Abstract

     In the present time of innovation, conveyor is an exceptionally indispensable part and has huge significance for material handling in various process and manufacturing industries. It is made with precisions, and the expenses are relatively high and should work with better productivity. The selection of the best conveyor is a crucial task, and the designers need to recognize different attributes that will influence the functionalities of the conveyor system to limit bottlenecks in the system. Researchers have worked on strategies for finding the best optimum solution for various selection problems, but conveyor selection using selected methods is not reported. Therefore, the present work aims to the selection process of the best option for conveyor by using four selected decision-making methods such as analytical hierarchy process, technique of order preference by similarity to ideal solution, compromise ranking method and Deng’s similarity-based method. The selection is done among four alternatives based on six attributes, viz. fixed cost each hour, variable cost each hour, conveyor speed, product width, product weight and flexibility. The analytical hierarchy process is used to determine weights of the attributes based on relative importance of each attribute. It is observed that A3 conveyor is best suitable conveyor. Hence, the proposed strategies help manufacturers to examine and choose the best conveyor.

Introduction

     Nowadays, the material handling conveyors are the backbone of many industries. A conveyor system is a mechanical device that moves items from one location to another. It is utilized in a variety of processing and manufacturing industries. The different conveyor systems have been established for conveying materials or products from one location to another, depending on the speed of handling, nature, amount, size and weight of commodities to be carried. The determination of the best option of conveyor for developing a dependable and efficient conveyor system will save money and increase production and lowering the risk of workers operating it. The appropriate planning of material handling is also required for minimizing the delivery time, and it leads to lower overall expenses of the manufacturing and improves customer service and reduces inventory cost. In India, 80% of material handling is done by conveyors in most of the industries and the worldwide transport framework market size is projected to arrive at USD 10.6 billion by 2025, from an expected worth of USD 8.8 billion in 2021. Thus, various processing and manufacturing plants are improving the performance of conveyor systems. Therefore, best conveyor must be chosen to reduce impact and wear of the conveyor and thereby increase durability and strength.

Conclusions and future work

     The evaluation of available conveyor and selection of best suitable conveyor is very crucial and important decision for any process industry. The several more conveyors with different properties and specifications are also available. The selection of improper conveyor can directly or indirectly affect the productivity and efficiency of any process industry. Thus, it is important to select the best conveyor for better material handling. The present work shows the easy and logical scientific study to guide any decisionmaker for selecting any best alternative. The methodology done in the present work helps decision-maker to take qualitative decision. The four selected decision-making methods, viz. AHP, TOPSIS, VIKOR and Deng’s similarity-based methods, are used for solving the proposed problem. The AHP method is used for obtaining weights of all attributes and is applicable for the remaining methods as well. It is found from the ranks obtained using selected methods that A3 conveyor is the best suitable choice for material handling among the four alternatives. It is observed that ranking of conveyor has some deviation in the rankings due to different mathematical approaches used in the four methods. Finally, it is concluded that these methods are very helpful for making decision in complex problems.

Abstract

     In the last decades, the art and science of multi-attribute decision-making (MADM) have witnessed significant developments and have found applications in many active areas. A lot of research has demonstrated the ability of cognitive techniques in dealing with complex and uncertain decision information. The purpose of representing human cognition in the decision-making process encourages the integration of cognitive psychology and multi-attribute decision-making theory. Due to the emergence of research on cognitively inspired MADM methods, we make a comprehensive overview of published papers in this field and their applications. This paper has been grouped into five parts: we first conduct some statistical analyses of academic papers from two angles: the development trends and the distribution of related publications. To illustrate the basic process of cognitively inspired MADM methods, we present some underlying ideas and the systematic structure of this kind of method. Then, we make a review of cognitively inspired MADM methods from different perspectives. Applications of these methods are further reviewed. Finally, some challenges and future trends are summarized. This paper highlights the benefits of the synergistic approach that is developed based on cognitive techniques and MADM methods and identifies the frontiers in this field.

