Plan Parameters Research Articles

Stochastic energy planning of a deltoid structure of interconnected multilateral grids by considering hydrogen station and demand response programs

This paper presents triple-objective stochastic energy planning and management of a deltoid structure in which a microgrid, nano-grid, and main grid connect and exchange power simultaneously. In addition, the impact of hydrogen stations due to the growth of hydrogen vehicles and their crucial role in the power system's future to reduce pollution is also discussed. Moreover, the effect of time-based demand response programs (TOU) according to the elasticity matrix (different operators and price-sensitive flexible loads) for the proposed multilateral grid is investigated under diverse scenarios. Stochastic planning is performed to make results more realistic and authentic. The uncertain parameters for stochastic planning include wind pace, solar radiation, fuel rate, and various demands. The assumed triple objective functions for the proposed planning are the microgrid's profit, the nano-grid's cost, and the total multilateral grid's pollution. The problem is modeled as mixed-integer linear programming (MILP) and solved using the GAMS and LP metric approach. The final results show that by implementing the supposed planning, the microgrid's profit increases by about 22.53 $/day (10.8%), and the nano-grid's cost decreases by about 1.31 $/day (9.8%). On the other hand, the total environmental pollution is reduced significantly and reaches 1.06 kg/day.

Research on reliable coverage planning of substation based on equipment service level

Abstract 5G network can meet the multi-type service access needs of substations and promote the intelligent upgrade and transformation of power grids. The layout of buildings and electrical equipment in substations, as well as electromagnetic interference, will have a greater impact on 5G signal coverage. In this paper, a three-dimensional simulation model of the substation is first established, and basic parameters of base station planning are determined. Then, a safety function considering equipment service level and an electromagnetic interference calculation method are defined. Finally, a coverage planning algorithm based on electrical equipment service level and received power quality is proposed to improve the reliability of service access and ensure the safe and stable operation of the system. Simulation results show that the algorithm is effective and reliable.

Dosiomics-based detection of dose distribution variations in helical tomotherapy for prostate cancer patients: influence of treatment plan parameters.

The stability of dosiomics features (DFs) and dose-volume histogram (DVH) parameters for detecting disparities in helical tomotherapy planned dose distributions was assessed. Treatment plans of 18 prostate patients were recalculated using the followings: field width (WF) (2.5 vs. 5), pitch factor (PF) (0.433 vs. 0.444), and modulation factor (MF) (2.5 vs. 3). From each of the eight plans per patient, ninety-three original and 744 wavelet-based DFs were extracted, using 3D-Slicer software, across six regions including: target volume (PTV), pelvic lymph nodes (PTV-LN), PTV + PTV-LN (PTV-All), one cm rind around PTV-All (PTV-Ring), rectum, and bladder. For the resulting DFs and DVH parameters, the coefficient of variation (CV) was calculated, and using hierarchical clustering, the features were classified into low/high variability. The significance of parameters on instability was analyzed by a three-way analysis of variance. All DF's were stable in PTV, PTV-LN, and PTV-Ring (average CV ( ≤0.36). Only one feature in the bladder ( =0.9), rectum ( =0.4), and PTV-All ( =0.37) were considered unstable due to change in MF in the bladder and WF in the PTV-All. The value of for the wavelet features was much higher than that for the original features. Out of 225 unstable wavelet features, 84 features had ≥1. The CVs for all the DVHs remained very small ( <0.06). This study highlights that the sensitivity of DFs to changes in tomotherapy planning parameters is influenced by the region and the DFs, particularly wavelet features, surpassing the effectiveness of DVHs.

Learning three-dimensional aortic root assessment based on sparse annotations.

