mrp with flexible constraints a fuzzy mathematical programming approach in manufacturing environments with complex product structures and multiple production stages material requirements planning mrp systems are the most commonly used for production planning and material supply decision making however in practice diverse difficulties arise such as uncertainty in market demand resources with limited available capacities uncertainty in capacity data or uncertain costs likewise classical procedures of resolution applied in mrp environments do not optimize production decisions this paper provides a new linear programming model for medium term production planning in a capacity constrained mrp multi product multi level and multi period manufacturing environment then this model is transformed into three fuzzy models with flexibility in the objective function in the market demand and in the available capacity of resources the main goal is to determine the master production schedule the mrp for each raw component in each period stock levels delayed demand and capacity usage levels over a given planning horizon in such a way as to hedge against uncertainty finally the model is tested using real data from an automobile seat manufacturer

relating different approaches to solve linear programming problems with imprecise costs abstract linear programming problems involving coefficients in the objective function with some lack of precision are usual to solve them several approaches have been proposed in this paper the different methods studied to solve the problem are briefly described next it is shown that the different solutions obtained from them may be obtained by solving a multiobjective linear programming problem

the expectation hypothesis of interest rates and network theory the case of brazil abstract this paper investigates the topological properties of the brazilian term structure of interest rates network we build the minimum spanning tree mst which is based on the concept of ultrametricity using the correlation matrix for interest rates of different maturities we show that the short term interest rate is the most important within the interest rates network which is in line with the expectation hypothesis of interest rates furthermore we find that the brazilian interest rates network forms clusters by maturity

generalizations of the clustering coefficient to weighted complex networks the recent high level of interest in weighted complex networks gives rise to a need to develop new measures and to generalize existing ones to take the weights of links into account here we focus on various generalizations of the clustering coefficient which is one of the central characteristics in the complex network theory we present a comparative study of the several suggestions introduced in the literature and point out their advantages and limitations the concepts are illustrated by simple examples as well as by empirical data of the world trade and weighted coauthorship networks

an algorithm for generalized fuzzy binary linear programming problems abstract fuzzy binary linear programming fblp problems are very essential in many fields such as assignment and assembly line balancing problems in operational research multiple projects locations and candidates selection cases in management science as well as representing and reasoning with prepositional knowledge in artificial intelligence although fblp problems play a significant role in human decision environment not very much research has focused on fblp problems this work first proposes a simple means of expressing a triangular fuzzy number as a linear function with an absolute term a method of linearizing absolute terms is also presented the developed goal programming gp model weighted by decision makers dms preference aims to optimize the expected objective function and minimize the sum of possible membership functions deviations after presented a novel way of linearizing product terms the solution algorithm is proposed to generate a crisp trade off promising solution that is also an optimal solution in a certain sense three examples equipment purchasing choice investment project selection and assigning clients to project leaders illustrate that the proposed algorithm can effectively solve generalized fblp problems

sensitivity analysis in fuzzy multiobjective linear fractional programming problem in this paper we study measurement of sensitivity for changes of violations in the aspiration level for the fuzzy multiobjective linear fractional programming problem

linear programming with fuzzy objectives abstract this paper presents a new method for solving linear programming problems with fuzzy parameters in the objective function to determine a compromise solution the authors do not reduce the set of infinitely many objective functions to a single compromise objective function as done in classical deterministic or stochastic procedures here the problem is only reduced to a few extreme objective functions the information contained in the membership functions can be used to any extent by a method called level related pair formation moreover if different level sets are considered the stability of the optimal solution can be tested

world trade web topological properties dynamics and evolution this paper studies the statistical properties of the web of import export relationships among world countries using a weighted network approach we analyze how the distributions of the most important network statistics measuring connectivity assortativity clustering and centrality have coevolved over time we show that all node statistic distributions and their correlation structure have remained surprisingly stable in the last 20 years and are likely to do so in the future conversely the distribution of positive link weights is slowly moving from a log normal density towards a power law we also characterize the autoregressive properties of network statistics dynamics we find that network statistics growth rates are well proxied by fat tailed densities like the laplace or the asymmetric exponential power finally we find that all our results are reasonably robust to a few alternative economically meaningful weighting schemes

algorithms for linear programming problems with interval objective function coefficients this paper presents three algorithms for solving linear programming problems in which some or all of the objective function coefficients are specified in terms of intervals which algorithm is applicable depends upon a the number of interval objective function coefficients b the number of nonzero objective function coefficients and c whether or not the feasible region is bounded the algorithms output all extreme points and unbounded edge directions that are multiparametrically optimal with respect to the ranges placed on the objective function coefficients the algorithms are most suitable to linear programs in which the objective function coefficients are deterministic but are likely to vary from time period to time period as for example in blending problems

using fuzzy numbers in network design optimization problems the fluctuations that have taken place in the worldwide economy over the past few years make it difficult to estimate the costs of the equipment that have to be used in order to design a telecommunications network the literature is replete with prescriptive integer programming models for various network design problems that do not take into account the problems inherent vagueness due to equipment costs this paper considers the real world wavelength division multiplexing wdm network design problem the vagueness that appears in this problem is assumed and handled by means of fuzzy set based methodologies a fuzzy integer linear programming model is then proposed to be used as an auxiliary model to solve the former problem with the purpose to obtain cost effective network designs this model is then solved using cplex which is a commercial high performance optimization engine

multiple objective linear programming with parametric criteria coefficients in this paper we study the multiple objective linear programming problem with parametric criteria coefficients this problem is of interest since in many situations the coefficients of the objective functions of a multiple objective linear program either represent estimates of the true data or are subject to systematic variations properties of this problem are developed and an algorithm for generating the set of all weakly efficient extreme points of this problem is described to implement this algorithm a nonconvex subproblem must be solved for each candidate extreme point encountered this is accomplished by applying the generalized benders decomposition method computational results concerning the solution of these subproblems are presented

effect of tolerance in fuzzy linear fractional programming abstract recently fuzzy linear programming flp is evoking greater interest since it can in many ways be well adopted to different decision problems in fuzzy constraints and objective functions of flp tolerance plays an important role in this paper the effect of tolerance in flp is investigated a similar effect is also observed in fuzzy linear fractional programming flfp the presence of singular points is investigated for the flfp

dynamic asset trees and black monday abstract the minimum spanning tree based on the concept of ultrametricity is constructed from the correlation matrix of stock returns the dynamics of this asset tree can be characterised by its normalised length and the mean occupation layer as measured from an appropriately chosen centre called the central node we show how the tree length shrinks during a stock market crisis black monday in this case and how a strong reconfiguration takes place resulting in topological shrinking of the tree

topology of correlation based minimal spanning trees in real and model markets we compare the topological properties of the minimal spanning tree obtained from a large group of stocks traded at the new york stock exchange during a 12 year trading period with the one obtained from surrogated data simulated by using simple market models we find that the empirical tree has features of a complex network that cannot be reproduced even as a first approximation by a random market model and by the widespread one factor model

interest rates hierarchical structure we propose a general method to study the hierarchical organization of financial data the statistical geometrical and topological properties of such data are analyzed by embedding the structure of their correlations in metric graphs in multi dimensional spaces we show an application to two dierent sets of interest rates data in this case we construct triangular embeddings on the sphere the resulting graph contains the minimum spanning tree as sub graph and it preserves its hierarchical structure this results in a clear cluster dierentiation

stock market networks the dynamic conditional correlation approach we demonstrate the economic relevance of minimum spanning trees msts constructed from dynamic conditional correlations dcc for a sample of s p 100 constituents an empirical comparison of mst properties shows that using the standard approach of rolling or sliding window correlations yields trees that are more robust have higher densities and exhibit higher industry clustering than msts based on dcc our results suggest that these properties are achieved at the expense of the smoothing of market dynamics which is better preserved by dcc the dcc approach offers a new perspective for the analysis of complex systems such as stock markets

detecting determinism in short time series using a quantified averaged false nearest neighbors approach we propose a criterion to detect determinism in short time series this criterion is based on the estimation of the parameter e 2 defined by the averaged false neighbors method for analyzing time series cao physica d 110 43 1997 using surrogate data testing with several chaotic and stochastic simulated time series we show that the variation coefficient of e 2 over a few values of the embedding dimension d defines a suitable statistic to detect determinism in short data sequences this result holds for a time series generated by a high dimensional chaotic system such as the mackey glass one different decreasing lengths of the time series are included in the numerical experiments for both synthetic and real world data we also investigate the robustness of the criterion in the case of deterministic time series corrupted by additive noise