Introduction

     Real-world decision-making problems are often too complex to be considered through a single criterion. In order to solve problems with multiple attributes, multi-attribute decision-making (MADM) methods are developed, which signifcantly enhance the ability of decision-making methods. As a cognitive process, MADM refers to selecting the optimal solution by evaluating alternatives in the presence of multiple attributes. The number of alternatives for MADM problems is predetermined and limited. Basically, MADM methods can be roughly grouped into two categories [1]: (1) Outranking techniques. The outranking methods are based on pairwise comparison of alternatives, like ELECTRE (Elimination et Choix Traduisant la Realité in French, Elimination and Choice Expressing the Reality) [2] and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) [3]. (2) Multi-attribute utility and value theories. This kind of method usually assigns a utility to each alternative and includes UTA (utility additives) [5], AHP (analytic hierarchy process) [6], ANP (analytic network process) [7], MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) [8], etc. There is a growing body of literature that recognizes the importance of MADM methods in many fields of human life, such as healthcare [9, 10], transportation [11–14], manufacturing [15–18], and business [20, 21].

Conclusions

     The emergence of cognitively inspired MADM methods enriches the theoretical framework in MADM and greatly promotes the development of intelligent decision-making. It presents excellent modeling ability in processing complex information, which makes it of signifcant importance to MADM both from theoretical and practical points of view.

     In this paper, we have gone through the recent contributions about cognitively inspired MADM methods and attempted to provide a comprehensive review in this feld. Firstly, some statistical analyses of published papers regarding cognitively inspired MADM have been unfolded from two aspects: the development trend and the distribution of related publications. We have seen a steady increase in the number of academic papers in this feld. As to this fact, more investors have their sights on the cognitively inspired MADM methods in recent years. After that, the system architecture of cognitively inspired MADM has been illustrated. Then, a comprehensive overview of diferent types of cognitively inspired MADM approaches has been provided, which concludes the contributions of these methods to modern decisionmaking. After analyzing the theoretical knowledge, practical applications have also been summarized, which concludes that cognitively inspired MADM methods make great efects on practical applications. We have ended by discussing some challenges and future trends in this feld.

Abstract

     In order to solve the problem of portfolio optimization, this paper proposes a method that combines multi-objective optimization and multi-attribute decision-making to solve the dual-objective portfolio optimization model with conditional value-at-risk (CVaR) measuring risk and including transaction costs. First, in the multi-objective optimization stage, a multi-population parallel NSGA-II based on sparsity strategy (SMP-NSGA-II) is proposed to obtain multiple Pareto optimal solutions of the model. Second, in the multi-attribute decision-making stage, in order to reflect different investment preferences, the Pareto optimal set obtained is clustered through the fuzzy C-means, and then the grey relational projection method is used to evaluate the solutions belonging to the same cluster to select the optimal compromise solution. Finally, a case study of 9 semiconductor stocks in China’s Shanghai and Shenzhen stock markets is carried out, and the optimal compromise portfolio under different investment preferences is given. At the same time, the proposed algorithm is compared with the other six multi-objective evolutionary algorithms (MOEAs), which verifies that the algorithm in this paper has certain competitiveness.

Introduction

     Nowadays, in the field of securities investment, the indicators used to quantify risk mainly include value-atrisk (VaR) and conditional value-at-risk (CVaR). Among them, VaR is represented by nonlinear, non-convex and nondifferentiable function with multiple local optima, making it difficult to calculate. To solve these problems, Rockafellar et al. [1] introduced the CVaR, which is a coherent risk measure that considers risk as the most serious loss in a given scenario, taking into account a certain degree of confidence. Since CVaR is a convex function, it can effectively solve the optimization problem that uses CVaR as a minimization goal or constraint [2, 3]. At the same time, Yu et al. [4] compared five different risk models and verified through experiments that using CVaR to measure risk is a good choice.