Analyzing the anatomy of the aorta and left ventricular outflow tract (LVOT) is crucial for risk assessment and planning of transcatheter aortic valve implantation (TAVI). A comprehensive analysis of the aortic root and LVOT requires the extraction of the patient-individual anatomy via segmentation. Deep learning has shown good performance on various segmentation tasks. If this is formulated as a supervised problem, large amounts of annotated data are required for training. Therefore, minimizing the annotation complexity is desirable. We propose two-dimensional (2D) cross-sectional annotation and point cloud-based surface reconstruction to train a fully automatic 3D segmentation network for the aortic root and the LVOT. Our sparse annotation scheme enables easy and fast training data generation for tubular structures such as the aortic root. From the segmentation results, we derive clinically relevant parameters for TAVI planning. The proposed 2D cross-sectional annotation results in high inter-observer agreement [Dice similarity coefficient (DSC): 0.94]. The segmentation model achieves a DSC of 0.90 and an average surface distance of 0.96mm. Our approach achieves an aortic annulus maximum diameter difference between prediction and annotation of 0.45mm (inter-observer variance: 0.25mm). The presented approach facilitates reproducible annotations. The annotations allow for training accurate segmentation models of the aortic root and LVOT. The segmentation results facilitate reproducible and quantifiable measurements for TAVI planning.

Generating 3D images of VMAT plans for predictive models and activation maps associated with plan deliverability.

Intensity modulation with dynamic multi-leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient-specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP-specific information upon QA failures. By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP-specific regions associated with plan deliverability. The patient cohort consisted of 259 and 190 cases for left- and right-breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three-channel 3D images generated from DICOM RT plans were fed into a DenseNet-based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC). The gradient-weighted class activation map (Grad-CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation. The DenseNet-based predictive model yielded AUCs of 92.2% and 93.8%, F1-scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left- and right-breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing-labeled from passing-labeled classes for the non-deliverable cases. The PCM with the highest correlation to the Grad-CAM varied from patient cases, implying that plan deliverability would be considered patient-specific. This work demonstrated that the deep learning-based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control-point specific planning parameter information associated with plan deliverability in a patient-specific manner.

Optimal and economic design of multiple deferred state repetitive group sampling plan for assuring mean life under a family of lifetime distributions

In this paper, a designing methodology of the multiple deferred (dependent) state repetitive group sampling plan is developed to ensure the mean lifetime of the products under the truncated life test, which follows a family of different lifetime distributions. An approach of two points on the operating characteristic curve is followed to obtain the optimal plan parameters with the known shape parameter values for various mean life ratios under different lifetime models. The proposed sampling plan is illustrated with a numerical example. The application of the proposed sampling plan is explained with a real-life example based on Burr XII distribution. Comparison is made among the performances of the proposed sampling plan using the different lifetime distributions; these performances are also compared with a single sampling plan using Burr XII distribution based on mean life assurance. Finally, an economic design of the proposed sampling plan is discussed, along with some cost models to minimize the total cost.

Optimizing volumetric modulated arc therapy prostate planning using an automated Fine-Tuning process through dynamic adjustment of optimization parameters

In radiotherapy treatment planning, optimization is essential for achieving the most favorable plan by adjusting optimization criteria. This study introduced an innovative approach to automatically fine-tune optimization parameters for volumetric modulated arc therapy prostate planning, ensuring all constraints were met. A knowledge-based planning model was invoked, and the fine-tuning process was applied through an in-house developed script. Among 25 prostate plans, this fine-tuning increased the number of plans meeting all constraints from 10/25 to 22/25, with a reduction in mean monitor units per gray without increasing plan’s complexity. This automation improved efficiency by saving time and resources in treatment planning.

3D volume construction methodology for cold spray additive manufacturing

Cold spraying has proved as an attractive and rapidly developing solid-state material deposition process that allows for fast formation of high quality, large 3D volume objects. Low risks of undesirable heat effects lead to increased interest in cold spraying based rapid additive manufacturing. However, by continuous powder spraying and high-pressure gas operation, cold spray additive manufacturing in terms of shape building is sensitive to operating parameters and imposes high requirements on the control of process conditions and locally needed kinematics. Every step of the manufacturing process therefore needs to be meticulously conceived and planned, especially the toolpath planning and implementation. This is not only essential to meet basic performance requirements, but also needed for the desired geometric accuracy. To address these needs, this study presents a new implementation method for cold spray additive manufacturing to improve manufacturing accuracy and flexibility. The workflow and principles of the proposed method are explained and strategies are presented for building up various component types, including rotational geometries as well as freestanding parts. The developed algorithms for 3D applications can be well integrated into the cold spray additive manufacturing process for toolpath planning and path parameter determination. Reliable and accurate robot programs were developed to make the process easy to repeat and to follow. Toolpath planning and robot programming are supported and performed by applying virtual cells to simulate the automation process for the real scenario. Path parameter adjustment and kinematic optimization can then be conducted according to the feedback from the simulation result. Applied benchmarking tests prove acceptable shape accuracy and demonstrate that the current method can enhance the capabilities of cold spray additive manufacturing for near-net shape construction. This implies that careful planning and manufacturing strategies should enable overcoming the challenges associated with cold spray additive manufacturing.

