Expected Value Method Research Articles

Preliminary study of natural dyes application on batik

Batik is an Indonesia cultural heritage. In general, batik dyeing uses synthetic dyes, which can cause problems in health and the environment. Therefore, the exploration of natural dyes is needed to apply to batik. This study aimed to determine the quality of batik used natural dyes based on the types of plants and the types of leaves. Natural dyes were extracted using a heating method with a water solvent. Natural dye sources took from glodogan tiang (Polyathea longifolia), teak (Tectona grandis L. F), angsana (Pterocarpus indicus), and mango (Mangifera indica) leaves. The types of leaves were fresh and fallen leaves. The natural dyes applied as a dye of batik with tunjung fixation material. Test on the result of batik dyeing used the color difference test (L* a* b) and consumer preference test with parameters of color intensity, batik neatness, and clarity on batik motif. Determining the importance level of the batik quality parameters used the Analytical Hierarchy Process method. Then the selection of the best treatment alternative was made using the Expectation Value method. The results of the study shown that the best treatment was batik with natural dyes from fallen leaves of teak. That caused dye of teak fallen leaves can bound more tightly than other leaves.

Risk analysis by integrated fuzzy expected value method and fuzzy failure mode and effect analysis for an elevated metro rail project of Ahmedabad, India

Risk identification and analysis for complex mega infrastructure projects has become an integral part of the present day project management process. This paper aims to develop an integrated Fuzzy Expected Value Method (FEVM) and Fuzzy Failure Mode and Effect Analysis (FFMEA) method for a complex infrastructure project such as the construction of an elevated corridor for metro rail in Ahmedabad, India. The design and methodology adopted in this research work was a primary data survey, where the major risky activities of an elevated metro rail infrastructure project were identified through interactions with experts, questionnaire surveys, and personal experience. The identified risks were analyzed for likelihood, impact, severity, detection, occurrence and risk priority numbers. Further computations of fuzzy risk severity (FRS), fuzzy risk priority score (FRPS), and risk rankings were carried out through the new application of integrated fuzzy EVM and fuzzy FMEA methods. It has been found that land acquisition, feasibility, tendering, segment erection, segment casting, obligatory span, construction planning, launching girder, road widening, piles and piers activities were highly risky and had very high fuzzy risk severity and FRPS values (0.605–0.775). Risk mitigation measures were proposed for all critical activities of the elevated metro project in Ahmedabad.

PROPORSI MOCAF DAN TEPUNG LARUT DENGAN PENAMBAHAN MALTODEKSTRIN PADA PENGOLAHAN COOKIES

Cookies is snacks made from wheat flour. Wheat flour contains gluten which cannot be consumed by people with celiac diseases and autism. MOCAF is a modification of cassava flour that changes functional properties and can be used to substitute wheat flour. The use of MOCAF produces cookies that are hard-textured, so it is necessary to add arrowroot flour. Arrowroot flour was chosen because it contains low calories and non-gluten. Maltodextrin was used as maker crispy and binder ingredients. The research aimed were to determine the effect of MOCAF and arrowroot flour proportion and maltodextrin addition to the cookies characteristics, and determine the best alternative cookies processing. This research used factorial randomized block design with two factors. First factor was MOCAF and arrowroot flour proportion (T) with three levels (T1=100%:0%, T2=95%:5%, T3=90%:10%) and second factor was maltodextrin concentration (M) with three levels (M1=2%, M2=4%, M3=6%). Parameters research were proximate analysis, calories, yield, ability to swell, and organoleptic properties. The alternative selection used Expectation Value method. The results showed that there were no interactions between treatments on all research parameters, T treatment was significantly different on all organoleptic parameters, M treatment was significantly different on yield, fat and carbohydrate content, organoleptic properties, and the chosen treatment was T2M3 with expectation value was 8.21.
 Keywords: arrowroot flour, maltodextrin, MOCAF, non-gluten cookies

A Fuzzy Multi-Objective Routing Model for Managing Hazardous Materials Door-to-Door Transportation in the Road-Rail Multimodal Network With Uncertain Demand and Improved Service Level