     As the complexity of practical applications continues to increase, scholars have developed various heuristic algorithms to solve portfolio optimization problems. The application of heuristic algorithms in portfolio optimization problems is divided into two categories. The first category simplifies portfolio objectives through the setting of weight coefficients [5–7], and obtains a risk-return curve by continuously changing the risk avoidance parameters of representative investors. This method has a certain degree of subjectivity. The second type uses multi-objective evolutionary algorithm (MOEA) to directly optimize risks and benefits simultaneously [8–11], and can obtain a complete effective frontier in one operation. Obviously, it is more convenient to use MOEAs to solve portfolio optimization problems.

Conclusion

     In order to solve the dual-objective portfolio optimization model with conditional value-at-risk (CVaR) as a measure of risk and including transaction costs, this paper proposes a method combining multi-objective optimization and multi-attribute decision-making. In the multi-objective optimization stage, this paper proposes a multi-population parallel NSGA-II based on sparsity strategy (SMP-NSGAII). In the case studies of 9 stocks in the semiconductor industry, we compared SMP-NSGA-II with the other six MOEAs through two performance evaluation indicators (HV and SP) and running time, then verified the feasibility of the SMP-NSGA-II algorithm. In the multi-attribute decision-making stage, this paper adopts the FCM-GRP hybrid method to give the optimal compromise investment portfolio under different DM preferences.

     There are a couple of drawbacks in the present study. Firstly, the model studied in this paper is relatively simple and does not take into account the many unstable factors of the real securities market; Secondly, the space complexity of the proposed SMP-NSGA-II algorithm is also high.

Abstract

     Cardiovascular disease is a global leading cause of death, and timely monitoring can determine its extent. Clinicians use these diagnostic indicators to make scientific and reasonable decisions. However, when decision-makers (DMs) encounter risks in complex environments, their limited rationality may affect decision behaviors. Therefore, the paper explores a new three-way multi-attribute decision making method based on regret theory (3W-MADM-R), which uses heart disease data to make decisions in fuzzy environments. There are three main steps in developing 3W-MADM-R, i.e., (i) we propose the notion of relative outcome functions and corresponding aggregated regret-based utility functions of each object; (ii) we estimate the conditional probability via an outranked set defined by an outranking relation based on the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II); (iii) we construct three-way decision rules to solve the problems of clustering and ranking of objects in data analysis. In order to demonstrate the usefulness of 3W-MADM-R, we apply it to analyze heart disease data. By comparing with results of other methods, we show the feasibility, stability and superiority of the presented 3W-MADM-R method.

Introduction

     Multi-attribute decision making (MADM) refers to a decision problem in which the importance of different attributes is considered and the decision scheme is ranked or the optimal one is selected. There are many applications of MADM methods in management, engineering design, economics and other fields. For instance, Churchman et al. [1] first used a simple weighting method to solve the problem of “choosing the investment policy of enterprises”. At present, the classic MADM methods, including the TOPSIS method [2], the ELECTRE method [3], the PROMETHEE-II method [4] and others. Meanwhile, many types of uncertainties have been considered in MADM methods, including randomness [5], fuzziness [6] and roughness [7]. According to the behavior characteristics of a DM, stochastic, fuzzy-set, and rough-set-based MADM models have been proposed and investigated [8], [9], [10], [11], [12].

     With the rise of MADM techniques, their applications in the medical field [13] have become more and more widespread. A case in point is that the MADM method can be applied to solve cardiovascular and cerebrovascular diseases that seriously threaten human health. According to a report released by World Health Organization (WHO), 18 million people die from cardiovascular diseases each year, and 85% of which are due to heart disease and stroke. Data from the National Bureau of Statistics (https://data.stats.gov.cn/) show that the proportion of deaths due to heart disease in the total number of deaths is increasing year by year and has reached 23.65% in 2019. Therefore, the prevention and control of heart disease need joint efforts of all individuals. Early monitoring, diagnosis and treatment can effectively reduce the morbidity and mortality of heart disease and improve the life quality of patients. One of the key problems in the field of life sciences is how to quickly and effectively diagnose heart disease. At present, routine detection items for this disease mainly include blood pressure, electrocardiogram, blood routine, blood lipids, blood glucose, hemorheology, and so forth. These tests will help clinicians to determine the location and extent of cardiovascular diseases. However, in complex environments, facing with limited rationality owned by DMs and different forms of biomedical data, there are two issues that need to be addressed. One is how to effectively and reasonably deal with ambiguities in psychological and medical data along with the bounded rationality of DMs, and the other one is how DMs with limited rationality apply these data to diagnose a variety of diseases. In real world, clinical decision making and other aspects remain to be explored.

