Search results for: stochastic integrals

Authors: Hacene Benkhoula, Mohamed Badreddine Benabdella, Hamid Bouzeboudja, Abderrahmane Asraoui

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The wind is a random variable difficult to master, for this, we developed a mathematical and statistical methods enable to modeling and forecast wind power. Gaussian Processes (GP) is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space or time and space. GP is an underlying process formed by unrecognized operator’s uses to solve a problem. The purpose of this paper is to present how to forecast wind power by using the GP. The Gaussian process method for forecasting are presented. To validate the presented approach, a simulation under the MATLAB environment has been given.

Keywords: wind power, Gaussien process, modelling, forecasting

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Authors: Ipek Kivanc, Demet Ozgur-Unluakin

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Over the past years, technological innovations and advancements have played an important role in the industrial world. Due to technological improvements, the degree of complexity of the systems has increased. Hence, all systems are getting more uncertain that emerges from increased complexity, resulting in more cost. It is challenging to cope with this situation. So, implementing efficient planning of maintenance activities in such systems are getting more essential. Partially Observable Markov Decision Processes (POMDPs) are powerful tools for stochastic sequential decision problems under uncertainty. Although maintenance optimization in a dynamic environment can be modeled as such a sequential decision problem, POMDPs are not widely used for tackling maintenance problems. However, they can be well-suited frameworks for obtaining optimal maintenance policies. In the classical representation of the POMDP framework, the system is denoted by a single node which has multiple states. The main drawback of this classical approach is that the state space grows exponentially with the number of state variables. On the other side, factored representation of POMDPs enables to simplify the complexity of the states by taking advantage of the factored structure already available in the nature of the problem. The main idea of factored POMDPs is that they can be compactly modeled through dynamic Bayesian networks (DBNs), which are graphical representations for stochastic processes, by exploiting the structure of this representation. This study aims to demonstrate how maintenance planning of dynamic systems can be modeled with factored POMDPs. An empirical maintenance planning problem of a dynamic system consisting of four partially observable components deteriorating in time is designed. To solve the empirical model, we resort to Symbolic Perseus solver which is one of the state-of-the-art factored POMDP solvers enabling approximate solutions. We generate some more predefined policies based on corrective or proactive maintenance strategies. We execute the policies on the empirical problem for many replications and compare their performances under various scenarios. The results show that the computed policies from the POMDP model are superior to the others. Acknowledgment: This work is supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under grant no: 117M587.

Keywords: factored representation, maintenance, multi-component system, partially observable Markov decision processes

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Authors: Jeffrey Tzu-Hao Tsai, Yi-Shan Wong

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This article proposes a cohort-age-period (CAP) model to characterize multi-population mortality processes using cohort, age, and period variables. Distinct from the factor-based Lee-Carter-type decomposition mortality model, this approach is empirically based and includes the age, period, and cohort variables into the equation system. The model not only provides a fruitful intuition for explaining multivariate mortality change rates but also has a better performance in forecasting future patterns. Using the US and the UK mortality data and performing ten-year out-of-sample tests, our approach shows smaller mean square errors in both countries compared to the models in the literature.

Keywords: longevity risk, stochastic mortality model, multivariate mortality rate, risk management

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Authors: Alexander E. Hramov, Vadim V. Grubov, Alexey A. Koronovskii, Maria K. Kurovskaуa, Anastasija E. Runnova