dynamics of cluster structures in a financial market network abstract in the course of recent fifteen years the network analysis has become a powerful tool for studying financial markets in this work we analyze stock markets of the usa and sweden we study cluster structures of a market network constructed from a correlation matrix of returns of the stocks traded in each of these markets such cluster structures are obtained by means of the p median problem pmp whose objective is to maximize the total correlation between a set of stocks called medians of size p and other stocks every cluster structure is an undirected disconnected weighted graph in which every connected component cluster is a star or a tree with one central node called a median and several leaf nodes connected with the median by weighted edges our main observation is that in non crisis periods of time cluster structures change more chaotically while during crises they show more stable behavior and fewer changes thus an increasing stability of a market graph cluster structure obtained via the pmp could be used as an indicator of a coming crisis

clustering coefficients of large networks abstract let g be a network with n nodes and eigenvalues 1 2 n then g is called an n d network if it is d regular and max 2 3 n it is shown that if g is an n d network and o d the average clustering coefficient c g of g satisfies c g d n for large d we show that this description also holds for strongly regular graphs and erd s renyi graphs although most real world networks are not constructed theoretically we find that many of them have c g close to d n and many close to 1 2 n d 1 d d 1 where d is the average degree of g and 2 is the average of the numbers of common neighbors over all non adjacent pairs of nodes

influence of noise on the averaged false neighbors method for analyzing time series this paper deals with the influence of noise on the averaged false neighbors method afn proposed by l cao for analyzing time series and describing the dynamical properties of their underlying process first we give a theoretical justification of the afn method results for a pure random time series white gaussian noise then we present some numerical experiments corresponding to different known chaotic processes corrupted by noise simulations on real measured data are also presented eventually after discussing these simulations we are led to state some practical results on the critical noise level not to be exceeded for getting usable results of the afn algorithm

fuzzy boolean programming problems with fuzzy costs a general study abstract this paper deals with boolean linear programming problems involving coefficients in the objective function as fuzzy numbers in the study of these problems different approaches can be proposed to use ranking fuzzy numbers methods and fuzzy preference relations obtaining auxiliary classical boolean programming problems and to use the representation theorem obtaining a convex set with extreme points defined by the lower or upper bound of the a level sets of the fuzzy coefficients and consequently an auxiliary interval boolean programming problem in this paper we develop and link the different approaches

sector analysis for a ftse portfolio of stocks abstract using a portfolio of stocks from the london stock exchange ftse100 index ftse we study both the time dependence of their correlations and the normalized tree length of the associated minimal spanning tree mst the first four moments of the distribution of correlations and lengths of the tree are examined in detail and differences in behavior noted for different economic groups and industries clustering is evident however comparing the classification used prior to 2006 with that introduced in january 2006 it is clear that the new classification apart from one or two notable exceptions is much more compatible with the clustering obtained by the mst analysis we finally compare the mst for real data with that obtained for a synthetic random market the latter tree would seem more like the structure found by coronnello et al for trees based on high frequency data

material requirement planning with fuzzy constraints and fuzzy coefficients we propose a new fuzzy mathematical programming model for production planning under uncertainty in an industrial environment this model considers fuzzy constraints related to the total costs the market demand and the available capacity of the productive resources and fuzzy coefficients for the costs due to the backlog of demand and for the required capacity the main goal is to determine the master production schedule of each product the mrp material requirement planning for each raw component in each period stock levels demand backlog and capacity usage levels over a given planning horizon finally the proposed model is tested by using data from an automobile seat assembler and compared with other fuzzy mathematical programming approaches the experiment shows that the proposed model has not got a better behaviour than more simple fuzzy models but the advantage is that both types of uncertainties fuzziness and lack of knowledge or epistemic uncertainty can be considered in a model with fuzzy constraints and fuzzy coefficients

dynamics of market correlations taxonomy and portfolio analysis the time dependence of the recently introduced minimum spanning tree description of correlations between stocks called the asset tree has been studied in order to reflect the financial market taxonomy the nodes of the tree are identified with stocks and the distance between them is a unique function of the corresponding element of the correlation matrix by using the concept of a central vertex chosen as the most strongly connected node of the tree an important characteristic is defined by the mean occupation layer during crashes due to the strong global correlation in the market the tree shrinks topologically and this is shown by a low value of the mean occupation layer the tree seems to have a scale free structure where the scaling exponent of the degree distribution is different for business as usual and crash periods the basic structure of the tree topology is very robust with respect to time we also point out that the diversification aspect of portfolio optimization results in the fact that the assets of the classic markowitz portfolio are always located on the outer leaves of the tree technical aspects such as the window size dependence of the investigated quantities are also discussed

