Effect of sequencing rules on order release policies in a stochastic and dynamic job shop with sequence-dependent setup times – a simulation study

Rani, Sushma; Jain, Anuj; Angra, Surjit

doi:10.1088/1742-6596/1240/1/012002

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…It is the average time between two job arrivals (λ). It is determined by the formula given below 8,23,25) .…”

Section: Mean Inter-arrival Timementioning

confidence: 99%

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Assessing the Effect of Setup Times and Shop Utilization Levels on Performance of ORR Policies in a Stochastic Dynamic Job Shop with Sequence-Dependent Setup Times: A Simulation Approach

Rani¹,

Jain²,

Angra³

2022

Evergreen

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Order review and release (ORR) controls making jobs from planning stage to execution stage. This article assesses the effect of setup times and shop utilization levels on ORR policies performance in stochastic dynamic job shop (SDJS) considering sequence-dependent setup time (SDST). The system performance indicators such as mean throughput time (MTT), mean lead time (MLT), number of tardy jobs (NOTJ), and total setups (TS) are used to assess system's performance. For conducting experiments, the simulation model is created using Promodel ® simulation software. Results indicate that for a given ORR policy, at a given setup time and shop utilization level, as workload trigger level, increases MTT increases. In contrast, the other performance measures such as MLT, NOTJ, and TS decrease. Further, for a given ORR policy, as shop utilization decreases, MTT, MLT, and NOTJ decrease at all workload trigger levels for all setup time levels. In contrast, TS increase at all workload trigger levels for all setup time levels. Further, as setup time increases for a given ORR policy, MTT and MLT increase at all workload trigger levels for all shop utilization levels. In contrast, TS and NOTJ decrease.

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“…It is the average time between two job arrivals (λ). It is determined by the formula given below 8,23,25) .…”

Section: Mean Inter-arrival Timementioning

confidence: 99%

“…According to TWK method the due date is determined by taking sum of processing times and setup times of the job or number of operations to be performed on the job. The majority of researchers employ TWK approach to determine the due date of jobs as given by the equation 7,8,19,23,25,27) .…”

Section: Due Datementioning

confidence: 99%

Assessing the Effect of Setup Times and Shop Utilization Levels on Performance of ORR Policies in a Stochastic Dynamic Job Shop with Sequence-Dependent Setup Times: A Simulation Approach

Rani¹,

Jain²,

Angra³

2022

Evergreen

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Effect of Setup Times and Workload Trigger Levels on Order Review and Release Policies Performance in a Stochastic Dynamic Job Shop with Sequence-Dependent Setup Time — A Simulation Approach

Rani

Jain

Angra

2023

J. Adv. Manuf. Syst.

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Order review and release (ORR) in a job shop considering sequence-dependent setup time (SDST) is among the most challenging job shop problems. This study investigates the effect of varying setup times and workload trigger levels on ORR policies performance in a stochastic dynamic job shop considering SDST. It is essential to examine the effect of varying setup times and workload trigger levels on ORR policies, because, for different ORR policies, workload trigger level plays a major role to control the work-in-process on the shop floor. Further, in manufacturing industries, less setup time is required in some cases, but it can also be equal to or greater than the processing time in others. Promodel[Formula: see text] simulation software is used to develop the simulation model for conducting experiments. The performance of the system is evaluated in terms of mean lead time (MLT), mean throughput time (MTT), number of tardy jobs (NOTJ), and total setups (TS) performance measures. From results, it is found that for a given ORR policy and at a given setup time level, lower workload trigger level provides minimum MTT and maximum MLT, TS, and NOTJ performance measures. As the workload trigger level increases, MTT performance measure increases, whereas MLT, TS, and NOTJ performance measures decrease. Further, for a given ORR policy and workload trigger level, as the setup time level increases, MTT and MLT performance measures increase. In contrast, TS and NOTJ performance measures decrease. A stochastic dynamic job shop considering ORR and SDST where setup time is either less than, equal to or more than operation processing time is the novelty of the work.

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Effect of sequencing rules on order release policies in a stochastic and dynamic job shop with sequence-dependent setup times – a simulation study

Cited by 2 publications

References 16 publications

Assessing the Effect of Setup Times and Shop Utilization Levels on Performance of ORR Policies in a Stochastic Dynamic Job Shop with Sequence-Dependent Setup Times: A Simulation Approach

Assessing the Effect of Setup Times and Shop Utilization Levels on Performance of ORR Policies in a Stochastic Dynamic Job Shop with Sequence-Dependent Setup Times: A Simulation Approach

Effect of Setup Times and Workload Trigger Levels on Order Review and Release Policies Performance in a Stochastic Dynamic Job Shop with Sequence-Dependent Setup Time — A Simulation Approach

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