Feasibility of Left Anterior Descending Coronary Artery Sparing Radiation Therapy for Locally Advanced Lung Cancer

Efforts to mitigate radiotherapy (RT) associated cardiotoxicity have focused on constraining mean heart dose (MHD). However, recent studies have shown greater predictive power with cardiac substructure dose metrics such as the left anterior descending (LAD) coronary artery volume (V) receiving 15Gy (V15Gy) ≥10%. Herein, we investigated the feasibility of LAD radiation sparing in contemporary IMRT/VMAT lung cancer plans. Single institution retrospective analysis of 54 locally advanced lung cancer patients treated with thoracic RT between February 2018-August 2021. After excluding 33 (5=non-IMRT/VMAT or intentionally LAD-optimized; 28=LAD V15Gy<10%), 21 plans with LAD V15Gy ≥10% were identified for LAD re-optimization with intent to meet LAD V15Gy <10% while maintaining meeting organ-at-risk (OAR) metrics and target coverage with original plan parameters. Dosimetric variables were compared using paired t tests. Most (57.1%, 12/21) were treated with definitive RT, 8/21 (38.1%) with post-operative RT, and one with neoadjuvant RT. The median prescribed RT dose was 60Gy (range 50.4-66Gy) in 30 fractions (range 28-33). LAD re-optimized plans (vs original) led to significant reductions in mean LAD V15Gy (39.4% ±13.9% vs 9.4% ±13.0%; p<0.001) and mean LAD dose (12.9Gy ± 4.6Gy vs 7.6Gy ±2.8Gy; p<0.001). Most (85.7%; 18/21) LAD re-optimized plans achieved LAD V15Gy <10%. There were no statistically significant differences in overall lung, esophageal, or spinal cord dose metrics. Only one re-optimization (1/21) exceeded an OAR constraint that was initially met in the original plan. To our knowledge, this is the first report describing the feasibility of LAD-optimized lung cancer RT planning using the newly identified LAD V15 Gy constraint. We observed that LAD V15Gy <10% is achievable in more than 85% of plans initially exceeding this constraint, with minimal dosimetric tradeoffs. Our results support the feasibility of routine incorporation of the LAD as an OAR in modern thoracic IMRT/VMAT planning.

Impact of histopathological parameters in prognosis of oral squamous cell carcinoma

AbstractObjectiveSquamous cell carcinomas comprise approximately 90% of all oral malignancies. There is a wide geographical variation in the incidence of oral cancer, with South and South East Asia (SSEA) accounting for almost two third of new cases. The prognosis of oral cancer is influenced by a vast array of factors including demographic, clinical, histopathological and molecular factors. The objective this review is to analyse the impact of histopathological features assessed in hematoxylin and eosin stained sections on the prognosis of OSCC.Materials and MethodsMedline and Scopus data base search was performed in order to identify related articles on histopathological parameters in predicting prognosis of oral squamous cell carcinoma. The primary emphasis is on the studies conducted in SSEA, with an accompanying comparison of their findings with those from research conducted in other parts of the world.ResultsIt has been shown that the number of studies conducted in SSEA is not proportionate to the high prevalence of Oral Cancer in the region. There is no significant difference between the findings from SSEA compared to the rest of the world. It is clearly shown that most histopathological parameters can be accurately used to predict nodal metastasis and prognosis.ConclusionsHistopathological parameters can be used reliably in planning treatment of Oral cancer. Clinicians should combine clinical and histopathological parameters in drawing treatment plan for Oral Cancer.

The effect of Inc parameter on VMAT radiotherapy plans quality for rectal cancer using Monaco TPS.

