A hybrid fuzzy multiple criteria group decision making approach for sustainable project selection

In this paper, a new hybrid fuzzy multiple criteria group decision making (FMCGDM) approach has been proposed for sustainable project selection. First, a comprehensive framework, including economic, social, and environmental effects of an investment, strategic alliance, organizational readiness, and risk of investment has been proposed for sustainable project selection. As the relative importance of the criteria of the proposed framework are hard to find through several conflictive preferences of a group of Decision Makers (DMs) so, a goal programming (GP) has been supplied to this aim considering multiplicative and fuzzy preference relation. Then, a fuzzy TOPSIS method has been developed to assess the fitness of investment chances. It is based on Preference Ratio (PR), which is known as an efficient ranking method for fuzzy numbers, and a fuzzy distance measurement. The properties of proposed hybrid approach make it robust for modeling real case of uncertain group decision making problems. The FMCGDM has been developed through a linkage between Lingo 11.0, MS-Excel 12.0, and Visual Basic 6.0. The proposed hybrid approach has been applied in a real case study called Iranian financial and credit institute for sustainable project selection.     

A fuzzy constraint satisfaction approach for electronic shopping assistance

The Internet and World Wide Web offer an additional channel for consumers to find, select, and buy products. However, unlike shopping in the traditional store, consumers have no direct contact with human clerks to get the required information in the electronic store. The objective of this paper is to propose a fuzzy constraint satisfaction approach to help buyers find fully satisfactory or replacement products in electronic shopping. For the buyer who can give precise product requirements, the proposed approach can generate product-ranking lists based on the satisfaction degrees of each product to the given requirements. For the buyer who may not input accurate requirements, a similarity analysis approach is proposed to assess buyer requirements automatically during his browsing process. The proposed approach could help buyers find the preferred products on the top of the ranking list without further searching the remaining pages. The experimental results show the applicability of the proposed approach for electronic shopping assistance.     

A fuzzy multi-criteria decision making approach for solving a bi-objective personnel assignment problem

Effective personnel assignment is one of the most crucial tasks performed by the decision makers of a company. This paper proposes a systematic approach with a feedback mechanism in which the interdependences among positions and the differences among the selected employees are considered simultaneously. Unfortunately, the two combined considerations have rarely been discussed in the literature. The purpose of this approach is to obtain the best matching of candidates and positions in order to organize a collaboratively cross-functional team. In a fuzzy environment and, then, in the proposed approach, a bi-objective binary integer programming (BOBIP) model is formulated. Based on the weighted composite scores determined in the third step of the proposed procedure, the BOBIP model is transformed into a fuzzy bi-objective goal programming (FBOGP) model. An elaborately designed heuristic algorithm is developed to determine the appropriate values of several important parameters in the FBOGP model, which is solved using LINDO 8.0. An application example is illustrated, and two additional examples are tested. The results indicate that the proposed approach achieves the acceptable satisfaction level and requires less computation time than the brute force enumerative method.     

A fuzzy logic based approach to the SLAM problem using pseudolinear models with multiframe data association

This paper presents an alternative solution to simultaneous localization and mapping (SLAM) problem by applying a fuzzy Kalman filter using pseudolinear process and measurement models. Nonlinear process model and observation model are formulated as pseudo-linear models and rewritten with a composite model whose local models are linear according to Takagi-Sugeno (T-S) fuzzy model. Using the Kalman filter theory, each local T-S model is filtered to find the local estimates. The linear combination of these local estimates gives the global estimate for the complete system. Data association to correspond features to the observed measurement is proposed with two sensor frames obtained from two sensors. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem, though nonlinearity is directly involved in the Kalman filter equations, compared to the conventional approach. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Multi-level decision making in reconfigurable machining systems using fuzzy logic

Reconfigurable machine tools (RMTs) are synthesised using the principles of modular design in order to achieve the required structural design for a particular part to be manufactured. In this paper, an effective method that uses multi-level fuzzy decisions to create dynamic optimal configurations of machine structures with respect to a given part geometry is presented. A system of modular machine configuration is utilised to arrive at machine configurations considering the fuzzy constraints that are pertinent in this process. With the utilisation of fuzzy decisions for the configuration system model, selection of optimal modular tool configurations is done. Decisions are made at a particular threshold level so as to verify the appropriateness of such decisions.     

