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The generation maintenance scheduling deals with a time sequence of preventive maintenance outages for a given set of generation units in an electricity market subject to power system restrictions. Incorporating a leader–follower structure in generation maintenance scheduling models is essential because of the inherent conflict between the interests of an independent system operator (ISO) and generation companies (GENCOs). The present paper proposes a new preventive maintenance scheduling model for generation companies facing the risk of involving generation units' disruption and demand variations while ensuring the reliability of the power system. Each GENCO proposes the maintenance schedule of its generation units to the ISO in a non-cooperative manner intending to maximize its net profit. Then ISO reacts to the aggregated schedule according to the power system's reliability index. Thus, a new formula is developed to consider all the interactions between the power system's stakeholders. In this regard, a stochastic multi-leader one-follower approach is applied. The GENCOs are considered independent leaders at the upper-level and the ISO is considered a follower at the lower-level. Then an equivalent single-level counterpart model is presented for each leader. So, the whole problem is converted into multiple individual stochastic single-level models, and then the Nash Equilibrium concept is used to determine GENCO equilibrium strategies. The proposed methodology is evaluated using some modified IEEE reliability test systems. The numerical analysis confirms that the proposed model is more effective in cases with higher uncertainties. Moreover, the performed analysis demonstrated the importance of applying a bi-level approach to the problem. Finally, the superiority of the proposed approach compared to the existing one is confirmed. [ABSTRACT FROM AUTHOR]

The modern manufacturing organizations are focused on making sustainable products by means of costs reduction and prevention of environmental problems. To achieve this, several tools are available to the organizations such as Quality Function Deployment for Environment (QFDE) which is the integration of voice of customer, voice of environment and quality characteristics. In this paper, an integrated QFDE, fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) and Fuzzy Analytic Network Process (FANP) is proposed for sustainable product design to help companies identify the best design criteria for a specific product. Therefore, the DEMATEL method is used to assume the interdependence of customer attributes and, in addition, fuzzy Maximum Mean de-Entropy (MMDE) algorithm is utilized to choose the best and practical threshold value in DEMATEL process. Moreover, FANP will be integrated into QFDE as a prioritization technique and also fuzzy Logarithmic Least Squares Method (LLSM) is employed to find weights during the FANP process. An integrated QFDE methodology is appropriate to use in early design, since it does not require detailed information about the product. In order to examine the practicality of the proposed model, a case study is carried out in Iran Transfo Corporation which attracts a significant interest due to its undeniable impacts on the environment. [ABSTRACT FROM AUTHOR]

• Two-stage stochastic programming for GMS in deregulated power system considering failure outage status. • Nash equilibrium is obtained as the solution of the non-cooperative game. • Coordination process among ISO and GENCOs with penalty mechanism. • Analysis of strategic behaviors of generation companies in maintenance scheduling. • Sensitivity analyses are applied to evaluate the impact of available capacity on energy price and GMS. This paper presents a two-stage stochastic programming formulation for yearly generation maintenance scheduling when the power generators are unreliable, which means they may be not available to provide service. Each generation company owns several power units and makes its own unit's maintenance decisions to maximize the net profit, considering the failure of the units is probable. At the first stage, maintenance schedule decisions are set. Once the maintenance schedule has been fixed, generation amounts and load allocations are determined in the second stage. The second-stage decisions are made in response to the realized scenario. Since generation companies' decisions are independent, they are considered as a Nash Equilibrium. After the Nash Equilibrium obtained, reliability indexes are acquired by the independent system operator in order to prepare rescheduling signals. The capabilities of the proposed model are assessed and evaluated on a modified IEEE reliability test system. [ABSTRACT FROM AUTHOR]

In constructing any control chart, the sample size and the number of samples taken are important issues. When destructive tests are applied to collect data, the sample size and the number of subsamples are usually limited. Lack of sufficient observations affects parameters estimates. Re‐sampling techniques are always suggested to increase the accuracy of parameters estimates. Also, the presence of outlying observations severely affect re‐sampling and the estimates of the underlying distribution parameters. The limited sample size, limited number of subsamples and presence of outliers are more important in multivariate process analysis. In this study, bootstrap re‐sampling technique and robust estimators of mean vector and variance‐covariance matrix are simultaneously utilized to design a multivariate robust T2 control chart to address the three problems in hand. The performance of the proposed robust T2 control chart is evaluated and compared to the Hotelling T2 control chart by means of average run length (ARL). Simulation results indicate that the suggested robust T2 control chart outperforms the Hotelling T2 in presence of outliers and similarly when there is no outlier. Design of robust control chart for processes generating auto‐correlated data applying bootstrap re‐sampling technique is an area for further research. [ABSTRACT FROM AUTHOR]