To investigate the effect of the Increment of gantry angle (Inc) parameter setting of the Monaco Treatment planning system (Monaco TPS) on the dosimetry and quality parameters of the volumetric modulated arc therapy (VMAT) program for rectal cancer. A retrospective analysis was conducted on 50 patients with rectal cancer who underwent intensity modulated radiation therapy using the Monaco TPS system from 2020 to 2021. Under the same optimization function configuration and other parameter settings, the Inc parameters in the VMAT radiotherapy plan were set to 10°, 20°, 30°, and 40°. The dose-volume histogram (DVH) was used to evaluate the dose distribution of the target area and the radiation dose of the organs at risk (OAR). The differences in the dosimetry of the planning target volume (PTV) and OAR, as well as the gamma pass rate (GPR) were compared. In terms of target dose, D98, Dmin, HI, and conformity index (CI) of Inc10 group was significantly lower than those of Inc20, 30, and 40 groups (P<0.05), and D2 of Inc10 group was significantly higher than that of Inc20 group (P=0.009). We also found CI of Inc20 and 30 were significantly better than that of Inc40 (both P<0.05). In terms of OAR dose, the study found that the Dmean, Dmin, V50%, V45%, and V40% for the bladder of the Inc10 group were lower than those of the other groups (all P<0.05), the Dmean for femoral head of the Inc20 group was lower than that of the Inc30 group (P<0.05), and Inc20 showed a better protective effect on the femoral head. The MUs tend to decrease as the Inc parameter setting is increased. The monitor unit (MU) in Inc10 group were significantly higher than those in Inc20, Inc30, and Inc40 groups, and the MU of Inc20 group was significantly higher than that of Inc40 group (both P<0.05). We found that for the 3%/3mm and 2%/2mm standards, the GPRs of each plan were>90%, which met clinical requirements. Different settings of Inc parameters have varying degrees of impact on target dose, OAR dose, and machine MU. It is important for doctors to choose different Inc parameters according to different clinical needs.

Misfiring and still succeeding: Seeking success in megaprojects amidst changing regulatory environments

This paper analyses the complexities involved in planning and managing infrastructural megaprojects under changing regulatory environments. Through the case of the megaprojects by Autostrade, a motorway construction company, the paper illustrates that the notion of success is problematic and not easily defined when changes in regulations by regulatory bodies become sources of overflows, inducing changes in plans that had been previously agreed upon.Since regulations are dynamic, there may be a limit to which the initial planning parameters are legitimate benchmarks for the actualized megaproject's cost, time, and quality. It follows that a megaproject's success can result in orchestrating actions and reactions that involve both the capability of envisioning boundaries and demolishing them. This has severe implications for the notion of success itself, which does not easily fall into the traditional perspective of goal fulfillment but rather is linked to the inability to perceive the project's incompleteness.

Reducing the lateral dose penumbra in IMPT by incorporating transmission pencil beams

ObjectiveIn intensity-modulated proton therapy (IMPT), Bragg peaks result in steep distal dose fall-offs, while the lateral IMPT dose fall-off is often less steep than in photon therapy. High-energy pristine transmission (‘shoot through’) pencil beams have no Bragg peak in the patient, but show a sharp lateral penumbra at the target level. We investigated whether combining Bragg peaks with Transmission pencil beams (‘IMPT&TPB’) could improve head-and-neck plans by exploiting the steep lateral dose fall-off of transmission pencil beams. ApproachOur system for automated multi-criteria IMPT plan optimisation was extended for combined optimisation of BPs and TPBs. The system generates for each patient a Pareto-optimal plan using a generic ‘wish-list’ with prioritised planning objectives and hard constraints. For eight nasopharynx cancer patients (NPC) and eight oropharynx cancer (OPC) patients, the IMPT&TPB plan was compared to the competing conventional IMPT plan with only Bragg peaks, which was generated with the same optimiser, but without transmission pencil beams. Main resultsClinical OAR and target constraints were met in all plans. By allowing transmission pencil beams in the optimisation, on average 14 of the 25 investigated OAR plan parameters significantly improved for NPC, and 9 of the 17 for OPC, while only one OPC parameter showed small but significant deterioration. Non-significant differences were found in the remaining parameters. In NPC, cochlea Dmean reduced by up to 17.5 Gy and optic nerve D2% by up to 11.1 Gy. ConclusionCompared to IMPT, IMPT&TPB resulted in comparable target coverage with overall superior OAR sparing, the latter originating from steeper dose fall-offs close to OARs.