A routing optimization with risk control and reduction provides a quantitative support for an efficient organization of the hazardous materials transportation where road-rail multimodal transportation plays an important role. Oriented on the viewpoint of the multimodal operator, this study investigates a hazardous materials routing problem for their door-to-door transportation through a road-rail multimodal hub-and-spoke network. It is difficult to determine the exact demand of hazardous materials for each transportation order during the advanced routing decision making. Therefore, demand uncertainty is introduced into the problem, in which the uncertain demands are modeled by triangular fuzzy numbers. To enhance the quality of service of the transportation, the hazardous materials routing optimization in this study uses fuzzy soft time windows to optimize the last-mile delivery of hazardous materials that determines the timeliness of transportation. Accordingly, this study proposes a fuzzy multi-objective routing model that is linear and considers the constraints of the customers' preferred service levels. To solve the proposed model, its crisp reformulation is first achieved by using the fuzzy expected value method and Jimenez's fuzzy ranking method. Then, the ε-constraint method is adopted by this study to obtain the Pareto frontier for the multi-objective optimization problem. Finally, an experimental case study is carried out to verify the proposed modeling of this study. By using sensitivity analysis and fuzzy simulation, the conflicting relationship among the economic, risk, reliability and timeliness objectives that improving one objective worsens the others is clearly indicated, and the insights that helps decision makers to make effective tradeoffs among these objectives to get a most suitable hazardous materials road-rail multimodal transportation plan are also summarized.

Proposing a new model to aggregate ratings in multi-source feedback approach based on the evidence theory

Researchers and practitioners in multi-source feedback (MSF) context generally use the average-based methods to aggregate ratings. Because of the uncertainties in the raters’ opinions, it is believed that the use of conventional averaging methods is not appropriate for aggregating MSF data. So, in MSF approach, there is a need to design a proper aggregation method that is capable to cope with the uncertainty in ratings. In this regard, in this paper, each rating group has been considered as a source of evidence, and a new aggregation model based on evidence theory has been proposed. In the proposed model, the collected data from each rating group by designing three different methods have been converted to the basic belief assignments and then aggregated using the Dempster rule of combination. In order to resolve the conflict between evidences, the discounting and compromise methods were used, and the output of the combination process was extracted using three different methods including the pignistic probability criterion, the plausibility transformation method and the expected value method. Finally, through a simulation study, the performance of the proposed model under various configurations was investigated. The results of the simulation study show that the proposed model, in almost all configurations, provides more accurate results than traditional aggregation method in MSF approach.

Stochastic structured tensors to stochastic complementarity problems

This paper is concerned with the stochastic structured tensors to stochastic complementarity problems. The definitions and properties of stochastic structured tensors, such as the stochastic strong P-tensors, stochastic P-tensors, stochastic $$P_{0}$$-tensors, stochastic strictly semi-positive tensors and stochastic S-tensors are given. It is shown that the expected residual minimization formulation (ERM) of the stochastic structured tensor complementarity problem has a nonempty and bounded solution set. Interestingly, we partially answer the open questions proposed by Che et al. (Optim Lett 13:261–279, 2019). We also consider the expected value method of stochastic structured tensor complementarity problem with finitely many elements probability space. Finally, based on the expected residual minimization formulation (ERM) of the stochastic structured tensor complementarity problem, a projected gradient method is proposed for solving the stochastic structured tensor complementarity problem and the related numerical results are also given to show the efficiency of the proposed method.

Comparative Study of Risk Indices for Infrastructure Transportation Project Using Different Methods

This paper is an attempt to develop and compare the risk indices developed by various methods of project risk analysis like modified expected value method, fuzzy expected value method and fuzzy analytic hierarchy process for an elevated metro rail corridor construction project in Bangalore, India. Risk management is increasingly a critical success factor for major infrastructure projects. It helps to identify, analyse, mitigate and control the risks associated with project cost, schedule and scope. Infrastructure projects are usually faced by different types of risks associated with different types of activities which finally lead to cost and time overrun. The main purpose of this work was to identify the risks and uncertainties associated with major activities of an infrastructure project like elevated metro rail corridor project and then analyse for risk severity and risk index through three different methods. It has been observed that fuzzy expected value method is more sensitive than the other two methods, and the computed values of risk indices predict risk severities which can be ranked according to criticality for almost all the identified activities. Thus, project authorities can easily take necessary mitigation measures to reduce the risk severities. Also, fuzzy expected value method gives good results for both small and large number of activities, whilst modified expected value method works well for up to 25 activities and fuzzy analytic hierarchy process works well for up to 20 activities.