Conclusions

     In recent years, the researches on MADM problems and 3WD models have become the mainstream. On this basis, we have combined RT with the traditional PROMETHEE-II method to construct a new 3W-MADM-R method to solve the decision making problems with behavioral psychology in real life. The effectiveness and superiority of our proposed method have been verified by an illustrative example and comparative analysis of other methods. In addition, in order to demonstrate the stability and feasibility of the 3W-MADM-R method, parameter analysis has been carried out and another three data sets have been added for further verification. The main contributions of this paper are summarized as follows:

     (1) Based on the net-flow of the traditional PROMETHEE-II method, we have constructed a new outranking relation and objectively given the membership degree of the membership function to calculate the conditional probability, which solves the deficiency of the existing methods [38,39].

Abstract

     In parallel with the development of information and network technology, large amounts of data are being generated by the Internet, and data-driven methodologies are now often being used in decision-making. Recent studies have investigated personalized individual semantics (PIS) in various decision-making contexts to model a fact that words mean different things to different people. However, few studies have investigated PIS in the context of multi-attribute decision-making (MADM). In MADM, in addition to multi-attribute linguistic information, pre-existing classification of the alternatives is always present, which have not been considered in prior research. Most previous studies have simply demonstrated the feasibility of PIS methods with numerical examples using small-scale models, and not with realistic datasets. Therefore, in this study, we propose a data-driven learning model to analyze the PIS of decision makers to support a multi-attribute decision-making model that considers pre-existing classification of the alternatives. Specifically, we first propose a PIS multi-attribute learning function to define a general computation form for comprehensive evaluation of the value of alternatives. Then, considering this pre-existing classification of the alternatives, a PIS learning model is constructed by analyzing the relations between calculated values of alternatives and corresponding class assignments to obtain personalized numerical scales of linguistic terms for a decision maker. Finally, we present a case study based on two datasets and a comparison with other methods to justify the feasibility of the proposed model.

Introduction

     Multi-attribute decision-making (MADM) involves an individual or a group of decision makers selecting an option by evaluating a set of alternatives according to multiple attributes [1,2,3,4]. In realistic MADM problems, decision makers often prefer to use language and linguistic terms to express their preferences for evaluating the objects or alternatives, and such information is conventionally included in multi-attribute linguistic decision matrices to represent their preferences [5].

     This means there is a need for computing with words (CWW) [6,7] when dealing with linguistic preferences in decision making. Obviously, words mean different things to different people [8,9], and type-2 fuzzy sets [8] are commonly used to deal with this issue in CWW. The CWW model of type-2 fuzzy sets is a useful tool to deal with multiple meanings, but this model does not represent specific semantics for individuals. For example, if a family with three members wants to buy a car, perhaps the members of the family all think the car is “good”. However, the numerical decision-making meaning of the word “good” might be accurately represented as 0.9 for one member, and 0.7 for the other two members. This reflects PIS among the three members. Porro et al. [10] proposed the concept of perceptual maps to represent the differences between the decision makers’ semantics of linguistic terms in MADM. Recently, Li et al.

Conclusions

     In this study, considering the fact that words mean different things to different people, we propose a PIS learning method for DD-LMADM based on multi-attribute linguistic information and information on the classification of alternatives. The proposed model starts with a PIS multi-attribute learning function to provide a general computation form for evaluating the comprehensive values of alternatives in LMADM models; based on this function, we propose a data-driven PIS learning model that considers the distinct linguistic term sets and the unknown weights associated with the criteria to calculate the PNS of linguistic terms for decision makers.

     The practical examples with car evaluation datasets and house evaluation datasets illustrate the applicability of the proposed data-driven PIS learning approach. Furthermore, we compare our approach with the existing CWW methods based on a triangular membership function and a 2-tuple linguistic model in terms of the inconsistency between the calculated preference values of alternatives and their class assignment.

Abstract

     For multi-attribute decision-making problems with interval numbers whose attribute weights are completely unknown, it is difficult to compare the value of interval numbers. So this paper proposes a method based on TOPSIS and the weighted parameter to deal with it. Firstly, this paper transforms the interval number matrix into two exact number matrices which reduces sorting complexity. Secondly, a parameter is given when determining the weights with entropy weight in order to reflect all the information of the interval numbers. Then TOPSIS is used to determine the order of each scheme. In addition, the average value of the ranking number is used to reflect the actual situation better. Finally, the analysis of the car purchase case shows the proposed method is feasible and practical. And the comparative analysis with the other method based on practical application dataset demonstrates the proposed method is stable and effective.

Introduction

     Due to the complex objective world and its uncertainty, it is difficult to describe the relevant attributes in decision-making problems with exact numbers. To make the model closer to the facts, interval numbers without clear preference information are often used when processing data, thus causing interval number multi-attribute decision-making problems, such as [1].

     At present, the research on such problems has gained attention from scholars at home and abroad. The research focuses on the following four aspects. The first is to sort the interval numbers according to possibility degree, relative superiority degree. Li Z W [2] used relative superiority degree and defined a new sorting vector to rank interval numbers. Li D Q [3] found the ranking method of possibility degree may obtain contrary results to the meaning of possibility degree. So a revised ranking by Boolean matrix was discussed. But the scientificity of the new method needs to be further verified. Yao N [4] defined an interval number ranking method considering symmetry axis compensation which consider multiple attitudes of decision makers with different risk appetites. Firozja, M [5] proposed a new interval distance of two fuzzy numbers that satisfy on metric properties. The second is to determine the weight by studying the application of entropy weight method [6], [7]. Dong P Y [8] proposed a combined weight method. Yue Z L [9] developed a determining weights method for group decision-making problems which each individual decision information is interval numbers. The third is to sort the schemes with the help of projection, grey relation analysis, etc [8], [10], [11], [12], [13], [14], [15], [16], [17], [18]. The forth is to propose the methods for group decision-making problems [9], [19], [20].

Conclusion and promotion

     It is difficult to compare the value of two intervals. In this paper, a novel method is proposed to solve multi-attribute decision-making problems with interval numbers. Two types of exact numbers which are left end point and length are used to replace an interval number which facilitates the determination of attribute weights. At the same time, when determining weights, a variety of possible rankings are considered, and the information brought by interval numbers is fully utilized to ensure the accuracy of results.

     When extracting exact numbers from interval numbers, other methods can also be applied, that is, interval numbers can be replaced with the right end point and length, or the left end point and the midpoint, or the right end point and the midpoint. And the other steps are the same as the corresponding content in this paper. All of them can reduce the difficulty of the problem, and the optimal solution obtained is the same as the method presented in this paper.

     Finally, the feasibility and effectiveness of the proposed method have been demonstrated by car purchasing. The method can also be used to solve the group decision-making problems with intervals.