intensity and coherence of motifs in weighted complex networks the local structure of unweighted networks can be characterized by the number of times a subgraph appears in the network the clustering coefficient reflecting the local configuration of triangles can be seen as a special case of this approach in this paper we generalize this method for weighted networks we introduce subgraph intensity as the geometric mean of its link weights and coherence as the ratio of the geometric to the corresponding arithmetic mean using these measures motif scores and clustering coefficient can be generalized to weighted networks to demonstrate these concepts we apply them to financial and metabolic networks and find that inclusion of weights may considerably modify the conclusions obtained from the study of unweighted characteristics

comparison of different generalizations of clustering coefficient and local efficiency for weighted undirected graphs binary undirected graphs are well established but when these graphs are constructed often a threshold is applied to a parameter describing the connection between two nodes therefore the use of weighted graphs is more appropriate in this work we focus on weighted undirected graphs this implies that we have to incorporate edge weights in the graph measures which require generalizations of common graph metrics after reviewing existing generalizations of the clustering coefficient and the local efficiency we proposed new generalizations for these graph measures to be able to compare different generalizations a number of essential and useful properties were defined that ideally should be satisfied we applied the generalizations to two real world networks of different sizes as a result we found that not all existing generalizations satisfy all essential properties furthermore we determined the best generalization for the clustering coefficient and local efficiency based on their properties and the performance when applied to two networks we found that the best generalization of the clustering coefficient is defined in miyajima and sakuragawa 2014 while the best generalization of the local efficiency is proposed in this letter depending on the application and the relative importance of sensitivity and robustness to noise other generalizations may be selected on the basis of the properties investigated in this letter

false nearest neighbors algorithm and noise corrupted time series the false nearest neighbors fnn algorithm was originally developed to determine the embedding dimension for autonomous time series for noise free computer generated time series the algorithm does a good job in predicting the embedding dimension however the problem of predicting the embedding dimension when the time series data are corrupted by noise was not fully examined in the original studies of the fnn algorithm here it is shown that with large data sets even small amounts of noise can lead to incorrect prediction of the embedding dimension surprisingly as the length of the time series analyzed by fnn grows larger the cause of incorrect prediction becomes more pronounced an analysis of the effect of noise on the fnn algorithm and a solution for dealing with the effects of noise are given here some results on the theoretically correct choice of the fnn threshold are also presented copyright ital 1997 ital the american physical society

weighted assortative and disassortative networks model abstract real world networks process structured connections since they have non trivial vertex degree correlation and clustering here we propose a toy model of structure formation in real world weighted network in our model a network evolves by topological growth as well as by weight change in addition we introduce the weighted assortativity coefficient which generalizes the assortativity coefficient of a topological network to measure the tendency of having a high weighted link between two vertices of similar degrees network generated by our model exhibits scale free behavior with a tunable exponent besides a few non trivial features found in real world networks are reproduced by varying the parameter ruling the speed of weight evolution most importantly by studying the weighted assortativity coefficient we found that both topologically assortative and disassortative networks generated by our model are in fact weighted assortative

clustering of casablanca stock market based on hurst exponent estimates abstract this paper deals with the problem of casablanca stock exchange cse topology modeling as a complex network during three different market regimes general trend characterized by ups and downs increasing trend and decreasing trend in particular a set of seven different hurst exponent estimates are used to characterize long range dependence in each industrial sector generating process they are employed in conjunction with hierarchical clustering approach to examine the co movements of the casablanca stock exchange industrial sectors the purpose is to investigate whether cluster structures are similar across variable increasing and decreasing regimes it is observed that the general structure of the cse topology has been considerably changed over 2009 variable regime 2010 increasing regime and 2011 decreasing regime time periods the most important findings follow first in general a high value of hurst exponent is associated to a variable regime and a small one to a decreasing regime in addition hurst estimates during increasing regime are higher than those of a decreasing regime second correlations between estimated hurst exponent vectors of industrial sectors increase when casablanca stock exchange follows an upward regime whilst they decrease when the overall market follows a downward regime

fuzzy linear programming problems with fuzzy numbers abstract linear programming problems with fuzzy parameters are formulated by fuzzy functions the ambiguity considered here is not randomness but fuzziness which is associated with the lack of a sharp transition from membership to nonmembership parameters on constraint and objective functions are given by fuzzy numbers in this paper our object is the formulation of a fuzzy linear programming problem to obtain a reasonable solution under consideration of the ambiguity of parameters this fuzzy linear programming problem with fuzzy numbers can be regarded as a model of decision problems where human estimation is influential