Stochastic rule-based decision support system for reliability redundancy allocation problem

Reliability Redundancy Allocation (RRA) is one of the most important problems facing the managers to improve the systems performance. In the most RRA models, presented in the literature components’ reliability used to be assumed as an exact value in (0,1) interval, while various factors might affect components’ reliability and change it over time. Therefore, components reliability values should be considered as uncertain parameters. In this paper, by developing a discrete - continuous inference system, an optimization - oriented decision support system is proposed considering the components’ reliability as stochastic variables. Proposed DSS uses stochastic if - then rules to infer optimum or near optimum values for the decision variables as well as the objective function. Finally, In order to evaluate the efficiency of the proposed system, several examples are provided. Comparison of the inferred results with the optimal values shows the very good performance of the developed stochastic decision support system

A Stochastic Decision Support System for Economic Order Quantity Problem

Improving decisions efficiency is one of the major concerns of the decision support systems. Specially in the uncertain environment, decision support systems could be implemented efficiently to simplify decision making process. In this paper stochastic economic order quantity (EOQ) problem is investigated in which decision variables and objective function are uncertain in nature and optimum probability distribution functions of them are calculated through a geometric programming model. Obtained probability distribution functions of the decision variables and the objective function are used as optimum knowledge to design a new probabilistic rule base (PRB) as a decision support system for EOQ model. The developed PRB is a new type of the stochastic rule bases that can be used to infer optimum or near optimum values of the decision variables and the objective function of the EOQ model without solving the geometric programming problem directly. Comparison between the results of the developed PRB and the optimum solutions which is provided in the numerical example illustrates the efficiency of the developed PRB

Developing a closed-loop green supply chain network design in uncertain space

In this research, a closed-loop green supply chain network is designed under uncertain conditions. In the proposed model, four objective functions including minimizing network costs, minimizing greenhouse gas emissions, minimizing production-technical risk, and minimizing the time of sending products to customers are considered simultaneously. Using the proposed network, it is possible to manage the flow of raw materials, first-hand products, and return products between facilities, production planning for each production center, how to allocate products to each facility, determine the number of manpower required for employment and training in each production center, how to allocate machinery and equipment, as well as time management by determining the minimum acceptable time to send products to customers so that the network has the least cost, the least amount of greenhouse gas emissions from the operational processes of facilities and transportation and has made strategic decisions with the least production-technical risk and the least time possible. In this research, to increase the efficiency of the model, parameters such as the amount of return product, recycling rate, and destruction are considered indefinitely and fuzzy logic is used to eliminate the uncertainty. Finally, due to the breadth of the model, the model is validated using a genetic algorithm. The result of the validation indicates the efficiency of the proposed model in optimizing the closed-loop green supply chain network

Petroleum Products Greenness Degree Evaluation Using Fuzzy Hierarchy Inference System

Refineries, including the conversion industries, use oil and gas as row materials and their production processes and products could be so harmful to the environment if they could not manage properly. Nowadays, these companies are working hard to minimize the environmental damages caused by their products, production processes, supply chain, distribution methods and etc. The first step to manage environmental impacts properly, is considering the current condition and correct evaluation of products. In this paper a new method is presented to evaluate the refinery products degree of greenness. The developed model consists of influencing parameters on greenness degree, which are classified in a hierarchical structure. Due to the difficulties in defining an explicit function, fuzzy controllers are implemented to infer degree of greenness based on influencing parameters values. Effectiveness of the presented model is evaluated by assessing the greenness degree of Bandar Abbas refinery products and the results reflect good performance of the developed model

Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer

Abstract Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan–Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein–protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC