A commutative l-monoid for classification with fuzzy attributes

In this paper the properties of an algebraic fuzzy structure are investigated in detail. The structure is suitable for modeling classifications through clusters composed of conventional sets and fuzzy attributes. We show that the structure is an integral commutative l-monoid. The expressive power of the structure is such that several situations can be viewed as classification problems, e.g., fuzzy assessment of students, user modeling for fuzzy hypermedia systems, spaces of the cognitive states of the user of a tutoring system, financial investments, medical diagnoses. The problem of getting the unknown classification beginning from the final classification is deeply investigated and it is shown that the problem is strictly related to the solution of an equation in the monoid. Thus it is possible to construct procedures of the type `what happens if' which permit to attain significant results both on the theoretical side and the applicative one. Finally, by means of this approach, both the absolute and the relative relevance of an attribute are defined and evaluated, given a universe of discourse and a set of classifications. Moreover, this couple of features allow to develop a sophisticated analysis of how a new attribute can be obtained beginning from a set of attributes.     

Some asymptotic results for the combination of evidence problem

The combination of evidence problem is treated here as the construction of a posterior possibility function (or probability function, as a special case) describing an unknown state parameter vector of interest. This function exhibits the appropriate components contributing to knowledge of the parameter, including conditions or inference rules, relating the parameter with observable characteristics or attributes, and errors or confidences of observed or reported data. Multivalued logic operators - in particular, disjunction, conjunction, and implication operators, where needed – are used to connect these components and structure the posterior function. Typically, these operators are well-defined for only a finite number of arguments. Yet, often in the problem at hand, a number of observable attributes represent probabilistic concepts in the form of probability density functions. This occur, for example, for attributes representing ordinary numerical measurements- as opposed to those attributes representing linguistic-based information, where non-probabilistic possibility functions are used. Thus the problem of discretization of probabilistic attributes arises, where p.d.f.'s are truncated and discretized to probability functions. As the discretization process becomes finer and finer, intuitively the posterior function should better and better represent the information available. Hence, the basic question that arises is: what is the limiting behavior of the resulting posterior functions when the level of discretization becomes infinitely fine, and, in effect, the entire p.d.f.'s are used?It is shown in this paper that under mild analytic conditions placed upon the relevant functions and operators involved, nontrivial limits in the above sense do exist and involve monotone transforms of statistical expectations of functions of random variable corresponding to the p.d.f.'s for the probabilistic attributes.     

Fuzzy measures of supplier evaluation under lean concepts

Supplier evaluation is a multiple attribute decision-making (MADM) problem involving a mixture of qualitative and quantitative attributes. Some mathematical programming techniques have difficulty in dealing with the qualitative attributes that are not easy to measure but very important in supplier evaluation. Fuzzy set theory is useful whenever variables defining a complex and vague system can neither be quantitatively defined nor assigned very precise measures, but are described by linguistic values. This paper presents a procedure for supplier evaluation by incorporating fuzzy set theory into the evaluation process to handle the qualitative attributes in the problem. A mathematical ranking method is used to convert the qualitative attributes into crisp scores. All the quantitative and qualitative performance measures are normalized and then using the proposed model, data are integrated into a single score to rank suppliers. The model application is demonstrated through two previously reported data sets under lean philosophy.     

基于二元语义TAC积分算子的语言型多属性群决策方法

针对属性之间存在模糊关联的语言型多属性群决策问题,给出了二元语义TAC(Two-Additive Choquet)积分算子的定义,分析和证明了算子的有关性质,并提出了相应的决策方法。该方法首先将各专家提供的语言短语形式的属性权重信息、属性关联信息与属性评价信息转化为二元语义形式,然后利用二元语义TAC积分算子将转化后的属性相关信息集结为各专家的方案评价值,并进一步集结专家意见获得方案的综合评价值,从而确定其排序。最后,通过实例分析和方法比较说明了所给方法的有效性和优点。研究结果表明,该方法具有属性关联刻画细致、计算过程简单且无信息损失、决策结果可解释性强等优点,为求解属性之间存在模糊关联的语言型多属性群决策问题提供了一种新的途径。     