1- Introduction

2- Literature review

3- Methodology

4- Results and discussion

5- Conclusion

References

Abstract

Biomass is becoming an increasingly widespread source of energy. Yet land, as one of the most important resources in biomass production, is surprisingly understudied in the literature of biomass supply chain planning. This study proposes a novel framework that combines the literature of bioethanol supply chain design with agricultural land planning to simultaneously address optimal supply chain planning and sustainable land use in a bioethanol supply chain. A bi-objective mixed-integer linear programming (MILP) model is proposed to formulate the optimal design and planning of a bioethanol supply chain network considering competition of food and biomass feedstock over the available croplands. The proposed model is capable of making strategic decisions (i.e. locations and capacities of facilities, sourcing and allocation of biomass feedstocks to biorefineries), along with some tactical decisions (i.e. land planning, inventory and production of both biomass feedstock and bioethanol). The model incorporates the two objectives of minimum cost and maximum suitability of crops with their assigned croplands. A novel integration of the FAO framework, the best-worst multi-criteria decision-making method, PROMETHEE II and GIS is used to determine the suitability of available croplands according to the croplands’ soil and topographical characteristics. The performance of the proposed model is demonstrated through a multi-feedstock bioethanol supply chain in Fars province, Iran. It is concluded that the proposed integrated land planning-network design framework outperforms hierarchical approaches in which network design and land planning problems are solved separately in a sequential manner. Also, the case study shows that conditional on implementing second generation bioethanol production, Fars province has the potential to satisfy three percent of the fuel demand for transportation in the country.

Introduction

With the rapidly increasing energy demand and the destructive environmental implications of fossil fuel consumption, renewable energies (REs) play an important role in protecting the environment for the future. Among all the renewable energy resources, biofuel is very attractive due to its worldwide availability, storage potential, and the efficiency of its conversion technologies [1]. In the recent years, many studies have been conducted on its future contribution to the global energy demand [2,3]. Iran holds the largest natural gas and the fourth largest oil reserves in the world and is within the ten countries with the highest emissions of CO2 [4]. The country’s main primary energy resources include oil (fuel and crude), natural gas, electric power (generated mainly by natural gas, followed by fuel oil, gas/diesel, wind and Hydro), gas/diesel and motor gasoline [4,5]. As reported by Aslani et al. [6], transportation sector is the second most energy-intensive sector, following residential sector, in the country and is currently utilizing gasoline, gas/diesel, natural gas and fuel oil as its main energy resources [4]. Also, as reported by Mousavi et al. [4], transportation has produced the second largest amounts of carbon dioxide, again following residential, on average between 2003 and 2014. Biofuel is one of the few renewable energy resources which can contribute to satisfying the energy demand of the transportation sector [7]. Therefore, biofuel production could potentially play an important role in sustainability of energy consumption and meeting the increasing gasoline demand in the near future in Iran [8,9]. In addition, biofuel production contributes to waste management, specially the extensive agricultural waste produced annually in the country, produces jobs and promotes development specially in rural areas [10]. According to Ghobadian [11], only utilizing the 17.86 million tons of wasted crops in Iran, would be able to produce 4.91 billion liters of bioethanol per year.

A single reference point (SRP) is typically employed in traditional studies on the multi-attribute decision making (MADM) method. However, SRP lacks the advantage of the multiplicity of reference points and is thus unable to adequately describe loss aversion actions. With the goal of determining the loss aversion characteristics of decision makers, this work proposes an MADM method that is based on double reference points (DRPs) to solve the MADM problem using crisp and interval fuzzy numbers. First, this work describes the universality of decision-making problems with DRPs and then evaluates the characteristics of DRPs and their effects on decision making. Second, attitude and utility functions are established on the basis of the requirements of loss aversion. Third, considering the ‘‘one-vote veto’’ characteristic of a ‘‘dissatisfaction’’ attitude, a binary utility value is created by integrating the occurrence probability p, and the utility value u. The fourth, the main characteristics of and the aggregation method for the binary utility value are analyzed and established. Finally, a new decision-making method is applied to resolve the location decision of an agricultural product logistics center. Results indicate that the relationship between attribute and reference point significantly influences decision behavior. Compared with the traditional decision-making method, the proposed decision-making method can effectively identify the feasibility levels of alternatives. The ranking of the proposed decision-making method for feasible alternatives is basically the same as that of the traditional decision-making method. These results can effectively help solve agricultural engineering problems and broaden the coverage of and provide references for MADM research.