Nowadays, the agricultural supply chain as one of the key fields of food production is considered which has recently attracted much attention from researchers. This paper focuses on optimizing a closed-loop citrus supply chain. To this end, a multi-objective mathematical model is formulated that it attempts to minimize total costs, to maximize demands' responsiveness, and to minimize CO2 emissions as environmental damages. To solve the proposed model, five meta-heuristic algorithms include a multi-objective version of the recently published algorithm called tree growth algorithm (MOTGA) and four well-known algorithms called NSGA-II, NRGA, MOKA, and MOSA are utilized. It should be noted that these algorithms are tuned using the Taguchi method to achieve better performance and these are validated using the ε-constraint method in small size examples. It shows that the ε-constraint cannot solve the large size problems and it implies the NP-hardness of the problem. Moreover, the MOTGA is selected as the best approach with the least distance from the ideal point. Finally, to more measurement, a sensitivity analysis is performed and the results confirmed the efficiency of the proposed algorithms. Based on the results, it is shown that multiple transportation vehicles can reduce CO2 emissions and improved all objective functions' values. In addition, considering multiple vehicles improved responsiveness by about 14%. So, considering two new assumptions include CO2 emissions and multiple vehicles in this model, can lead to improve demand responsiveness and emissions reductions. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of reliable and green routing in a road-rail multimodal transportation network. To enhance network reliability, types of disruptions occurring simultaneously in each node and arc are identified. Additionally, carbon emission is considered a risk caused by transportation process disruptions for achieving green routing. A combination of rating methods such as DEMATEL-based Analytic Network Process (DANP) and the Combined Compromise Solution (COCOSO), along with Failure mode and effects analysis (FMEA) Criticality reliability assignment method, is used to evaluate disruptions and select a safe route from origins to destinations. The DANP method produces a ranking of sub-criteria, and the COCOSO method produces a ranking of criteria in the network. The FMEA Criticality reliability assignment method is used to assign reliability to each option. Next, the route with the highest reliability from each origin to each destination is selected. The presented model investigates a cement road-rail transportation network in Iran. After analyzing the sensitivity of criteria and sub-criteria, the results provide best routes with increased reliability and reduced emissions risk. • Identifying risks in each node and arc in the transportation network. • Prioritizing criteria, sub-criteria, and options using a mix of DANP and COCOSO ranking methods. • Assessing the reliability of each option using the FMEA Criticality method. • Selecting the best path with the highest reliability and the less carbon emission risk in each O-D pair. • Considering a case study in the cement road-rail transportation network in Iran. [ABSTRACT FROM AUTHOR]

In many statistical process control (SPC) applications, the quality of a process or product is represented by a relationship or the so-called quality profile, between a quality characteristic and one or more explanatory variables. The process quality characteristics are sometimes measured with a reasonable degree of approximation. Often, especially when qualitative assessments arise, evaluation of quality characteristics is carried out ambiguously or using linguistic qualifiers. The fuzzy sets theory has proved to be a well-established approach for dealing with uncertainty due to the approximate measurement, ambiguity in subjective evaluations, or vagueness in linguistic variables. Our main purpose was to present and compare four methods of monitoring nonlinear fuzzy profiles, for which different nonlinear fuzzy regression modeling approaches are considered. The first two methods are "a data-driven fuzzy rule-based" and "an extended least square support vector machine (LS-SVM)," for which the profile is characterized without considering a predefined mathematical relationship. However, for the other two methods, a specific form of the profile was needed. The third method, namely "a modified fuzzy regression model," was initially invented for linear models. Besides, the fourth method employs "the fuzzy least square method" based on linearizing transformation. The exponentially weighted moving average (EWMA) control statistic was used to derive the control statistic to be plotted on the univariate as well as multivariate control charts. An extensive simulation study was conducted to compare the performance of the methods, and the average run length (ARL) criterion was considered to assess the detect-ability of control charts against various out-of-control conditions. Our comparison results indicated that the multivariate EWMA (MEWMA) chart based on the LS-SVM method outperforms the rest in detecting process shifts with smaller values of ARL when the process undergoes out-of-control conditions. [ABSTRACT FROM AUTHOR]

The complexity of a competitive business environment and increased customer expectations has revealed the necessity of awareness from organizational strengths and weakness and continuous improvement of productivity. Thus, today's managers seek a comprehensive, reliable and flexible solution to evaluate their organizations' performances so that they will obtain accurate and sufficient information of their positions while ensuring the implementation of their own strategies, and will improve their organizations with a future-oriented view. This performance management research, specifically measures the customer relationship management (CRM) in business to business (B2B) area using balanced scorecard (BSC) for determination of CRM indicators and fuzzy analytic network process (FANP) to determine CRM indicators and sub-indicators in a case study on MAPNA organization in order to provide for a ground for CRM efficiency improvement in this organization. [ABSTRACT FROM AUTHOR]

In spite of the classic approaches of solution of dynamic facility layout problem, which only material handling and rearrangement costs are considered as objective function, these problems are the multi-objective problems. In this paper, a mixed integer linear programming formulation is presented for multi-objective dynamic facility layout problem concerning flexible bay structure. In addition, three current objectives in dynamic facility layout problems including minimizing material handling and rearrangement costs, maximizing adjacency rate, and minimizing shape ratio difference have been considered. Also, for solving this problem, two methods including the GAMS software and proposed parallel variable neighborhood search (PVNS) algorithm are used. So, it is worth mentioning that four test problems are solved by them, and the results show that the proposed PVNS algorithm is more efficient than the GAMS software. [ABSTRACT FROM AUTHOR]