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The purpose of this study is to provide a model for evaluating, prioritizing and allocating orders to suppliers in the supply chain for mega-projects.

By using an integrated model (based on fuzzy analytic network process), suppliers are selected and the appropriate amounts are allocated to them in mega-projects. Initially, a hierarchical model of the research method was introduced. Then, the results on reliability and validity analysis of research measurement tools were presented. Finally, prioritization and allocation of orders to suppliers, with a case study of Iran Mall project, was carried out using Decision Making Trial and Evaluation Laboratory (DEMATEL) and analytic network process (ANP). T-test was used to evaluate the research hypotheses.

The findings were examined against conventional numerical analysis techniques. Finally, implication and recommendations for future work were presented.

The originality of this work is about using multi-criteria decision-making techniques for evaluating suppliers in mega-projects.

Correct and well-planned maintenance based on modern global methods directly affects efficiency, quality, direct production costs, reliability and profitability. The selection of an optimal policy for maintenance can be a good solution for industrial units. In fact, by managing constraints such as costs, working hours and human workforce causing sudden equipment failure, production and performance can increase.

Therefore, in this research a model was presented to select the best maintenance strategy at Kaghaz Kar Kasra Co of Iran. In this study, it was tried to integrate the two techniques of goal programming and the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritize maintenance strategies. First, all factors affecting maintenance were identified, and based on the Best Worst Method (BWM) the degree of their importance was determined.

After the evaluation, only 14 criteria in the 4 dimensions of cost, added value, safety and feasibility were selected. The highest points were given to the criteria of equipment cost and depreciation, equipment and personnel performance, equipment installation time and technical feasibility, respectively. In the next stage, using the TOPSIS method the item of maintenance strategy was ranked, and the 3 strategies of preventive maintenance (PM), predictive maintenance (PDM) and corrective maintenance (CM) were chosen. Modeling was performed utilizing a goal programming approach to select the optimal maintenance strategy for 13 devices. All the technical specifications, cost limits and the device time were extracted. After the model was finished and solved the best item for each device was specified.

1. Developing a goal programming model and decision-making dashboard. 2. Identifying the criteria and factors affecting the selection of the maintenance strategy for paper production Industry

The overall equipment effectiveness (OEE) is a powerful metric in production as well as one of the methods in evaluating function for measuring productivity in the production process. In the existing method, measuring OEE is based on three main elements consisting availability, performance and quality. The purpose of this paper is to evaluate the recognized metrics of production: OEE and overall line effectiveness (OLE) by using smart systems techniques.

In this paper, to improve the calculative methods and productivity with three methods: measuring OEE using Mamdani fuzzy inference systems (FIS), measuring OEE using Sugeno FIS, and measuring OLE using FIS and artificial neural networks (ANNs) are proposed.

The proposed methodologies aim to decrease some weaknesses of OEE and OLE methods by exploiting intelligent system techniques, such as FIS and ANNs. In particular, this research will solve the following issues that occur in manual and automatic data gathering. This technique is an effective way of measuring OEE and OLE with regard to different weights of losses as well as difference in the weight of the machines. In addition, it allows the operator’s knowledge to take a part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed.

In relation to other methodologies, it allows the operator’s knowledge to take part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed.

The food industry is directly related to the health of humans and society and also that little attention has been paid to the assessment of sustainable supply chain risk management in this area, this will be qualified as an important research area. This study aims to develop a framework for assessing the sustainable supply chain risk management in the realm of the food industry (confectionery and chocolate) with a case study of three generic companies denotes as A1–A3. The proposed risk management was evaluated in three aforementioned manufacturing companies, and these three companies were ranked by the Fuzzy-Weighted Aggregated Sum Product Assessment (F-WASPAS) method in EXCEL.