Introduction

Multi-attribute decision-making (MADM) problem refers to the ranking and selection of finite alternatives with multiple attributes [1], [2]. Owing to the MADM problems generally occurring in daily production and activities [1]–[5], the academic community has carried out comprehensive analyses. Today, studies on MADM problems come in two forms: those focused on perfect rational decision making and those focused on bounded rational decision making. The former is typically based on expected utility theory and is frequently adopted to solve MADM problems. The recent studies on the theory and method of MADM suggest that the research on MADM based on complete rationality has achieved substantial results [6], [7], [8]. The latter type is based on loss aversion theory. According to this theory, loss, relative to a reference point, produces greater psychological utility than the same amount of gain does [9], [10]. Loss aversion is a common phenomenon that is rooted in people’s decisionmaking actions and occurs in the areas of politics, economics, athletics, and so on [10]–[12]. Tom et al. even published a thesis on the science that proves loss aversion as a basic mechanism of human beings or animals [13].

In recent years, complex network theory with advances in the understanding of the highly interconnected nature of various social, biological, and communication systems has gained much attention[1, 2, 3, 4, 5, 6, 7, 8]. Transferring information, trust, ideas, diseases and influences between any two nodes is the key function of complex networks[9, 10, 11]. The information can spread rapidly to a large number of nodes begin with an influential node. Hence, evaluating the influence of the nodes is a significant issue in complex networks [12], such as in the control of the disease and rumor dynamics[13, 14, 15], research on public opinion[16, 17], and creating new marketing tools [18, 19, 20, 21, 22, 23, 24, 25, 26, 27]. Many measurements of node centrality have been used commonly such as Degree centrality (DC)[28], Betweenness centrality (BC)[28, 29, 30], Closeness centrality (CC)[28] and so on. The DC method is very simple but of little relevance, since the measure does not take into consideration the global structure of the network. BC and CC are global metrics which can better identify influential nodes, but they are difficult to apply in largescale networks due to their computational complexity. Another limitation of CC is the lack of applicability to networks with disconnected components: two nodes that belong to different components but do not have a finite distance between them. Several spectral centrality measures are also available, such as semi-local centrality(SLC)[31], eigenvector centrality (EC)[32], PageRank (PR)[33], and LeaderRank (LR)[34]. In SLC the topo logical connections among the neighbors are neglected, only the number of the nearest and the next nearest neighbors of a node is taken into account[35]. EC can not be applied to asymmetric networks in which some positions are unchosen[32]. PR, as well as LR, only has effect in directed networks, it will degenerate to DC in undirected networks[31].

مقاله سال 2017

In today’s competitive and connected environment, many commercial organisations value effective management of the flow of materials considering the relationships between vendors, manufacturers, distribution centres, customers and other services for success (Thomas & Griffin, 1996). The integration of all facilities which add value for buyers from the procurement of raw materials to the distribution of end products can be broadly defined as Supply Chain Management(SCM) (Thomas & Griffin, 1996; Setak et al., 2012). In SCM, it is crucial to be working with dependable suppliers and planning the inventory via efficient allocation of the resources in the supply chain for a competitive advantage Vonderembse & Tracey (1999). Choosing a supplier can impact on the cost and the quality of products. Evaluation and selection of suppliers is a critical issue in a supply chain. On the other hand, inventory planning is an integrated process handling the inventory across the entire network from suppliers to customers. Miller et al. (2011); Miller & John (2010) pointed out that the supply chains with well managed inventory considers satisfying the demand, preventing stock outs and reducing holding costs – where stock is kept in the store for an undesirable period of time.

In most of the previous work, supplier selection and inventory planning are treated as separate problems. Moreover, many previous studies on supplier selection focus on fuzzy systems formulating the problem as a Multiple Attribute Decision Making (MADM) problem and taking the requirements of decision makers into account as well during the solution process. For example,Chen et al. (2006); Pattnaik (2011); A.Sarkar & Mohapatra (2006) and Gong (2013) studied an MADM approach based on Type-1 Fuzzy Sets and Interval Type-2 Fuzzy Sets (IT2FS), respectively. The supplier selection problem can be formulated as a multi-objective problem and so some of the previous work investigated multi-objective models looking into the trade-off between minimisation of total cost and lead time (Mastrocinque et al., 2013), minimisation of total cost and maximisation of customer service quality (Liao et al., 2011), minimising total .