A survey on algorithmic approaches for solving tourist trip design problems

The tourist trip design problem (TTDP) refers to a route-planning problem for tourists interested in visiting multiple points of interest (POIs). TTDP solvers derive daily tourist tours, i.e., ordered visits to POIs, which respect tourist constraints and POIs attributes. The main objective of the problem discussed is to select POIs that match tourist preferences, thereby maximizing tourist satisfaction, while taking into account a multitude of parameters and constraints (e.g., distances among POIs, visiting time required for each POI, POIs visiting days/hours, entrance fees, weather conditions) and respecting the time available for sightseeing on a daily basis. The aim of this work is to survey models, algorithmic approaches and methodologies concerning tourist trip design problems. Recent approaches are examined, focusing on problem models that best capture a multitude of realistic POIs attributes and user constraints; further, several interesting TTDP variants are investigated. Open issues and promising prospects in tourist trip planning research are also discussed.     

基于模糊的创新动态联盟伙伴选择多属性决策模型

针对开放式创新合作伙伴选择过程中决策者判断模糊性的特点,考虑到不同的决策者其决策可信性的不同,提出了开放式创新合作伙伴选择的模糊多属性群决策模型。首先描述了开放式创新合作伙伴选择的多属性群决策问题;然后依据该种方法比较突出的特点,考虑到决策者对评价指标权重的不同,对TOPSIS方法加以改进;最后,通过算例说明了该方法的有效性和可行性。     

A multiple attribute group decision making framework for the evaluation of lean practices at logistics distribution centers

In recent years, to adapt rapidly to changing market environments and outdo the competition more companies and organizations have adopted lean management practices. One problem that has arisen in these companies and organizations is the need to develop methods to accurately evaluate the lean practices performance. This study proposes a multiple attribute group decision making (MAGDM) framework to facilitate such evaluations. It deals with the consensus process and selection process for MAGDM problems based on the 2-tuple linguistic computation model. The similarity degree and consensus for the linguistic decision matrix are defined using an Euclidian distance function. An algorithm describing the consensus reaching process is presented and its properties analyzed. The entropy method is generalized to a linguistic setting to derive the importance weights for the attributes. One of the main ideas behind the entropy method is that attributes with quite different values are considered more important and therefore have higher weights. Finally, the developed MAGDM framework is applied to a lean practices evaluation problem for a commercial tobacco company’s logistics distribution centers in China.     

An extended transportation problem: a DEA-based approach

The transportation problem is an important network structured linear programming problem that arises in several contexts and that has received a great deal of attention in the literature. The existing transportation problems are limited to consideration unit of shipping cost or profit along an arc. However, in many real applications, various attributes are usually considered in a transportation problem. The current paper, proposes an extension to this problem in the presence of multiple in-commensurate inputs and outputs for each arc. The concept of relative efficiency is defined for each possible transportation plan. Two linear programming models are proposed to determine the transportation plan with the maximum efficiency. A numerical example is used to illustrate the applicability of the approach.     

An attribute weight based feedback model for multiple attributive group decision analysis problems with group consensus requirements in evidential reasoning context

In an evidential reasoning context, a group consensus (GC) based approach can model multiple attributive group decision analysis problems with GC requirements. The predefined GC is reached through several rounds of group analysis and discussion (GAD) in the approach. However, the GAD with no guidance may not be the most appropriate way to reach the predefined GC because several rounds of GAD will spend a lot of time of all experts and yet cannot help them to effectively emphasize on the assessments which primarily damage the GC. In this paper, an attribute weight based feedback model is constructed to effectively identify the assessments primarily damaging the GC and accelerate the GC convergence. Considering important attributes with the weights more than or at least equal to the mean of the weights of all attributes, the feedback model constructs identification rules to identify the assessments damaging the GC for the experts to renew. In addition, a suggestion rule is introduced to generate appropriate recommendations for the experts to renew their identified assessments. The identification rules are constructed at three levels including the attribute, alternative and global levels. The feedback model is used to solve an engineering project management software selection problem to demonstrate its detailed implementation process, its validity and applicability, and its advantages compared with the GC based approach.     