Comparing the performance of the Cpk and (X‐bar, S) sampling plans

AbstractWhen the Cpk sampling plan is in use, a sample of size (n) is taken from the lot, and the mean and the standard deviation of the sample observations are used to obtain the —the sample estimator of the Cpk index; if the is lower than a threshold (c0), then the lot is rejected, otherwise, the lot is accepted. In recent studies, the α and β risks are the risks of rejecting/accepting lots of items produced by in‐control/out‐of‐control processes with Cpks equal to (, that is, the two parameters (n, c0) of the Cpk sampling plans depend on the inputs (α, , βmax¸ ). When the design of the Cpk sampling plan is based on the inputs C0 and C1, the optimum sample size is always big and, excluding the cases where the magnitude of the mean shift is too small, the β risks associated to the combinations of mean shifts and variance increases (determined by the inputs C0 and C1) are always equal to the maximum allowed value βmax. This fact motivated us to compare the Cpk sampling plan with the (X‐bar, S) sampling plan, where the sample mean (X‐bar) and the sample standard deviation (S) are directly compared with thresholds. For most disturbances, the (X‐bar, S) sampling plan requires smaller samples to meet the condition of β ≤ βmax, that is, for a fixed sample size, it is always possible to find endless combinations of mean shifts and variance increases where the β risks of the (X‐bar, S) sampling plan are β = βmax, and the β risks of the Cpk sampling plan are β &gt; βmax.

The effect of auto-planning parameter settings on the quality of automated cervical cancer plans

PurposeThis study refines the parameters of automatic planning to improve plan quality and enhance the protection of organs at risk (OARs). Materials & methods30 cases of cervical cancer underwent manual (M-VMAT) and automated VMAT planning. The automated planning used five parameters: Engine Type (Biological and Non-Biological) and Use Cold-Spot ROIs (Yes and No) were qualitative; Tuning Balance (1–100%, default: 11%), Dose Fall-off Margin (1.0–4.5 cm, default: 2.6 cm), and Hot-Spot Maximum Goal (101–110%, default: 107%) were quantitative. The original automated plans (A1-VMAT) utilized default parameters, while the adjusted automated plans (A2-VMAT) utilized modified parameters. Dosimetric parameters and plan complexities were compared among M-VMAT, A1-VMAT, and A2-VMAT. ResultsCompared to M-VMAT, the A1-VMAT underperformed in protecting OARs. However, the A2-VMAT effectively addressed this issue. When adjusting “Use Cold-Spot ROIs” to “No”, “Tuning Balance” to 100%, and “Hot-Spot Maximum Goal” to 110%, while keeping the remaining parameters at their default settings, A2-VMAT significantly improved OAR protection in this study. Compared to A1-VMAT, the A2-VMAT showed only a slight increase in bowel's Dmax (approximately 2.11%, P < 0.05), while various OARs achieving significant dose reductions. Notably, there were significant reductions (over 30%, P < 0.05) in V35, V40 of rectum, V25, V30, V35 of femoral heads, and V10, V15, V20 of kidney. Furthermore, the efficacy of these five parameters was also observed in this study. Except for Engine Type, adjustments to the other parameters impacted Conformity Index, Homogeneity Index, and maximum dose (Dmax) for the PTV. Engine Type affected the average dose to OARs, and Hot-Spot Maximum influenced the Dmax for OARs overlapping with the PTV. ConclusionThe adjusted automated plan improved plan quality compared to the original automated plan, enhancing OAR protection. The adjusted plan demonstrated lower complexity than the manual plan.

Two-Stage Life Test Acceptance Sampling Plans for the Weibull Distribution using Trinomial Distribution

In materials engineering applications, lifetime of product is one of the quality characteristics of interest. Lifetimes of products are inspected in life tests upto a predetermined time t0, which may observe early failure of some of the products; incorporation of this fact may not only reduce the cost of inspection, but also provide some more insight into the failure mechanism. In this paper, we consider a two-stage time censoring scheme by dividing test truncation time t0 into two parts [.. formula..] and observe the number of failures in each interval, considering the remainder items as censored at t0. The trinomial distribution has been used to develop life test acceptance sampling plans for establishing quantile life of items having Weibull lifetimes. The proposed life test sampling plan gives added advantage to producers and extra safeguard to consumers, as compared to the life test sampling plans available in the literature for the Weibull distribution. Tables of plan parameters are constructed through code written in R 4.1.2, and illustrated for some real-life industrial cases.. KEYWORDS :Quantile, Two-stage censoring, Trinomial distribution, Weibull distribution.