Using fuzzy logic to implement decision policies in system dynamics models

This paper aims to show how the use of fuzzy systems can enhance the application of system dynamics (SD), with the construction of virtual worlds, for organizational learning. By doing so, the main contribution is to propose a fuzzy-SD integrated methodology that allows a natural language modeling of decision policies. At first it will be revised the recent literature with practical and theoretical fuzzy-SD integration showing that the motivation and purpose of this article have not been explored yet. Following, it will be scrutinized the origin of system dynamics to theoretically justify the proposed fuzzy-SD integration for organizational learning. Finally, it will be presented a hypothetical case study based on the balance scorecard model to illustrate the methodology in action.     

A novel approach to multi-attribute decision making based on intuitionistic fuzzy sets

In this paper we present a new approach to solve multi-attribute decision making problems in intuitionistic fuzzy environment. This approach is based on a new ranking method of intuitionistic fuzzy sets, in which the evaluated values (in the form of intervals) of the same alternative with different attributes are considered as one unified entity. According to people’s intuition, the ranking method proposed in this paper is mainly grounded on a revised score function and a revised accuracy function of intuitionistic fuzzy sets. Different from the traditional methods, in this new approach, the degree of membership, the degree of nonmembership and the degree of hesitation are considered with various importance in reflecting the true image of the respective alternative. Furthermore, an optimization model is established to estimate the relative degree of importance of each quantity. Finally, two practical examples are provided to illustrate our approach.     

A decision support system for coating selection based on fuzzy logic and multi-criteria decision making

Wear and corrosion are the most important factors that the surface of the engineering parts must confront. The need for protection and improvement of the mechanical characteristics of the surface of engineering parts can be to some extent satisfied by coatings. Coatings are considered as an excellent solution when resistance to corrosion, oxidation or low friction is demanded, but due the complexity of selecting the appropriate one, engineers often avoid them. The need for simultaneous consideration of qualitative and quantitative properties, render the use of classic material selection theories inadequate. An expert system for coating selection is presented in this paper, which can handle both qualitative and quantitative variables. The mathematical model used combines the multi-criteria decision making theories (MCDM) together with the fuzzy sets theory. The “Max-Min set” method is applied to calculate the ordering value of the alternatives while the TOPSIS method is used to rank them. A numerical example is provided to illustrate the method. Finally, the process presented can be easily computerized, to create the relative software.     

A new approach to partial constraint satisfaction problems

A finite binary Constraint Satisfaction Problem (CSPs) is defined as consisting of a set of n problem variables, a domain of d potential values for each variable and a set of m binary constraints involving only two variables at a time. A solution to such a CSP is specified by assignment of a value to each variable that does not violate any of the constraints. The CSPs belong to the class of NP-Complete Problems. Backtracking and its variants have been generally used for solving CSPs. The class of Partial Constraint Satisfaction Problems (PCSPs) is a subclass of CSPs that are either too difficult to solve or are unsolvable. Near optimal solutions are always desired to these problems.

In this article, we have considered only finite binary CSPs or PCSPs and developed a method of time complexity O(n ² d ²) to obtain a near optimal solution for them. The performance of the method in terms of the average number of consistency checks and the average number of constraint violations is measured on various randomly generated binary CSPs and compared with the Branch and Bound (BB) method used to obtain the same solution. The BB method is a widely used optimization technique that may be viewed as a variation of backtracking. Thus, it was a natural choice in seeking an analog of backtracking to find optimal partial solutions for PCSPs. The proposed method moves much faster to the solution. The performance results indicate that in terms of the number of consistency checks, the proposed method has much less consistency checks than BB whereas in terms of average number of constraint violations both methods are same. An upper bound on the distance of the solution from the optimal solution is obtained analytically as ?n(n???2)(d???2)/(d???1)?.     

A logic approach to fuzzy relational databases

A new definition of Fuzzy Relational Database is given by using logical concepts. With this definition it is possible to consider several kinds of fuzziness for the database attributes. A Domain Calculus-based query language is also established for the model. It allows us to formulate several types of queries with different lack of precision levels. © 1994 John Wiley & Sons, Inc.     

A hybrid constraint programming approach to a wood procurement problem with bucking decisions

In this article, we present a wood procurement problem that arises in Eastern Canada. We solve a multi-period wood supply planning problem, while taking into account bucking decisions. Furthermore, we present a new form of flexibility which allows the harvesting capacity to change from one time period to another. We study the impact of such flexibility upon the harvesting cost. We assess the performance of the problem by comparing it with a variant where the harvesting capacity is fixed during sites’ harvesting. To address this problem, we develop a hybrid approach based on both constraint and mathematical programming. In the first phase, we propose a constraint programming model dealing with forest sites harvesting and bucking problems. The result of this model is used as part of an initial solution for the whole problem formulated as a mixed integer model. We test the two versions of the problem on a set of different demand instances and we compare their results.     

