Multiobjective model for solving resource‐leveling problem with discounted cash flows

Nowadays, executers are struggling to improve the economic and scheduling situation of projects. Construction scheduling techniques often produce schedules that cause undesirable resource fluctuations that are inefficient and costly to implement on site. The objective of the resource‐leveling problem is to reduce resource fluctuation related costs (hiring and firing costs) without violating the project deadline. In this article, minimizing the discounted costs of resource fluctuations and minimizing the project makespan are considered in a multiobjective model. The problem is formulated as an integer nonlinear programming model, and since the optimization problem is NP‐hard, we propose multiobjective evolutionary algorithms, namely nondominated sorting genetic algorithm‐II (NSGA‐II), strength Pareto evolutionary algorithm‐II (SPEA‐II), and multiobjective particle swarm optimization (MOPSO) to solve our suggested model. To evaluate the performance of the algorithms, experimental performance analysis on various instances is presented. Furthermore, in order to study the performance of these algorithms, three criteria are proposed and compared with each other to demonstrate the strengths of each applied algorithm. To validate the results obtained for the suggested model, we compared the results of the first objective function with a well‐tuned genetic algorithm and differential algorithm, and we also compared the makespan results with one of the popular algorithms for the resource constraints project scheduling problem. Finally, we can observe that the NSGA‐II algorithm presents better solutions than the other two algorithms on average.     

Inventory and pricing strategies for deteriorating items with shortages: A discounted cash flow approach

In this article, we consider an infinite horizon, single product economic order quantity where demand and deterioration rate are continuous and differentiable function of price and time, respectively. In addition, we allow for shortages and completely backlogged. The objective is to find the optimal inventory and pricing strategies maximizing the net present value of total profit over the infinite horizon. For any given selling price, we first prove that the optimal replenishment schedule not only exists but is unique. Next, we show that the total profit per unit time is a concave function of price when the replenishment schedule is given. We then provide a simple algorithm to find the optimal selling price and replenishment schedule for the proposed model. Finally, we use a couple of numerical examples to illustrate the algorithm.     

Evolutionary fuzzy hybrid neural network for project cash flow control

This paper develops an evolutionary fuzzy hybrid neural network (EFHNN) to enhance project cash flow management. The developed EFHNN combines neural networks (NN) and high order neural networks (HONN) into a hybrid neural network (HNN), which acts as the major inference engine and operates with alternating linear and nonlinear NN layer connections. Fuzzy logic is employed to sandwich the HNN between a fuzzification and defuzzification layer. The authors developed and applied the EFHNN to sequential cash flow trend problems by fusing HNN, FL, and GA. Results show that the proposed EFHNN can be deployed effectively to sequential cash flow estimation. The performance of linear and nonlinear (high order) neuron layer connectors in the EFHNN was significantly better than the performance achieved by previous models that used singular linear NN. Trained results were used for the prediction and strategic management of project cash flow. The proposed strategy can assist project managers to control project cash flows within the banana envelope of the S-curve to enhance project success.     

运用遗传算法优化项目级现金流问题的研究

针对项目级现金流的优化问题,结合传统的资源受限项目调度模型,得到一种带现金流折现的非线性规划项目调度模型,并设计启发式遗传算法对其进行优化求解.通过引入现金流分析,项目调度模型能反映企业的财务指标,更切合实际.通过设计相应的编码方式、遗传算子和调度生成策略,提出了一种改进的遗传算法并用于求解此优化问题,数值实验结果证明了该算法的正确性和高效性.     

River flow forecasting through nonlinear local approximation in a fuzzy model

This study investigates the potential of nonlinear local function approximation in a Takagi–Sugeno (TS) fuzzy model for river flow forecasting. Generally, in a TS framework, the local approximation is performed by a linear model, while in this approach, linear function approximation is substituted using a nonlinear function approximation. The primary hypothesis herein is that the process being modeled (rainfall–runoff in this study) is highly nonlinear, and a linear approximation at the local domain might still leave a lot of unexplained variance by the model. In this study, subtractive clustering technique is used for domain partition, and neural network is used for function approximation. The modeling approach has been tested on two case studies: Kolar basin in India and Kentucky basin in USA. The results of fuzzy nonlinear local approximation (FNLLA) model are highly promising. The performance of the FNLLA is compared with that of a pure fuzzy inference system (FIS), and it is observed that both the models perform similar at 1-step-ahead forecasts. However, the FNLLA performs much better than FIS at higher lead times. It is also observed that FNLLA forecasts the river flow with lesser error compared to FIS. In the case of Kolar River, more than 40 % of the total data are forecasted with <2 % error by FNLLA at 1 h ahead, while the corresponding value for FIS is only 20 %. In the case of 3-h-ahead forecasts, these values are 25 % for FNLLA and 15 % for FIS. Performance of FNLLA in the case of Kentucky River basin was also better compared to FIS. It is also found that FNLLA simulates the peak flow better than FIS, which is certainly an improvement over the existing models.     

