Discrete-Event Simulation and Optimization of Spare Parts Inventory and Preventive Maintenance Integration Model Considering Cooling Down and Machine Dismantling Time Factor

Coverage is one of the most critical performance metric of wireless sensor networks (WSNs) because it shows how well a region of interest (RoI) is being monitored by the deployed network. In general, the RoI is either circular or rectangular in shape which have boundary regions. Sensor nodes (SNs) deployed in these regions suffer boundary effects (BEs), i.e., the useful coverage area of an SN deployed near the boundary regions is less as compared to the SNs deployed in the middle of the RoI. It is imperative to consider these BEs while evaluating the performance of WSNs because analytical results derived for large networks are not valid for finite networks. In addition, SNs of a deployed WSN are prone to failure due to a large number of factors such as battery drainage, high temperature, and other environmental conditions. Earlier researchers have ignored the impact of BEs in the presence of sensor failure while evaluating the coverage performance of WSNs. In this work, we derive an analytical model by considering BEs and sensor failure to achieve a closed form expression for the k-coverage performance of a WSN deployed in a rectangular RoI. Further, we analyze the influence of various network parameters such as number of SNs, sensing range, and sensor failure rate on the k-coverage performance of the network. The results obtained using the proposed model show a good match with simulations outcomes with Root Mean Square Error (RMSE) no more than 0.03, thus, validating our model. For 𝑟𝑟 𝑠𝑠 = 80 m and N = 100, 1-coverage probabilities are found to be 0.9874, 0.9313 and 0.8069 for k = 1, 2 and 3 respectively showing that the k-coverage probability deriorates with the increase in the value of k.

“…As a part of future work, we can use various optimization 29,30) and machine learning-based algorithms 31,32,33,34) to further improve the model performance.…”

Section: Discussionmentioning

confidence: 99%

Estimating the Coverage Performance of a Wireless Sensor Network Considering Boundary Effects in the Presence of Sensor Failure

Mini¹,

Pal²

2021

“…In this article, the system modeling is calculated numerically using a computer program. The research on simulation models conducted by most researchers and academicians uses many empirical models to verify theories and solve complex calculations 18,19) . The floating crane design used in the simulation model is a bargeshaped structure that can be called a floating crane barge.…”

Section: Model Descriptionmentioning

confidence: 99%

Preliminary Modeling of Coupled Motion Response for Floating Crane and Suspended Module by Using Boundary Element Approach and Empirical Model

Firdaus,

Ali,

Hidayat

et al. 2023

External forces and environments can destabilize the floating crane's performance and inflict a high risk of failure in operation. Considering this, the effect of the suspended module during the lifting operation in sea waves is investigated to evaluate the dynamic response of the floating crane. In this article, the combination of potential flow theory in the Boundary Element Method (BEM) and an empirical model is proposed for the prediction of coupled motion on the floating crane and suspended module. Calculations of multibody systems are solved using the coupled motion formula based on the frequency domain method. An analysis of the lifting operation under beam waves is examined with two variations in the weight of the suspended module. The motion characteristics are depicted with the response amplitude operator (RAO) on the nine rigid body degrees of freedom. The resonances of peak frequency occurred in the floating crane's responses due to the lifting load system. The peak frequency of the roll response on the floating crane shifted to a higher frequency due to the impact of the pendulum motion. The difference in peak response of roll motion between variations of suspended load is about 25.8 %. The acceleration response of the suspended module in the z-direction for the large module presented a lower peak frequency than the smaller module.

“…Simulation modeling assists in analyzing the manufacturing system in a better way [28][29][30][31][32] . Using ProModel ® software, the current study created a simulation model of ORR in a job shop with three setup times and shop utilization levels.…”

Section: Configuration Of Simulation Modelmentioning

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

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.