Joint decision-making model of preventive maintenance and delayed monitoring SPC based on imperialist competitive algorithm

2021 ◽  
pp. 1-14
Author(s):  
Yan Zhang ◽  
Shiyu Li ◽  
Yang Deng ◽  
Honggen Chen ◽  
Xin Yan ◽  
...  
Keyword(s):  
Decision Making ◽  
Preventive Maintenance ◽  
Early Period ◽  
Imperialist Competitive Algorithm ◽  
Sampling Interval ◽  
Model Parameters ◽  
Statistical Process ◽  
Joint Decision ◽  
Decision Making Model ◽  
And Control

This paper develops a joint decision-making model approach to preventive maintenance and SPC (statistical process control) with delayed monitoring considered. The proposal of delayed monitoring policy postpones the sampling process till a scheduled time and contributes to six renewal scenarios of the production process, where maintenance actions are triggered by scheduled duration of prenentive maintenance or the alert of X ¯ chart for monitoring the shift of process mean resulted by deterioration of equipment. By analyzing the evolution of the system in different scenarios, a mathematical model is given to minimize the expected cost per unit time by optimizing values of five variables (scheduled duration without monitoring, scheduled duration of preventive maintenance, sample size, sampling interval and control limit). The results of a numerical example indicate that the hourly cost of the proposed model is lower than the model that delayed monitoring is not considered when the system has a low hazard rate during the early period. Finally, a sensitivity analysis is performed to demonstrate the effect of model parameters.

scholarly journals Multiobjective Joint Optimization of Production Scheduling and Maintenance Planning in the Flexible Job-Shop Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jianfei Ye ◽  
Huimin Ma
Keyword(s):  
Decision Making ◽  
Optimization Model ◽  
Preventive Maintenance ◽  
Production Scheduling ◽  
Job Shop ◽  
Joint Optimization ◽  
Planning Problem ◽  
Flexible Job Shop ◽  
Maintenance Planning ◽  
Joint Decision

In order to solve the joint optimization of production scheduling and maintenance planning problem in the flexible job-shop, a multiobjective joint optimization model considering the maximum completion time and maintenance costs per unit time is established based on the concept of flexible job-shop and preventive maintenance. A weighted sum method is adopted to eliminate the index dimension. In addition, a double-coded genetic algorithm is designed according to the problem characteristics. The best result under the circumstances of joint decision-making is obtained through multiple simulation experiments, which proves the validity of the algorithm. We can prove the superiority of joint optimization model by comparing the result of joint decision-making project with the result of independent decision-making project under fixed preventive maintenance period. This study will enrich and expand the theoretical framework and analytical methods of this problem; it provides a scientific decision analysis method for enterprise to make production plan and maintenance plan.

Study on the Cooperation Decision-Making Model in Integrated Green Supply Chain for Industrial Wastes Reusing Considerations

2011 ◽  
Vol 361-363 ◽  
pp. 1397-1401 ◽  
Author(s):  
Hai Yan Li ◽  
Bin Dan ◽  
Kai Rao ◽  
Hong Zhao
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Main Product ◽  
Supply And Demand ◽  
Industrial Wastes ◽  
Green Supply Chain ◽  
Joint Decision ◽  
Cooperation Scope ◽  
Product Output ◽  
Decision Making Model

Difference from traditional green single supply chain, this paper discusses a multiple supply chains cooperation model with the consideration of production wastes reusing, integrated green supply chain. We establish the uncooperative dynamic game model and the joint decision-making model. The different waste supply and demand quantificational conditions and the output and price strategy of product under the above condition are given. Especially, through comparing the joint decision-making with the independent decision-making, the following are suggested: When waste supply is far less than demand, the cooperation scope of system profit and main product output do not exist; When waste supply is far greater than demand, the cooperation scope of main product output of upstream supply chain is the maximum, and the cooperation scope of system profit is much bigger; When waste supply is roughly matchable to demand, the cooperation scope of system profit and product output of upstream supply chain are the largest.

scholarly journals Joint Optimization of Preventive Maintenance, Spare Parts Inventory and Transportation Options for Systems of Geographically Distributed Assets

Machines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 55 ◽  
Author(s):  
Keren Wang ◽  
Dragan Djurdjanovic
Keyword(s):  
Decision Making ◽  
Preventive Maintenance ◽  
Spare Part ◽  
Spare Parts ◽  
Joint Optimization ◽  
Joint Decision ◽  
Geographically Dispersed ◽  
Simulation Based ◽  
The Right ◽  
Maintenance Schedules

Maintenance scheduling for geographically dispersed assets intricately and closely depends on the availability of maintenance resources. The need to have the right spare parts at the right place and at the right time inevitably calls for joint optimization of maintenance schedules and logistics of maintenance resources. The joint decision-making problem becomes particularly challenging if one considers multiple options for preventive maintenance operations and multiple delivery methods for the necessary spare parts. In this paper, we propose an integrated decision-making policy that jointly considers scheduling of preventive maintenance for geographically dispersed multi-part assets, managing inventories for spare parts being stocked in maintenance facilities, and choosing the proper delivery options for the spare part inventory flows. A discrete-event, simulation-based meta-heuristic was used to optimize the expected operating costs, which reward the availability of assets and penalizes the consumption of maintenance/logistic resources. The benefits of joint decision-making and the incorporation of multiple options for maintenance and logistic operations into the decision-making framework are illustrated through a series of simulations. Additionally, sensitivity studies were conducted through a design-of-experiment (DOE)-based analysis of simulation results. In summary, considerations of concurrent optimization of maintenance schedules and spare part logistic operations in an environment in which multiple maintenance and transpiration options are available are a major contribution of this paper. This large optimization problem was solved through a novel simulation-based meta-heuristic optimization, and the benefits of such a joint optimization are studied via a unique and novel DOE-based sensitivity analysis.