A new DEMATEL method based on interval type-2 fuzzy sets for developing causal relationship of knowledge management criteria

The fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL) has been used to solve various multi-criteria group decision-making problems where triangular type-1 fuzzy sets are utilized in defining decision makers’ linguistic evaluation. Most of the fuzzy DEMATEL modifications are built from linguistic variables based on type-1 fuzzy sets (T1FS). Previous literature suggests that interval type-2 fuzzy sets (IT2FS) can offer an alternative that can handle vagueness and uncertainty. This paper proposes a modification fuzzy DEMATEL characterized by IT2FS for linguistic variables. Differently from the typical fuzzy DEMATEL which directly utilizes triangular type-1 fuzzy numbers, this modification introduces trapezoidal IT2 fuzzy numbers to enhance evaluation in the group decision-making environment. This new modification includes linguistic variables expressed by IT2FS and an expected value method for normalizing upper and lower memberships of IT2FS to crisp numbers. The proposed modification is applied to a case of knowledge management (KM) where eleven criteria are considered. Three experts in KM were invited to provide linguistic judgments with respect to the criteria, and the eight-step computational procedure of the proposed modification was implemented without losing the originality of the DEMATEL method. The results unveiled that ‘trust’ is the most influential criteria in KM. Therefore, trust is a phenomenon that impacts on the success of KM. Comparable results are also presented to check the feasibility of the proposed method. It is shown that the criteria weight and the causal relationship of criteria using the proposed method are consistent with the other two methods.

Prenatal Screening Markers for Down Syndrome: Sensitivity, Specificity, Positive and Negative Expected Value Method.

SummaryBackgroundGenetic screening for chromosomopathy is performed in the first trimester of pregnancy by determining fetal nuchal translucency (NT), and the pregnancy associated plasma protein-A (PAPP-A) and free human chorionic gonadotropin (free-beta HCG) biomarkers in maternal serum.MethodsWe tested the sensitivity, specificity, positive and negative expected values of each marker with the aim of setting a model for prenatal screening readings. Statistical data treatment has been performed on a sample of 340 pregnant women with positive results of prenatal screening.ResultsSensitivity of PAPP-A was 0.6250 (probability 62.50%), free beta HCG 0.5893 (58.93%), NT 0.1785 (17.85%). Specificity of PAPP-A was 0.5106 (probability 51.06%), free beta HCG 0.5246 (52.46%), NT 0.9718 (97.18%). Positive expected value of PAPP-A was 0.2011 (probability 20.11%), free beta HCG 0.1964 (19.64%), NT 0.556 (55.56%). Negative expected value of PAPP-A was 0.8735 (probability 87.35%), free beta HCG 0.8662 (86.62%), NT 0.8571 (85.71%). The NT marker has a significantly higher specificity, which means that its normal value will significantly reduce the final risk of trisomy 21. The sensitivity of NT is much lower than that of biochemical markers, which means that a pathological value of NT does not have a significant influence on the final risk, i.e. the significantly higher sensitivity of biochemical markers will reduce the final risk of trisomy 21.ConclusionsThe analyses stress the importance of using a software which has the possibility to separate the level of a biochemical risk by correlating PAPP-A and free beta HCG and, by adding the NT marker, calculate the level of a final risk of Down syndrome.

A Lagrangian Relaxation for a Fuzzy Random EPQ Problem with Shortages and Redundancy Allocation: Two Tuned Meta-heuristics

This paper develops an economic production quantity model for a multi-product multi-objective inventory control problem with fuzzy-stochastic demand and backorders. In this model, the annual demand is represented by trapezoidal fuzzy random numbers. The centroid defuzzification and the expected value methods are applied to defuzzify and make decisions in a random environment. In the case where the warehouse space is limited, the Lagrangian relaxation procedure is first employed to determine the optimal order and the maximum backorder quantities of the products such that the total inventory cost is minimized. The optimal solution obtained by the proposed approach is compared with that obtained by the traditional deterministic method. Moreover, a sensitivity analysis presents the rationality of the solution. Then, the model is extended to a multi-objective integer programming problem in which the optimal numbers of redundant production machines are determined to maximize the production system reliability. In the second proposed model, several constraints are considered to fit real-world situations. As the second model is developed for an NP-hard problem and hence cannot be solved using exact methods in a reasonable computational time, a multi-objective evolutionary algorithm called non-dominated sorting genetic algorithm-II (NSGA-II) is employed to provide Pareto front solutions. Due to non-availability of benchmark in the literature, another multi-objective evolutionary algorithm called non-dominated ranking genetic algorithm is implemented as well to validate the obtained results and evaluate the performance of NSGA-II. In addition, the Taguchi method is used to calibrate the parameters of both algorithms for better performance.

