Robust design of a VP-NCS chart for joint monitoring mean and variability in series systems under maintenance policy

Salmasnia, Ali; Namdar, Mohammadreza; Noroozi, Maryam

doi:10.1016/j.cie.2018.06.026

Cited by 26 publications

(13 citation statements)

References 25 publications

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“…Salmasnia et al [49] utilized PSO to optimize the cost function of integrating EPQ, MP, and VP-T 2 chart design subject to statistical constraints. In another study of integrating MP and control chart design for series systems, a robust optimization approach was presented utilizing PSO to minimize model costs under uncertain parameters [58]. Since the proposed model is also of non-linear type in the presence of both discrete and continuous variables, we employ PSO to optimize our proposed model according to the following steps.…”

Section: Solution Approachmentioning

confidence: 99%

An integrated model for optimal selection of quality, maintenance, and production parameters with auto correlated data

et al. 2021

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Statistical process monitoring, maintenance policy, and production have commonly been studied separately in the literature, whereas their integration can lead to more favorable conditions for the entire production system. Among all studies on integrated models, the underlying process is assumed to generate independent data. However, there are practical examples in which this assumption is violated because of the extraction of correlation patterns. Autocorrelation causes numerous false alarms when the process is in the in-control state or makes the traditional control charts react slowly to the detection of an out-of-control state. The autoregressive moving average (ARMA) control chart is selected as an effective tool for monitoring autocorrelated data. Therefore, an integrated model subject to some constraints is proposed to determine the optimal decision variables of the ARMA control chart, economic production quantity, and maintenance policy in the presence of autocorrelated data. Due to the complexity of the model, a particle swarm optimization (PSO) algorithm is applied to search for optimal decision variables. An industrial example and some comparisons are provided for more investigations. Moreover, sensitivity analysis is carried out to study the effects of model parameters on the solution of the economic-statistical design.

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Section: Solution Approachmentioning

confidence: 99%

An integrated model for optimal selection of quality, maintenance, and production parameters with auto correlated data

et al. 2021

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“…In this paper, in order to achieve optimal values of decision variables, Particle Swarm Optimization (PSO) is implemented because of its good performance in optimizing non-linear models, unique searching mechanism, simplicity in concept, computational efficiency, and ease of implementation. It is one of the most popular metaheuristic algorithms that recently has been widely applied by some researchers such as Salmasnia et al [31] and Salmasnia et al [32].…”

Section: Solution Approachmentioning

confidence: 99%

An integrated maintenance planning, warranty policy, technology level and pricing model considering time value of money in a three-level servicing contract

Salmasnia

Hatami

2021

Scientia Iranica

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A common way to address customer concerns in the post-warranty period is to provide an extended warranty. Although sometimes the manufacturer is reluctant to offer an extended warranty, an agent takes on this task to maintain market share. For this purpose, the warranty policy is presented as a three-level servicing contract with the objectives of maximizing the manufacturer's profit, agent's profit, and customer satisfaction. The model considers two approaches to control the number of product failures: (1) technology level used in manufacturing as an effective factor in product reliability; and (2) non-periodic maintenance activities to maintain the product reliability at an acceptable level. To calculate the costs imposed on each side of the contract more accurately, the time value of money is considered in the calculation of financial flows. To illustrate the effectiveness of the approach, three comparative studies are provided. The first one shows the impact of the presence of the agent and the provision of an extended warranty period, while the second one proves the importance of preventive maintenance to reduce costs and increase the interests of each side. The results of the last one shows the effect of considering time value of money in calculating cash flows.

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“…Method / algorithm / framework to robust optimization Production -A novel Robust optimization (RO) approach to deal with the tolerance optimization problem for the internal combustion engine (ICEs) under parameter and model uncertainties [12] -the Gaussian process models [12] -A VP-NCS chart for joint monitoring mean and variability in series systems under maintenance policy [13] -Particle Swarm Optimization (PSO) Algorithm [13] -Automation and robotics in the slaughterhouse [14] -the optimization method RBFopt [14] -Aggregate planning with Flexibility Requirements Profile [15] -Mixed-Integer Linear Programming (MILP) [15] -Lot-sizing and scheduling problem under multistage demand uncertainty [16] -a Monte Carlo simulation [16] -The multi-objective robust optimization of the billet are performed based on 3D finite element simulation [17] -Non dominated sorting genetic algorithm (NSGA-II) [17] -A No Speeds and Coefficients PSO approach to reliability optimization problems [18] -No Speeds and Coefficients Particle Swarm Optimization (NSC-PSO) [18] -Optimizing make-to-stock policies [19] -Mixed-Integer Linear Programming (MILP) [19] -The assembly line worker assignment and balancing problem (ALWABP) [20] -Min-max regret [20] , 0 -The problem of scheduling jobs with interval uncertain due-dates is considered with the objective to minimize the total weight of late jobs, utilizing the min-max regret approach for computing robust solutions [21] -Mixed-Integer Linear Programming (MILP) [21] -min-max regret [21] -An integrated business continuity and disaster recovery planning (IBCDRP) model to build organizational resilience [22] -Multi-Objective Programming [22] -Production planning with order acceptance and demand uncertainty [23] -Mixed-Integer Linear Programming (MILP) [23] -A hybrid approach integrating a particle swarm optimization algorithm with a Cauchy distribution and genetic operators (HPSO+GA) for solving an flexible job-shop scheduling [24] -Particle Swarm Optimization (PSO) Algorithm [24] -Cauchy Distribution and Genetic Operators (HPSO+GA) [24] -The robust machine availability p...…”

Section: Decision Problemmentioning

confidence: 99%

Robust optimization in production engineering – methods and application

et al. 2019

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Methods that use robust optimization are aimed at finding robustness to decision uncertainty. Uncertainty may affect the input parameters (problem) and the final solution. Robust optimization is applicable in many areas, such as: operational research, IT, energy, production engineering and others. The aim of the work was to indicate the main methods and examples of applications of robust optimization in the area of production engineering. Documents (articles and proceedings paper) indexed in the Web of Science - Core Collection database (WoSCC) from 2014-2018 were used for analysis. The search has been limited to the WoS-CC category: Engineering Industrial and Engineering Manufacturing. The main areas of application were: the scheduling of projects and tasks, production planning, and risk management. The most common methods were: linear programming, evolutionary algorithms, mixed integer programming, dynamic programming and many others.

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Robust design of a VP-NCS chart for joint monitoring mean and variability in series systems under maintenance policy

Cited by 26 publications

References 25 publications

An integrated model for optimal selection of quality, maintenance, and production parameters with auto correlated data

An integrated model for optimal selection of quality, maintenance, and production parameters with auto correlated data

An integrated maintenance planning, warranty policy, technology level and pricing model considering time value of money in a three-level servicing contract

Robust optimization in production engineering – methods and application

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