The evaluation was carried out using integrated multi-criteria decision-making methods Best-Worst method (BWM)-WASPAS. Via an extensive literature review in the area of sustainable supply chain, sustainable food supply chain and risks in this, 9 risk criteria and 59 sub-criteria of risk were identified. Using expert opinion in the food industry, 8 risk criteria and 39 risk sub-criteria were identified for final evaluation. The final weight of the main and sub-criteria was obtained using the F-BWM method via LINGO software. Risk management in the sustainable supply chain has the role of identifying, analyzing and providing solutions to control risks.

The following criteria in each group gained more weight: loss of credibility and brand, dangerous and unhealthy working environment, unproductive use of energy, human error, supplier quality, quality risk, product perishability and security. Among the criteria, the economic risks have the highest weight and among the alternatives, A3 has obtained first ranking.

Modeling of risk for the food supply chain is the unique contribution of this work.

To outsource part of their work, organizations are looking for suppliers who also have green criteria with other criteria. Selecting suppliers begins with the definition of potential suppliers and then selects the best among them. This study aims to present a two-part approach for selecting suppliers consisting of suppliers’ prioritization.

In the first part, the criteria that influence on selecting the suppliers have been identified and extracted using the literature review and experts’ opinion which consists of 19 criteria. Then, these criteria were evaluated by the content validity ratio index and using experts’ opinions, and finally, 16 criteria were selected for selecting green suppliers in a polyethylene’s products producer company in Iran. In the next step, suppliers are selected in a green supply chain using multi-criteria decision-making methods such as Dempster–Shafer theory and grey relationship analysis, which is a strategic decision.

This study attempts to improve the level of reliance on the whole uncertain degree by combining Dempster–Shafer theory and grey relational analysis (GRA), which makes the grey analysis method more robust and its results more reliable. The findings show that Supplier 4 is ranked as first within six suppliers.

Using GRA and Dempster–Shafer theory for green supplier selection problem in polyethylene industry is the novelty of this work.

Shewhart's control charts are the most important statistical process control tools that play a role in inspecting and producing quality control. The purpose of this paper is to investigate the attributes of fuzzy U control chart.

If the data were uncertain, they were converted into trapezoidal fuzzy number and the fuzzy upper and lower control limits were trapezoidal fuzzy number calculated using fuzzy mode approach. The result was grouped into four categories (in control, out of control, rather in control, rather out of control). Finally, a case study was presented and the method coding was done in MATLAB software using design U control chart; then, the results were verified.

The definition of fuzzy numbers for each type of defect sensitivity and the unit can be classified into four groups: in-control and out-of-control, rather in-control and rather out-of-control which represent the actual quality of the products. It can be concluded that fuzzy control chart is more sensitive on recognition out of control patterns.

This paper studies the use of control charts, specifically the attributes of a fuzzy U control chart, for monitoring defects in the format of a case study.

In this context, process capability indices (PCI) reveal the process zones base on specification limits (SLs). Most of the research on control charts assumed certain data. However, to measure quality characteristic, practitioners sometimes face with uncertain and linguistic variables. Fuzzy theory is one of the most applicable tools which academia has employed to deal with uncertainty. The paper aims to discuss these issues.

In this investigation, first, fuzzy and S control chart has been developed and second, the fuzzy formulation of the PCIs such as C _pm ,C _pmu ,C _pml , C _pmk , P _p , P _pl , P _pu , P _pk are constructed when SLs and measurements are at both triangular fuzzy numbers (TFNs) and trapezoidal fuzzy numbers (TrFNs) stages.

The results show that using fuzzy make more flexibility and sense on recognition of out-of-control warnings.

For further research, the PCIs for non-normal data can be conducted based on TFN and TrFN.

The application case is related to a piston company in Konya’s industry area.

In the previous researches, for calculating C _p , C _pk , C _pm and C _pmk indices, the base approach was calculate standard deviation for a short term variation. For calculating these indices, the variation between subgroups are being ignored. Therefore, P _p and P _pk indices solved this fault by mentioning long term and short term variations. Therefore these two indices calculate the actual process capability.