Evaluating the Multi-period Systems Efficiency in the Presence of Fuzzy Data

In many real world applications, the efficiency of production systems should be determined in multiple periods of time while inputs and outputs are imprecise and fuzzy. However, the relative efficiencies of decision making units (DMUs) in the traditional data envelopment analysis (DEA) models are usually measured in a particular period of time and in the presence of precise inputs and outputs. Therefore, the current paper proposes a method to measuring the overall and period efficiencies of DMUs under uncertainty. Actually, a fuzzy DEA approach, which has been based on a fuzzy expected value method, is suggested to calculate the performance of DMUs with fuzzy input/output factors in several periods of time. Furthermore, efficiency changes of DMUs are handled in fuzzy multi-period systems by global Malmquist productivity index (MPI) with common-weights. The proposed approach is illustrated and clarified with two numerical examples. The introduced method can be utilized in the presence symmetrical and asymmetrical fuzzy numbers.

Der Wald-Wild-Konflikt – was kann die Forstökonomie zur Lösung beitragen? (Essay)

The forest-game conflict – how can forest economics contribute to solve it? (Essay) Core parameters of forest economics such as land expectation value or highest revenue show that damage caused by wild ungulates can critically influence the economic success of forest enterprises. When assessing and evaluating the damage in order to calculate damage compensation, methods are applied in Germany that look either into the past (“cost value methods”) or into the future (“expected value methods”). The manifold uncertainties related to this evaluation over long-term production periods are taken into account within a framework of conventions through strongly simplifying assumptions. Only lately, the increased production risk due to game-induced loss of species diversity is also considered. Additional aspects that should be taken into account in the future are the loss of climate-adapted species, the change of the insurance values of forest ecosystems and the impossibility of specific management systems such as single-tree selection forestry due to the influence of game. Because of high transaction costs when assessing the damage, financial compensation should only be the “ultimate measure” and a meditation between stakeholder groups with the goal to find a cooperative solution before the damage occurs should be preferred.

Multi-item imperfect production inventory model in Bi-fuzzy environments

In this paper, we propose a mathematical model for a single period multi-product production inventory model producing stochastically imperfect items with continuous stochastic demand under budget and limited shortage constraints in Bi-fuzzy environment. The stochastic constraints are first converted into corresponding crisp values using expected value method. Here, we have considered the model as single period’s inventory for each item and the cycle lengths for different items are constant but different. Total demand for a cycle and the rate of production of defective units is considered as stochastic. The model is formulated and the expected average profits for each product are calculated from density function of demand and percentage of imperfectness in general form and then particular expressions are obtained by using appropriate boundary conditions. Here, all the constraints are Bi-fuzzy in nature and represented by possibility constraints. The deterministic problem is then solved by using generalized reduced gradient method. The model is illustrated through numerical examples. Sensitivity analysis on profit functions due to different aspiration and confidence level is presented via graphically.

An approach to group decision making with hesitant information and its application in credit risk evaluation of enterprises

Hesitant multiplicative preference relation (HMPR) contains much more comprehensive information than the traditional multiplicative preference relations. The HMPR is a useful tool to help the decision makers express their preferences in group decision making under uncertainty. The key of group decision making with the HMPR is to derive the priority weights from the HMPR. Thus, an efficient and practical priority method should be put forward so as to ensure the reasonability of the final decision result. In order to do that, in this paper, we first introduce the expected value and the geometric average value of hesitant multiplicative element (HME) which is the component of the HMPR. Then from different perspectives, we utilize the error-analysis technique to put forward three novel methods for the priorities of the HMPR, i.e., the expectation value method, the geometric average value method, and the multiplicative deviation method. We also investigate the relationships among these methods, and develop an approach to group decision making with the HMPR by using the methods and the possibility degree formula. Finally, by constructing the indicator system for credit risk evaluation of supply chain enterprises, we make a detailed case study concerning Lu-Zhou-Lao-Jiao (the well-known liquor enterprise in China) to demonstrate our approach.