Application of multi-dimensional procurement auction in single-period inventory models

Procurement is one of the major activities in the Manufacturing Resource Planning (MRP II), which is closely coupled with inventory management. Any improvement in this area will have a direct impact on the performance of the entire supply chain. Auction mechanism can be a successful procurement method when there are several potential suppliers available. In this paper, we consider a single-period inventory model when the selection of the supplier is done through a reverse auction with bids consisting of several attributes, namely price, shortage quantity, and lead time. The multi-dimensionality of the bid increases the complexity of the underlying problem creating a need for an approach to determine an approximate optimal target level. This is needed for the initiation of the auction and the procurement process. Monte-Carlo simulation method was used to investigate the performance of the approximation. Simulation results show that the analytical method provides an acceptable approximation for the optimal target inventory level.     

Categorical approach to modelling and to coupling of models

Solution of any engineering problem starts with a modelling process, which typically involves a choice among different kinds of models. To create a realistic model, one has to think carefully about the modelling process. Particularly in the case of coupled problems when several models are coupled together to represent a given physical phenomenon. This paper presents an approach based on the category theory that allows to describe this modelling process on a more abstract level. Using the advantages of abstract level, one can describe the coupling process in a concise way and introduce certain criteria to check consistency of a coupled model. The main idea of the proposed approach is to introduce a structure in the modelling process, which allows to see how different models interact without a precise look into them. Copyright © 2016 John Wiley & Sons, Ltd.     

Prioritisation of an IT budget within a local authority

This paper describes the prioritisation of an IT budget within a department of a local authority. The decision problem is cast as a simple multiattribute evaluation but from two perspectives. First, as an exercise in group decision making. Here the emphasis is on a shared process wherein the object is to obtain consensus. The use of an explicit evaluation framework and the ability to interact with the evaluation data in real time via a simple spreadsheet model were found to improve the decision making. Second, the prioritisation is made analytically. The motivation is to determine the degree to which the rankings are the result of the structural characteristics of the projects themselves rather than of the differences in importance attached to the achievement of the goals represented by the project attributes. Three methods are used: Monte Carlo simulation of ranks, cluster analysis based on attributes and an approach based on entropy maximisation. It is found that in the case studied the structure inherent in the data is high and so the results of the analyses are robust. Finally, a procedure is suggested for the appropriate use of these analyses via a facilitator to aid prioritisation decisions.     

A unified solution framework for multi-attribute vehicle routing problems

Vehicle routing attributes are extra characteristics and decisions that complement the academic problem formulations and aim to properly account for real-life application needs. Hundreds of methods have been introduced in recent years for specific attributes, but the development of a single, general-purpose algorithm, which is both efficient and applicable to a wide family of variants remains a considerable challenge. Yet, such a development is critical for understanding the proper impact of attributes on resolution approaches, and to answer the needs of actual applications. This paper contributes towards addressing these challenges with a component-based design for heuristics, targeting multi-attribute vehicle routing problems, and an efficient general-purpose solver. The proposed Unified Hybrid Genetic Search metaheuristic relies on problem-independent unified local search, genetic operators, and advanced diversity management methods. Problem specifics are confined to a limited part of the method and are addressed by means of assignment, sequencing, and route-evaluation components, which are automatically selected and adapted and provide the fundamental operators to manage attribute specificities. Extensive computational experiments on 29 prominent vehicle routing variants, 42 benchmark instance sets and overall 1099 instances, demonstrate the remarkable performance of the method which matches or outperforms the current state-of-the-art problem-tailored algorithms. Thus, generality does not necessarily go against efficiency for these problem classes.     

A primal–dual algorithm for the economic lot-sizing problem with multi-mode replenishment

The classical economic lot-sizing problem assumes that a single supplier and a single transportation mode are used to replenish the inventory. This paper studies an extension of this problem where several suppliers and transportation modes are available. The decision-making process in this case involves identifying (i) the timing for an order; (ii) the choice of shipment modes; and (iii) the order size for each mode. The problem is defined as a network flow problem with multiple setups cost function and additional side constraints. This study provides an MIP formulation for the problem. We also provide an additional formulation of the problem by redefining its decision variables and show that the dual of the corresponding LP-relaxation has a special structure. We take advantage of the structure of the dual problem to develop a primal–dual algorithm that generates tight lower and upper bounds. Computational results demonstrate the effectiveness of the algorithm.     

