Reactive Scheduling Addressing Unexpected Disturbance in Cellular Manufacturing Systems

Arkat, Jamal; Rahimi, Vahid; Farughi, Hiwa

doi:10.5829/ije.2021.34.01a.18

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Other important articles in the subject area of this research are: Arkat et al [29] on their article entitled "Reactive Scheduling Addressing Unexpected Disturbance in Cellular Manufacturing Systems" focussed on scheduling in CMfg. Puspitasari et al [30] on their work entitled "Generator Scheduling Optimization Involving Emission to Determine Emission Reduction Costs" also paid attention on cost anlysis of CMfg.…”

Section: Related Workmentioning

confidence: 99%

Rescheduling Unreliable Service Providers in a Dynamic Multi-objective Cloud Manufacturing

Fazeli

Farjami

Bidgoly

2023

IJE

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Cloud manufacturing (CMfg) is a new advanced manucatring model developed with the help of enterprise information technologies under the support of cloud computing, Internet of Things and service-based technologies. CMfg compose multiple manufacturing resources to provide efficient and valuable services. CMfg has a highly dynamic environment. In this environment, many disruptions or events may occur that lead the system to unplanned situations. In CMfg, a series of service providers are scheduled for production. During the production operation, some of them may be damaged, stopped, and out of service. Therefore, rescheduling is necessary for the continuation of the production process according to the concluded contracts and initial schedule. When any disruptions or other events occurred, the rescheduling techniques used to updating the inital schedule. In this paper, the dynamic rescheduling problem in CMfg is analyzed. Then the multi-objective rescheduling in CMfg is modeled and defined as a multi-objective optimization problem. Defining this problem as a multi-objective optimization problem provides the possibility of applying, checking and comparing different algorithms. For solving this problem, previous optimization methods have improved and a multi-objective and elitist algorithm based on the Jaya algorithm, called advanced multi-objective elitist Jaya algorithm (AMEJ) is proposed. Several experiments have been conducted to verify the performance of the proposed algorithm. Computational results showed that the proposed algorithm performs better compared to other multiobjective optimization algorithms.

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Section: Related Workmentioning

confidence: 99%

Rescheduling Unreliable Service Providers in a Dynamic Multi-objective Cloud Manufacturing

Fazeli

Farjami

Bidgoly

2023

IJE

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“…Cellular manufacturing (CM) is a well-known just-intime manufacturing process which aims to increase production efficiency by assigning multi-skilled workers, parts, and machines into cells significantly cheaper than other kinds of producing processes [1][2][3][4]. CM systems can produce medium-volume and medium-variety parts to decrease setup cost, lead time, inventory cost, worker allocation cost, worker salary cost, and inventory cost and improve material flow and product quality [5,6].…”

Section: Introductionmentioning

confidence: 99%

Integrated Dynamic Cellular Manufacturing Systems and Hierarchical Production Planning with Worker Assignment and Stochastic Demand

Bakhshi-Khaniki

Ghomi

2023

IJE

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This study deals with the interaction of dynamic cellular manufacturing (DCM) and hierarchical production planning (HPP) problems with stochastic demands for the first time. Each of these alone does not consider the system factors such as stochastic demands and dynamic cellular formation separately. Accordingly, to fill this gap, this paper presents an integrated optimized model incorporating the most comprehensive design of DCM systems and HPP problems with stochastic demands. This model helps administrators get the optimal size and number of cells to decrease costs. Also, the model applies the principles of HPP to reduce the complexity of the integrated model. Since demands are uncertain, they need to be accurately predicted. Therefore, this study aims to combine the most precise decision variables with the most realistic conditions. A case study from an agriculture mechanization and industrial development company shows that an integrated model can provide managers with a feasible solution to meet demand, reconfigure cells in each period, provide new machinery to increase the required production capacity, and adjust production capacity to help them cope with demand fluctuations. A sensitivity analysis was performed and the results show that increase in forecast error and inter-cell move cost cause less significant changes in total cost but the total cost is sensitive to intra-cell move cost, available time capacities and cell quantity. It is also shown that the total cost was very sensitive to available regular time and available over time and the system should try to increase the time capacity.

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“…Rahimi et al [21] presented a mathematical model to address the integrated cell formation and cellular rescheduling problems in a cellular manufacturing system. As a reactive model, the model is developed to handle the arrival of a new job as a disturbance to the system.…”

Section: Introductionmentioning

confidence: 99%

A joint optimization model for production scheduling and preventive maintenance interval

Babaeimorad

Fatthi

Fazlollahtabar

2021

IJE

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Reactive Scheduling Addressing Unexpected Disturbance in Cellular Manufacturing Systems

Cited by 4 publications

References 12 publications

Rescheduling Unreliable Service Providers in a Dynamic Multi-objective Cloud Manufacturing

Rescheduling Unreliable Service Providers in a Dynamic Multi-objective Cloud Manufacturing

Integrated Dynamic Cellular Manufacturing Systems and Hierarchical Production Planning with Worker Assignment and Stochastic Demand

A joint optimization model for production scheduling and preventive maintenance interval

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