فرضیه بیشتر روش¬های ﺗﺼﻤﻴﻢﮔﻴﺮﻱ ﺑﺎ ﻣﻌﻴﺎﺭﻫﺎﻱ ﭼﻨﺪﮔﺎﻧﻪ. (MADM) این است که معیارها مستقل از یکدیگر هستند، که در بسیاری از مسائل دنیای واقعی، یک فرض واقع بینانه نیست. اشکال مختلفی از تعامل میان معیارها ممکن در شرایط زندگی واقعی رخ دهد به طوری که روش های پیچیده تر یا هوشمندانه تری برای مقابله با نیازهای خاص مشکل مورد نظر مورد نیاز خواهد بود. متاسفانه، در مقالات به مفهوم تعامل معیارها کمتر پرداخته شده است. این همچنان یک موضوع بسیار مهم و حیاتی تحقیقاتی برای تصمیم گیری هوشمندانه در MADM است. هدف مقاله حاضر برداشتن یک گام به جلو برای پر کردن این خلا با به تصویر کشیدن تصویری کلی است، که ارائه کننده یک طبقه بندی از روش های مربوط به پدیده تعامل معیارها میباشد، و برای اولین بار در مقالات، در مورد پیوند روش ارزیابی و آزمایش تصمیم گیری دیمتل (DEMATEL) و فرایند تحلیل سلسله مراتبی (ANP) بحث میکند. ترکیب دیمتل (DEMATEL) و ANP در سال های اخیر توجه قابل توجهی از سوی جامعه تحلیل تصمیم گیری دریافت کرده است و به عنوان یکی از امیدوار کننده ترین روش های رسدیگی به تعاملات معیارها در یک محیط MADM در نظر گرفته میشود.

عملکرد مالی امری حیاتی برای شرکت‌های فن آوری اطلاعات (IT) جهت باقی ماندن در رقابت شدید جهانی است. با توجه پیچیدگی موجو.د در محیط کسب و کار و سرعت پیشرفت فن آوری‌ها، شرکت‌ها فاقد راهنمای خاصی به منظور درک رابطه ضمنی میان شاخص‌های بسیار مهم مالی برای بهبود چشم اندازها در یک رویکرد زمینه می‌باشند. برای حل و فصل نگرانی‌های مذکور، این مطالعه رویکرد جدیدی را با ترکیب رویکرد مجموعه راف یا ناهموار مبتنی بر تسلط همسانی متغیر (VC-DRSA) همراه با تکنیک آزمایشگاه آزمایش و ارزیابی تصمیم گیری (DEMATEL) برای کشف رابطه پیچیده میان متغیرهای مالی و بهبود عملکردهای آینده ارائه نموده است. علاوه بر این، یک سیستم استنتاج فازی بر اساس یافته‌های VC-DRSA و تکنیک DEMATEL به منظور بررسی دانش گردآمده و پیامدهای ان ابداع شد. گروهی از شرکت‌های واقعی IT که در بازار سهام تایوان ثبت شده بودند به عنوان یک مورد تجربی جهت ارائه مزایای رویکرد جدید مورد استفاده قرار گرفتند. نتایج مجموعه‌ای از قواعد تصمیم گیری را پدید آوردند که می‌توانند برای پیش بینی چشم اندازهای عملکرد آینده و تشخیص اثرگذاری هدایتی متغیرهای مهم برای به دست آوردن بینش‌هایی استفاده گردند؛ برخی از قواعد تصمیم گیری قوی با استفاده از استنتاج فازی به منظور بررسی پیامدهای به دست آمده بیشتر بررسی گشتند. این یافته‌ها به برنامه‌های کاربردی مالی علم تصمیم گیری و هوش محاسباتی در عمل کمک می‌کنند.