Simulation of bi-directional pedestrian flow by using a cell transmission model

An extended cell transmission model is proposed to simulate bi-directional pedestrian flow in the corridor. In the model, the walking space is discretized into regular hexagonal cells. Three walking preferences of pedestrians are taken into account, including walking on the right-hand side, following front people in the same direction, and avoiding conflicts with ones in the opposite direction. An implementation of the model with periodic boundary condition is then presented. Furthermore, by simulation experiments, we show the effects of the model parameters on flow distributions and fundamental diagrams. The model is also calibrated through comparing the flow-density relationships from empirical data and model simulations. In addition, the model can successfully reproduce typical self-organization phenomena in bi-directional pedestrian flow, e.g., two-lane formation and multi-lane formation, although it is not a microscopic model.     

Credit scoring analysis using a fuzzy probabilistic rough set model

Credit scoring analysis is an important activity, especially nowadays after a huge number of defaults has been one of the main causes of the financial crisis. Among the many different tools used to model credit risk, the recent development of rough set models has proved effective. The original development of rough set theory has been widely generalized and combined with other approaches to uncertain reasoning, especially probability and fuzzy set theories. Since coherent conditional probability assessments cope well with the problem of unifying these different approaches, a merging of fuzzy rough set theory with this subjectivist approach is proposed. Specifically, expert partial probabilistic evaluations are encompassed inside a gradual decision rule structure, with coherence of the conclusion as a guideline. In line with Bayesian rough set models, credibility degrees of multiple premises are introduced through conditional probability assessments. Nonetheless, discernibility with this method remains too fine. Therefore, the basic partition is coarsened by equivalence classes based on the arity of positively, negatively and neutrally related criteria. A membership function, which grades the likelihood of default, is introduced by a peculiar choice of t-norms and t-conorms. To build and test the model, real data related to a sample of firms are used.     

On supply chain cash flow risks

This study models the supply chain related cash flow risks for a business entity measured by the standard deviations of cash inflows, outflows, and netflows of each period in a planning horizon. The goal is to provide an insightful look on how common practices that intend to improve the Cash Conversion Cycle (CCC), e.g., offering early payment discounts, may contribute to cash flow risks. We show the benefits and recommend the best policy of using Asset-Backed Securities (ABS) to finance accounts receivable as a means to shorten the CCC and lower the cash inflow risk. It is particularly helpful to small vendors having tight cash reserves and high financing costs.     

Convenient T-S fuzzy model with enhanced performance using a novel swarm intelligent fuzzy clustering technique

To automatically extract T-S fuzzy models with enhanced performance from data is an interesting and important issue for fuzzy system modeling. In this paper, a novel methodology is proposed for this issue based on a three-step procedure. Firstly, the idea of variable length genotypes is introduced to the artificial bee colony (ABC) algorithm to derive a so-called Variable string length Artificial Bee Colony (VABC) algorithm. The VABC algorithm can be used to solve a kind of optimization problems where the length of the optimal solutions is not known as a priori. Secondly, fuzzy clustering without knowing cluster number as a priori is viewed as such kind of optimization problem. Thus, a novel version of Fuzzy C-Means clustering technique (VABC-FCM), holding powerful global search ability, is proposed based on the VABC algorithm. Use of VABC allows the encoding of variable cluster number. This makes VABC-FCM not require a priori specification of the cluster number. Finally, the proposed VABC-FCM algorithm is used to extract T-S fuzzy model from data. Such VABC-FCM based convenient T-S fuzzy model extraction methodology does not require a specification of rule number as a priori. Some artificial data sets are applied to validate the performance of the convenient T-S fuzzy model. The experimental results show that the proposed convenient T-S fuzzy model has low approximation error and high prediction accuracy with appreciate rule number. Moreover, the convenient T-S fuzzy model is used to model the characteristics of superheated steam temperature in power plant, and the results suggest the powerful performance of the proposed method.     