A new method selection approach for fuzzy group multicriteria decision making

Fuzzy multicriteria decision making (MCDM) has been widely used in ranking a finite number of decision alternatives characterized by fuzzy assessments with respect to multiple criteria. In group decision settings, different fuzzy group MCDM methods often produce inconsistent ranking outcomes for the same problem. To address the ranking inconsistency problem in fuzzy group MCDM, this paper develops a new method selection approach for selecting a fuzzy group MCDM method that produces the most preferred group ranking outcome for a given problem. Based on two group averaging methods, three aggregation procedures and three defuzzification methods, 18 fuzzy group MCDM methods are developed as an illustration to solve the general fuzzy MCDM problem that requires cardinal ranking of the decision alternatives. The approach selects the group ranking outcome of a fuzzy MCDM method which has the highest consistency degree with its corresponding ranking outcomes of individual decision makers. An empirical study on the green bus fuel technology selection problem is used to illustrate how the approach works. The approach is applicable to large-scale group multicriteria decision problems where inconsistent ranking outcomes often exist between different fuzzy MCDM methods.     

An automatic approach to reaching consensus in multiple attribute group decision making

In this work, we consider the problem of consensus of multiple attribute group decision making, and develop an automatic approach to reaching consensus among group opinions. In the process of group decision making, each expert provides his/her preferences over the alternatives with respect to each attribute, and constructs an individual decision matrix. The developed approach first aggregates these individual decision matrices into a group decision matrix by using the additive weighted aggregation (AWA) operator, and then establishes a convergent iterative algorithm to gain a consentaneous group decision matrix. Then based on the consentaneous group decision matrix, the approach utilizes the AWA operator to derive the overall attribute values of alternatives, by which the most desirable alternative can be found out. Finally, we detailedly expound the implementation process of the approach with a practical example.     

Approach to decision making in fuzzy environment

A general approach to solving a wide class of optimization problems with fuzzy coefficients in objective functions and constraints is described. It is based on a modification of traditional mathematical programming methods and consists in formulating and solving one and the same problem within the framework of interrelated models with constructing equivalent analogs with fuzzy coefficients in objective function alone. This approach allows one to maximally cut off dominated alternatives from below as well as from above. The subsequent contraction of the decision uncertainty region is associated with reduction of the problem to multicriteria decision making in a fuzzy environment. The approach is applied within the context of fuzzy discrete optimization models, that is based on a modification of discrete optimization algorithms. The results of the paper are of a universal character and are already being used to solve problems of the design and control of power systems and subsystems.     

A new approach to fuzzy group decision making with trapezoidal fuzzy preference relations by using compatibility measure

This paper aims to develop a new approach to deal with fuzzy group decision making (GDM) with additive trapezoidal fuzzy preference relations (ATFPRs) by using compatibility measure. We firstly present some concepts of compatibility index and expected preference relation (PR) for ATFPR and then propose a compatibility improving algorithm to help each individual PR achieve acceptable compatibility . Moreover, a least deviation model is provided to obtain the priority vector. Besides, based on the criterion of minimizing the compatibility index, we put forward an optimal model to determine the weights of experts in GDM. Finally, the GDM process with compatibility of ATFPRs is presented, and an illustrative example is utilized to verify the developed approach . The main features of our approach are that: (1) It guarantees that each individual ATFPR is acceptably compatible by using compatibility improving algorithm. (2) It ensures that experts’ weights in group aggregation are determined objectively by optimal model.

     

A hybrid approach for fuzzy multi-attribute decision making in machine tool selection with consideration of the interactions of attributes

Global economic competition has spurred the manufacturing sector to improve and invest in modern equipment to satisfy the needs of the market. In particular, machine tool selection is the most important problem; it plays a primary role in the improvement of productivity and flexibility in the manufacturing environment and involves the imprecise, vague and uncertain information. This paper presents the hybrid approach of the fuzzy ANP (Analytic Network Process) and COPRAS-G (COmplex PRoportional ASsessment of alternatives with Grey relations) for fuzzy multi-attribute decision-making in evaluating machine tools with consideration of the interactions of the attributes. The fuzzy ANP is used to handle the imprecise, vague and uncertain information from expert judgments and model the interaction, feedback relationships and interdependence among the attributes to determine the weights of the attributes. COPRAS-G is employed to present the preference ratio of the alternatives in interval values with respect to each attribute and calculate the weighted priorities of the machine alternatives. Alternatives are ranked in ascending order by priority. As a demonstration of the proposed model, a numerical example is implemented based on the collected data and the literature. The result is then compared with the rankings provided by other methods such as TOPSIS-G, SAW-G and GRA. Moreover, a sensitivity analysis is conducted to verify the robustness of the ranking. The result highlights that the hybrid approach of the fuzzy ANP and COPRAS-G is a highly flexible tool and reaches an effective decision in machine tool selection.     