An improved skip-lot sampling scheme with resampling mechanism using an advanced capability index

In today’s competitive market, product quality holds significant importance for consumers, motivating companies to prioritise high-quality product development and enhance their competitiveness. Acceptance sampling plans have proven to be indispensable tools in quality control practices, enabling producers and consumers to protect their interests by establishing evaluation rules for product batches. As defective rates decrease in modern manufacturing, skip-lot sampling plans (SkSP) have emerged as a suitable way for lots exhibiting stable and excellent product quality. Additionally, repetitive group sampling plan (RGSP) offers a cost-effective way to maintain desired protection for both parties, especially in costly or destructive inspection scenarios. Therefore, this study integrates the benefits of process capability indices, SkSP, and RGSP to develop a new variables SkSP with RGSP as its reference plan based on an advanced capability index Cpmk (called Cpmk -based SkSP-RGSP). The proposed plan achieves comparable discriminatory power to traditional sampling plans while significantly reducing the required sample size. Detailed tables of plan parameters are given to facilitate effective implementation for practitioners.

Coping with the uncertainties of make-to-order production: a new approach for determining reliable delivery times with the throughput diagram

Make-to-order companies have to deal with particular uncertainties in their offer processing. At the early stage of planning offers, it is particularly uncertain 1) which open offers will be accepted by customers and 2) what capacity these will require. Therefore, it is particularly challenging for make-to-order companies to determine and keep to binding delivery times, so that many companies have problems achieving a high delivery reliability. In this paper, we show a simple model-based procedure to determine delivery times using the throughput diagram with early available information such as the offer requests in the planning process. Simulation studies show that the procedure, especially in combination with a CONWIP order release and an EODD sequencing rule, achieves a high delivery reliability without sacrificing other logistic objectives such as utilisation. Furthermore, the procedure proves to be very robust against random deviations of the planning parameters.

Determination and economic design of repetitive group sampling plan under two parameter Lindley distribution

ABSTRACT A designing procedure of repetitive group sampling plan for assuring mean life under a two-parameter Lindley distribution is proposed, and the optimization model is developed for finding the optimal plan parameters such that the demands of both supplier and buyer are satisfied simultaneously. A real-time data set of ball bearings is used to explain the application of the proposed plan. A comparison between the proposed plan and the single sampling plan is done to reveal the better performance of the proposed plan based on operating characteristic function and average sample number. Applying the proposed plan to provide lifetime assurance reduces the inspection effort and related costs. We discuss the design of the proposed plan with an economic aspect. In addition, the sensitivity of the optimal sampling plans concerning the failure probability and cost parameters is analyzed. Finally, it is evident from the study that the proposed plan will be preferable to a single sampling plan for providing quality assurance with a minimum cost of the inspection.

Optimal designing of skip-lot sampling plan with a provision for reducing the normal inspection using the Taguchi process capability index

A product with a strong track record of quality might have less inspection according to the skip-lot sampling plan. As a result, skip-lot sampling plans are created to cut down the inspection cost with desired protection. The skip-lot sampling method is also reliable, practical, and favorable from an economic standpoint. As a result, this work develops the SkSP-V plan, a novel system of skip-lot sampling plan, based on the Taguchi process capability index. The proposed plan stipulates that any lot is rejected during sampling inspection, normal inspection must resume, however, it also allows for a reduced level of normal inspection in the event that higher product quality is demonstrated. The exact sampling distribution of the estimated Taguchi capability index is used to derive the operating characteristic function of the proposed plan. An optimization model is employed to establish the optimal plan parameters, guaranteeing adherence to the specified quality levels and risk criteria. The performance measures such as probability of acceptance and average sample number of the proposed plan are examined and compared with existing plans. This article also includes real-world example to effectively demonstrate the practical implementation of the proposed SkSP-V plan based on the Taguchi process capability index.