A discounted integrated inspection-maintenance model for a single deteriorating production facility

In this paper, we address the problem of determining optimum inspection schedules for a single deteriorating production system with a predetermined replacement cycle. It is assumed that, at different discrete points in time over the fixed planning horizon, the facility is inspected to detect its operating state and then it goes over an imperfect preventive maintenance routine to enhance its operating performance. Moreover, the facility undergoes minimal repair once detected in an “out‐of‐control” state. We also adopt the concept of discounted cash flow analysis to account properly for the effect of time value of money on the inspection policies. Under these settings, we formulate the discounted integrated inspection‐maintenance problem as a dynamic programming model with general time to failure distribution. After illustrating the model with a numerical example, we perform sensitivity analysis to investigate the effects of some input parameters on the expected present worth and the number of inspections.     

Eliciting transparent fuzzy model using differential evolution

In this paper a new technique for eliciting a fuzzy inference system (FIS) from data for nonlinear systems is proposed. The strategy is conducted in two phases: in the first one, subtractive clustering is applied in order to extract a set of fuzzy rules, in the second phase, the generated fuzzy rule base is refined and redundant rules are removed on the basis of an interpretability measure. Finally the centers and widths of the Membership Functions (MFs) are tuned by means differential evolution. Case studies are presented to illustrate the efficiency and accuracy of the proposed approach. The results obtained are compared and contrasted with those obtained from a conventionally neuro-fuzzy technique and the superiority of the proposed approach is highlighted.     

Supporting decision-making in patient risk assessment using a hierarchical fuzzy model

Cognition, Technology & Work - In this paper, we present a hierarchical fuzzy model to support patient triage in primary health care. In developing countries like Brazil, public health must...     

Refinement of generated fuzzy production rules by using a fuzzy neural network.

Fuzzy production rules (FPRs) have been used for years to capture and represent fuzzy, vague, imprecise and uncertain domain knowledge in many fuzzy systems. There have been a lot of researches on how to generate or obtain FPRs. There exist two methods to obtain FPRs. One is by painstakingly, repeatedly and time-consuming interviewing domain experts to extract the domain knowledge. The other is by using some machine learning techniques to generate and extract FPRs from some training samples. These extracted rules, however, are found to be nonoptimal and sometimes redundant. Furthermore, these generated rules suffer from the problem of low accuracy of classifying or recognizing unseen examples. The reasons for having these problems are 1) the FPRs generated are not powerful enough to represent the domain knowledge, 2) the techniques used to generate FPRs are pre-matured, ad-hoc or may not be suitable for the problem, and 3) further refinement of the extracted rules has not been done. In this paper we look into the solutions of the above problems by 1) enhancing the representation power of FPRs by including local and global weights, 2) developing a fuzzy neural network (FNN) with enhanced learning algorithm, and 3) using this FNN to refine the local and global weights of FPRs. By experimenting our method with some existing benchmark examples, the proposed method is found to have high accuracy in classifying unseen samples without increasing the number of the FPRs extracted and the time required to consult with domain experts is greatly reduced.     

The facility layout problem approached using a fuzzy model and a genetic search

The problem of facility layout design is discussed, taking into account the uncertainty of production scenarios and the finite production capacity of the departments. The uncertain production demand is modelled by a fuzzy number, and constrained arithmetic operators are used in order to calculate the fuzzy material handling costs. By using a ranking criterion, the layout that represents the minimum fuzzy cost is selected. A flexible bay structure is adopted as a physical model of the system while an effective genetic algorithm is implemented to search for a near optimal solution in a fuzzy contest. Constraints on the aspect ratio of the departments are taken into account using a penalty function introduced into the fitness function of the genetic algorithm. The efficiency of the genetic algorithm proposed is tested in a deterministic context and the possibility of applying the fuzzy approach to a medium-large layout problem is explored.This revised version was published in June 2005 with corrected page numbers.     