Risk Decision Analysis of Logistics Purchasing Based on Mathematical Expectation

The mathematical expectation value method is a commonly used method in risk decision-making. This paper studies the problem of how to arrange the purchase plan in order to get the maximum expected profit; considering the applicable principles of mathematical expectation, the decision method for getting the optimal decision scheme is given. Finally, we do simulation and stability analysis on an example and obtain the reasonable result. This result shows that mathematical expectation value method is effective in solving the problem of risk decision.

Geometric average of spatial evidence data layers: A GIS-based multi-criteria decision-making approach to mineral prospectivity mapping

Techniques for GIS-based multicriteria decision-making (GIS-MCDM), like mineral prospectivity mapping (MPM), are concerned with combining information from several criteria into a single evaluation model to solve certain problems in the fields of geosciences. In this paper, we introduce the geometric average method for MPM, as a GIS-MCDM approach, and demonstrate its advantage over the expected value MPM method. The comparative analysis shows that the geometric average MPM method yields better prediction of mineral prospectivity and it overcomes the limitation of the expected value method in terms of using spatial evidence values with the same unit.

Implementation of theZ-vector method in the relativistic-coupled-cluster framework to calculate first-order energy derivatives: Application to the SrF molecule

The molecular dipole moment and magnetic hyperfine structure constant demand an accurate wavefunction far from the nucleus and in near nuclear region, respectively. We, therefore, employ the so-called Z-vector method in the domain of relativistic coupled cluster theory to calculate the first order property of molecular systems in their open-shell ground state configuration. The implemented method is applied to calculate molecular dipole moment and parallel component of the magnetic hyperfine structure constant of SrF molecule. The results of our calculation are compared with the experimental and other available theoretically calculated values. We are successful in achieving good accordance with the experimental results. The result of our calculation of molecular dipole moment is in the accuracy of ~? 0.5 %, which is clearly an improvement over the previous calculation based on the expectation value method in the four component coupled cluster framework [V. S. Prasannaa et al, Phys. Rev. A 90, 052507 (2014)] and it is the best calculated value till date. Thus, it can be inferred that the Z vector method can provide an accurate wavefunction in both near and far nuclear region, which is evident from our calculated results.

Application of fuzzy failure mode effect analysis and expected value method for project risk analysis of elevated corridor metro rail projects

This paper is an attempt to develop a methodology of risk analysis by the use of fuzzy failure mode and effect analysis (FFMEA) and expected value method (EVM) of elevated corridor construction for metro rail. Case studies of Ahmedabad-Gandhinagar metro rail and Mumbai metro rail construction in India, at various phases, are presented to validate the concept of this technique. According to EVM analysis, for elevated corridor projects about 81% risks are in the domain of 'critical' and 19% risks are under the domain of 'high' risk severity. According to FFMEA analysis about 85% of the risks fall under 'critical' severity category and rest 15% of the risks fall under 'v. high' and 'high' risk category. Thus quite similar results are obtained from both techniques. The project authorities need to adopt risk mitigation measures to avoid undue time and cost overrun for the project.

Bilevel multi-objective construction site security planning with twofold random phenomenon

The lack of early security arrangements during the construction period can increase the vulnerability of construction project. To support current construction security standards, this paper proposes a bilevel multi-objective model for construction site security problem (CSSP). In contrast to prior studies of CSSP, the bilevel relationship and twofold random phenomenon are considered. Specifically, the upper level programming denotes that project security officer must first decide which facilities to be secured under limited funds whilst maximizing the efficiency of the construction facilities system and minimizing the countermeasure cost and economic loss. The lower level programming denotes that the attacker will destroy a subset of the facilities to inflict maximum loss of efficiency in the construction facilities system. To deal with the uncertainties, expected value method and chance constraint method are introduced to transform the uncertain model into a calculable one. Thereafter, a stochastic simulation based constraint checking procedure is designed. Plant Growth Simulation Algorithm (PGSA) is applied to solve this model. Finally, the approach is carried out in the Longtan hydropower construction project to illustrate the efficiency of the proposed model and algorithm.

Reserve Capacity Optimization of Large Scale Wind Power Integration

The traditional method of power system reserve capacity is not suitable for the power system with large-scale wind power integration. This paper analyzed the prediction error of wind power, the introduction of the system reliability index, established a correlation mode between wind power prediction error and the spare demand change; the introduction of interruptible load to replace part of the reserve capacity, combining probability of the using of spare capacity in historical data, according to the standby capacity demand use of decision theory expected value method is used to solve the optimal reserve. The practical examples prove that the algorithm is feasible.