scholarly journals Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yukun Wang ◽  
Xuebo Chen
Keyword(s):  
Decision Making ◽  
Liver Injury ◽  
Drug Development ◽  
Early Stage ◽  
Drug Induced ◽  
Joint Decision ◽  
Drug Induced Liver Injury ◽  
Majority Principle ◽  
Consensus Models ◽  
Decision Making Model

Drug-induced liver injury (DILI) is the major cause of clinical trial failure and postmarketing withdrawals of approved drugs. It is very expensive and time-consuming to evaluate hepatotoxicity using animal or cell-based experiments in the early stage of drug development. In this study, an in silico model based on the joint decision-making strategy was developed for DILI assessment using a relatively large dataset of 2608 compounds. Five consensus models were developed with PaDEL descriptors and PubChem, Substructure, Estate, and Klekota–Roth fingerprints, respectively. Submodels for each consensus model were obtained through joint optimization. The parameters and features of each submodel were optimized jointly based on the hybrid quantum particle swarm optimization (HQPSO) algorithm. The application domain (AD) based on the frequency-weighted and distance (FWD)-based method and Tanimoto similarity index showed the wide AD of the qualified consensus models. A joint decision-making model was integrated by the qualified consensus models, and the overwhelming majority principle was used to improve the performance of consensus models. The application scope narrowing caused by the overwhelming majority principle was successfully solved by joint decision-making. The proposed model successfully predicted 99.2% of the compounds in the test set, with an accuracy of 80.0%, a sensitivity of 83.9, and a specificity of 73.3%. For an external validation set containing 390 compounds collected from DILIrank, 98.2% of the compounds were successfully predicted with an accuracy of 79.9%, a sensitivity of 97.1%, and a specificity of 66.0%. Furthermore, 25 privileged substructures responsible for DILI were identified from Substructure, PubChem, and Klekota–Roth fingerprints. These privileged substructures can be regarded as structural alerts in hepatotoxicity evaluation. Compared with the main published studies, our method exhibits certain advantage in data size, transparency, and standardization of the modeling process and accuracy and credibility of prediction results. It is a promising tool for virtual screening in the early stage of drug development.

scholarly journals Integration of manufacturing and pricing for downward substitution products decision-making

2021 ◽  
Author(s):  
Hua He
Keyword(s):  
Decision Making ◽  
Trade Policy ◽  
Green Technology ◽  
Optimal Order ◽  
Cap And Trade ◽  
Joint Decision ◽  
Single Period ◽  
Order Quantities ◽  
Decision Making Model ◽  
Period Model

AbstractWith a cap-and-trade policy and green technology as inputs, we built a manufacturing ordering and pricing joint decision-making model for two downward substitution products to identify the conditions for optimal order quantities and prices of products under the additive demand case. Considering the case of a single period model, the conditions required for optimal manufacturing quantities and pricing were discussed, and the construction of the model was analyzed; furthermore, a study of the tactical choices between green technology inputs and manufacturing decisions was conducted, and the conditions required for green technology manufacturing input were obtained.

scholarly journals Age of onset of cannabis use and decision making under uncertainty

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5201 ◽  
Author(s):  
Jose Ramón Alameda-Bailén ◽  
Pilar Salguero-Alcañiz ◽  
Ana Merchán-Clavellino ◽  
Susana Paíno-Quesada
Keyword(s):  
Decision Making ◽  
Computational Model ◽  
Loss Aversion ◽  
Early Onset ◽  
Iowa Gambling Task ◽  
Age Of Onset ◽  
Late Onset ◽  
Cannabis Use ◽  
Model Parameters ◽  
And Control

Objective Cannabis, like other substances, negatively affects health, inducing respiratory problems and mental and cognitive alterations. Memory and learning disorders, as well as executive dysfunctions, are also neuropsychological disorders associated to cannabis use. Recent evidence reveals that cannabis use during adolescence may disrupt the normal development of the brain. This study is aimed to analyze possible differences between early-onset and late-onset cannabis consumers. Method We used a task based on a card game with four decks and different programs of gains/losses. A total of 72 subjects (19 women; 53 men) participated in the study; they were selected through a purposive sampling and divided into three groups: early-onset consumers, late-onset consumers, and control (non-consumers). The task used was the “Cartas” program (computerized version based on the Iowa Gambling Task (IGT)), with two versions: direct and inverse. The computational model “Prospect Valence Learning” (PVL) was applied in order to describe the decision according to four characteristics: utility, loss aversion, recency, and consistency. Results The results evidence worst performance in the IGT in the early-onset consumers as compared to late-onset consumers and control. Differences between groups were also found in the PVL computational model parameters, since the process of decision making of the early-onset consumers was more influenced by the magnitude of the gains-losses, and more determined by short-term results without loss aversion. Conclusions Early onset cannabis use may involve decision-making problems, and therefore intervention programs are necessary in order to reduce the prevalence and delay the onset of cannabis use among teenagers.

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