基于RS和SVM的动态供应链成员选择算法

良好的成员选择方法是动态供应链平稳运行的重要基础,针对动态供应链成员选择时面临决策属性多且可供决策分析数据样本少的难题,提出了基于粗糙集和支持向量机的动态供应链成员选择算法,核心是应用粗糙集进行属性约简,然后结合支持向量机进行链上成员分类.方法在保证不会降低分类性能的前提下,达到降低数据维数和分类过程中复杂度的目的.     

Heuristics for the lexicographic max-ordering vehicle routing problem

In this paper, we propose fast heuristics for the vehicle routing problem (VRP) with lexicographic max-order objective. A fixed number of vehicles, which are based at a depot, are to serve customers with known demands. The lexicographic max-order objective is introduced by asking to minimize lexicographically the sorted route lengths. Based on a model for this problem, several approaches are studied and new heuristic solution procedures are discussed resulting in the development of a sequential insertion heuristic and a modified savings algorithm in several variants. Comparisons between the algorithms are performed on instances of the VRP library VRPLIB. Finally, based on the results from the computational experiments, conclusions about the applicability and efficiency of the presented algorithms are drawn.     

A new multiple attribute group decision making method in intuitionistic fuzzy setting

The multiple attribute group decision making (MAGDM) problem with intuitionistic fuzzy information investigated in this paper is very useful for solving complicated decision problems under uncertain circumstances. Since experts have their own characteristics, they are familiar with some of the attributes, but not others, the weights of the decision makers to different attributes should be different. We derive the weights of the decision makers by aggregating the individual intuitionistic fuzzy decision matrices into a collective intuitionistic fuzzy decision matrix. The expert has a big weight if his evaluation value is close to the mean value and has a small weight if his evaluation value is far from the mean value. For the incomplete attribute weight information, we establish some optimization models to determine the attribute weights. Furthermore, we develop several algorithms for ranking alternatives under different situations, and then extend the developed models and algorithms to the MAGDM problem with interval-valued intuitionistic fuzzy information. Numerical results finally illustrate the practicality and efficiency of our new algorithms.     

A new learning strategy for stereo matching derived from a fuzzy clustering method

This paper presents an approach to the local stereo correspondence problem. The primitives or features used are groups of collinear connected edge points called segments. Each segment has several associated attributes or properties. We have verified that the differences of the attributes for the true matches cluster in a cloud around a center. Then for each current pair of primitives we compute a distance between the difference of its attributes and the cluster center. The correspondence is established in the basis of the minimum distance criterion (similarity constraint). We have designed an image understanding system to learn the best representative cluster center. For such purpose a new learning method is derived from the Fuzzy c-Means (FcM) algorithm where the dispersion of the true samples in the cluster is taken into account through the Mahalanobis distance. This is the main contribution of this paper. A better performance of the proposed local stereo-matching learning method is illustrated with a comparative analysis between classical local methods without learning.     

Requisite Decision Modelling: A Case Study

This case study in decision analysis concerns a company that had to decide between continuing to manufacture an old product that might in the near future by banned by the government or introducing an improved but conventional product that would beat the ban but might lose market share to competing products using microchip technology. A decision tree with three attributes describing the consequences over a ten-year horizon modelled the problem. Implementation on a micro computer facilitated extensive sensitivity analyses, the final round of which were conducted by the Board of Directors. More and more pessimistic assumptions were made until the decision switched from the new to the old product; at that point no Director believed all the assumptions. Thus, agreement was reached about the decision even though the Directors disagreed about the uncertainties. The case illustrates ‘requisite’ rather than optimal decision modelling and shows the essential roles of problem structure and sensitivity analysis.     

An axiomatic basis for multiattribute value functions defined on binary attributes

The insufficiency of solvability axioms for defining an additive conjoint structure in the case of binary attributes motivates an alternative approach to development of an axiomatic basis for multilinear, multiplicative and additive value functions defined on binary attributes. The alternative approach is to define four sufficiency conditions with respect to binary attributes with alternative combinations of these conditions allowing derivation of the multilinear, multiplicative and additive models.