Detection of heart valve diseases by using fuzzy discrete hidden Markov model

In the present study, biomedical based application was developed to classify the data belongs to normal and abnormal samples generated by Doppler ultrasound. This study consists of raw data obtaining and pre-processing, feature extraction and classification steps. In the pre-processing step, a high-pass filter, white de-noising and normalization were used. During the feature extraction step, wavelet entropy was applied by wavelet transform and short time fourier transform. Obtained features were classified by fuzzy discrete hidden Markov model (FDHMM). For this purpose, a FDHMM that consists of Sugeno and Choquet integrals and λ fuzzy measurement was defined to eliminate statistical dependence assumptions to increase the performance and to have better flexibility. Moreover, Sugeno integral was used together with triangular norms that are mentioned frequently in the literature in order to increase the performance. Experimental results show that recognition rate obtained by Sugeno fuzzy integral with triangular norm is more successful than recognition rates obtained by standard discrete HMM (DHMM) and Choquet integral based FDHMM. In addition to this, it is shown in this study that the performance of the Sugeno integral based method is better than the performances of artificial neural network (ANN) and HMM based classification systems that were used in previous studies of the authors.     

ATM现金流量动态分析

根据ATM现金流量时间序列,分析ATM的取款量变化规律．用Matlab作为建模和分析工具,通过模型识别和参数估计建立自回归滑动平均模型,刻画ATM上现金流的变化规律,检验模型的正确性．在此基础上可预测未来10天的ATM现金流量,对银行现金的准备和调配工作具有重要意义．     

Discounted cash flow analysis of time series

The most precise models for treating risk and uncertainty in engineering economy require a knowledge of the covariances between future cash flows. Analysts have long been accustomed to developing expected values of future cash flows and some have started to obtain estimates of the variances of these quantities. Few have been willing to grapple with the formidable problems associated with quantitative examination of the covariances. In some cases the cash flows to be predicted may be considered to be future realizations of an established random process. In their book Time Series Analysis, Box and Jenkins (1970) have shown how to identify a model underlying the process providing that a historical record of sufficient length is available. They have also provided algorithms for machine calculation of (1) estimates of the model parameters, (2) unbiased minimum mean-square-error forecasts, and (3) variances of the forecasts of the future values of the process. The present paper extends the technique so as to obtain similar estimates of the covariance matrix. The present worth of the cash flow forecasts is their sum, weighted by the appropriate discount factors. The variance of the present worth is easily written in terms of the discount factors and the covariance function. A PL/I program is presented that accepts estimated model parameters and the historical record of the random cash flow process and then computes (1) the forecasts and their covariances, and (2) the present worth and its variance. Numerical results are presented by way of illustration.     

Multidisciplinary collaborative optimization using fuzzy satisfaction degree and fuzzy sufficiency degree model

Collaborative optimization (CO) is a bi-level multidisciplinary design optimization (MDO) method for large-scale and distributed-analysis engineering design problems. Its architecture consists of optimization at both the system-level and autonomous discipline levels. The system-level optimization maintains the compatibility among coupled subsystems. In many engineering design applications, there are uncertainties associated with optimization models. These will cause the design objective and constraints, such as weight, price and volume, and their boundaries, to be fuzzy sets. In addition the multiple design objectives are generally not independent of each other, that makes the decision-making become complicated in the presence of conflicting objectives. The above factors considerably increase the modeling and computational difficulties in CO. To relieve the aforementioned difficulties, this paper proposes a new method that uses a fuzzy satisfaction degree model and a fuzzy sufficiency degree model in optimization at both the system level and the discipline level. In addition, two fuzzy multi-objective collaborative optimization strategies (Max–Min and α-cut method) are introduced. The former constructs the sufficiency degree for constraints and the satisfaction degree for design objectives in each discipline respectively, and adopts the Weighted Max–Min method to maximize an aggregation of them. The acceptable level is set up as the shared design variable between disciplines, and is maximized at the system level. In the second strategy, the decision-making space of the constraints is distributed in each discipline independently through the allocation of the levels of α. At the system level, the overall satisfaction degree for all disciplines is finally maximized. The illustrative mathematical example and engineering design problem are provided to demonstrate the feasibility of the proposed methods.     

A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations

A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well.     

基于区间T-S 模糊模型的运动模式预测

针对一类复杂的生产过程,提出一种基于运动模式的预测模型.针对该方法中模式类别变量的度量问题,提出采用区间数对定义在模式运动"空间"上的模式类别变量进行度量.为了描述模式在模式运动"空间"的运动,对作者先前提出的区间T-S模糊模型进行了改进,并采用改进后的区间T-S模糊模型建立基于模式类别变量的预测模型.最后,以烧结生产过程实际运行数据为例验证了所提出建模方法的有效性.