A mathematical programming approach to multi-attribute decision making with interval-valued intuitionistic fuzzy assessment information

This article proposes an approach to handle multi-attribute decision making (MADM) problems under the interval-valued intuitionistic fuzzy environment, in which both assessments of alternatives on attributes (hereafter, referred to as attribute values) and attribute weights are provided as interval-valued intuitionistic fuzzy numbers (IVIFNs). The notion of relative closeness is extended to interval values to accommodate IVIFN decision data, and fractional programming models are developed based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine a relative closeness interval where attribute weights are independently determined for each alternative. By employing a series of optimization models, a quadratic program is established for obtaining a unified attribute weight vector, whereby the individual IVIFN attribute values are aggregated into relative closeness intervals to the ideal solution for final ranking. An illustrative supplier selection problem is employed to demonstrate how to apply the proposed procedure.     

A constraint satisfaction approach to resolving product configuration conflicts

Product configuration is an essential means for selecting various components to constitute a customized product with the aim of meeting the individualized requirements of a customer. Nevertheless, configuration conflicts may occur when too strict requirements of the customer are given and thus any configuration cannot be found to satisfy the customer requirements. In this situation, it is vital important for a product configurator to recommend the corrective actions for generating valid configurations. In this paper, we present a method of applying constraint satisfaction to resolve product configuration conflicts. In the presented approach, the configuration conflict problem is encoded as a CSP (constraint satisfaction problem). Therefore, the resolution to the configuration conflict problem can be regarded as solving a CSP. As a consequence, corrective actions such as removing selected components or adding new components are suggested by the system. In the case of multiple resolving strategies, a multi-objective decision model is adopted to find the optimal repair strategy according to customer preferences. A computer configuration is illustrated to demonstrate the effectiveness of the presented approach.     

Using a fuzzy multi-criteria decision making approach to evaluate alternative licensing mechanisms

Selecting a licensing policy for third-generation (3G) mobile communications services in Taiwan will have a profound impact on the government’s fiscal income, the advancement of domestic telecommunications technologies and services, and the potential return on investment for 3G licensees. Evaluation criteria should include satisfying multi-goals. The top-level goals are set to satisfy requirements of the government, consumers, and need for competence of the operators. Under each of these three first-tier goals, four second-tier evaluation criteria may be used to assess to what extent the first-tier goals are satisfied. Scholars and experts in the Taiwan telecommunications arena were surveyed to determine their preferences between pairs of evaluation criteria. Additionally, they were asked to estimate the utility scores of each criteria achieved by alternative licensing policies. Fuzzy multi-criteria decision making (MCDM) methodology was then used to evaluate four 3G licensing policies in Taiwan, including: auction, beauty contest, tender, and beauty contest with fixed license fee. The survey results revealed the views and preferences of experts with different backgrounds. The methodology and experience presented in this study could serve as a reference for telecom regulators in constructing their 3G licensing policies.     

A new approach to hybrid probabilistic logic programs

This paper presents a novel revision of the framework of Hybrid Probabilistic Logic Programming, along with a complete semantics characterization, to enable the encoding of and reasoning about real-world applications. The language of Hybrid Probabilistic Logic Programs framework is extended to allow the use of non-monotonic negation, and two alternative semantical characterizations are defined: stable probabilistic model semantics and probabilistic well-founded semantics. These semantics generalize the stable model semantics and well-founded semantics of traditional normal logic programs, and they reduce to the semantics of Hybrid Probabilistic Logic programs for programs without negation. It is the first time that two different semantics for Hybrid Probabilistic Programs with non-monotonic negation as well as their relationships are described. This proposal provides the foundational grounds for developing computational methods for implementing the proposed semantics. Furthermore, it makes it clearer how to characterize non-monotonic negation in probabilistic logic programming frameworks for commonsense reasoning. An erratum to this article can be found at