flow Time in A Flow Shop Sequence Dependent Group Scheduling Problem

2014

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Production processes in Cellular Manufacturing Systems (CMS) often involve groups of parts sharing the same technological requirements in terms of tooling and setup. The issue of scheduling such parts through a flow-shop production layout is known as the Flow-Shop Group Scheduling (FSGS) problem or, whether setup times are sequence-dependent, the Flow-Shop Sequence-Dependent Group Scheduling (FSDGS) problem. This paper addresses the FSDGS issue, proposing a hybrid metaheuristic procedure integrating features from Genetic Algorithms (GAs) and Biased Random Sampling (BRS) search techniques with the aim of minimizing the total flow time, i.e., the sum of completion times of all jobs. A well-known benchmark of test cases, entailing problems with two, three, and six machines, is employed for both tuning the relevant parameters of the developed procedure and assessing its performances against two metaheuristic algorithms recently presented by literature. The obtained results and a properly arranged ANOVA analysis highlight the superiority of the proposed approach in tackling the scheduling problem under investigation.

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A hybrid genetic algorithm for job sequencing and worker allocation in parallel unrelated machines with sequence-dependent setup times

Cited by 42 publications

References 30 publications

A flow shop batch scheduling and operator assignment model with time-changing effects of learning and forgetting to minimize total actual flow time

A flow shop batch scheduling and operator assignment model with time-changing effects of learning and forgetting to minimize total actual flow time

Workforce influence on manufacturing machines schedules

A Hybrid Metaheuristic Approach for Minimizing the Total Flow Time in A Flow Shop Sequence Dependent Group Scheduling Problem

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