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The perception of visual signals in the brain was among the first issues discussed in terms of multistability which has been introduced to provide mechanisms for information processing in biological neural systems. In this work the influence of the cognitive noise on the visual perception of multistable pictures has been investigated. The study includes an experiment with the bistable Necker cube illusion and the theoretical background explaining the obtained experimental results. In our experiments Necker cubes with different wireframe contrast were demonstrated repeatedly to different people and the probability of the choice of one of the cubes projection was calculated for each picture. The Necker cube was placed at the middle of a computer screen as black lines on a white background. The contrast of the three middle lines centered in the left middle corner was used as one of the control parameter. Between two successive demonstrations of Necker cubes another picture was shown to distract attention and to make a perception of next Necker cube more independent from the previous one. Eleven subjects, male and female, of the ages 20 through 45 were studied. The choice of the Necker cube projection was detected with the Electroencephalograph-recorder Encephalan-EEGR-19/26, Medicom MTD. To treat the experimental results we carried out theoretical consideration using the simplest double-well potential model with the presence of noise that led to the Fokker-Planck equation for the probability density of the stochastic process. At the first time an analytical solution for the probability of the selection of one of the Necker cube projection for different values of wireframe contrast have been obtained. Furthermore, having used the results of the experimental measurements with the help of the method of least squares we have calculated the value of the parameter corresponding to the cognitive noise of the person being studied. The range of cognitive noise parameter values for studied subjects turned to be [0.08; 0.55]. It should be noted, that experimental results have a good reproducibility, the same person being studied repeatedly another day produces very similar data with very close levels of cognitive noise. We found an excellent agreement between analytically deduced probability and the results obtained in the experiment. A good qualitative agreement between theoretical and experimental results indicates that even such a simple model allows simulating brain cognitive dynamics and estimating important cognitive characteristic of the brain, such as brain noise.

Keywords: bistability, brain, noise, perception, stochastic processes

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Authors: Y. de Smet, J. Hubinont

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PROMETHEE and GAIA are well-known Multiple Criteria Decision Aid methods. Given an evaluation table and preference parameters they allow to rank the alternatives, to visualize the problem, to perform sensitivity and robustness analysis, etc. Unfortunately, it is often hard for the Decision Maker (DM) to estimate the precise values of these parameters. Therefore an alternative option is to give ranges of potential values in order to apply Stochastic Multicriteria Acceptability Analysis. This has been recently studied in the context of the SMAA-PROMETHEE method. The aim of this contribution is to propose an SMAA extension of GAIA. We show how this tool can be useful and provide complementary information to SMAA-PROMETHEE. This is illustrated on a pedagogical example.

Keywords: multiple criteria decision making, PROMETHEE, GAIA, SMAA

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Authors: Marianna Bolla

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The aim of this paper is to compare spectral, discrepancy, and degree properties of expanding graph sequences. As we can prove equivalences and implications between them and the definition of the generalized (multiclass) quasirandomness of Lovasz–Sos (2008), they can be regarded as generalized quasirandom properties akin to the equivalent quasirandom properties of the seminal Chung-Graham-Wilson paper (1989) in the one-class scenario. Since these properties are valid for deterministic graph sequences, irrespective of stochastic models, the partial implications also justify for low-dimensional embedding of large-scale graphs and for discrepancy minimizing spectral clustering.

Keywords: generalized random graphs, multiway discrepancy, normalized modularity spectra, spectral clustering

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Authors: Abdulrahman Abdulrahman

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A side weir is a hydraulic structure set into the side of a channel. This structure is used for water level control in channels, to divert flow from a main channel into a side channel when the water level in the main channel exceeds a specific limit and as storm overflows from urban sewerage system. Computation of water surface over the side weirs is essential to determine the flow rate of the side weir. Analytical solutions for water surface profile along rectangular side weir are available only for the special cases of rectangular and trapezoidal channels considering constant specific energy. In this paper, a rectangular side weir located in a combined (trapezoidal with exponential) channel was considered. Expanding binominal series of integer and fraction powers and the using of reduction formula of cosine function integrals, a general analytical formula was obtained for water surface profile along a side weir in a combined (trapezoidal with exponential) channel. Since triangular, rectangular, trapezoidal and parabolic cross-sections are special cases of the combined cross section, the derived formula, is applicable to triangular, rectangular, trapezoidal cross-sections as analytical solution and semi-analytical solution to parabolic cross-section with maximum relative error smaller than 0.76%. The proposed solution should be a useful engineering tool for the evaluation and design of side weirs in open channel.

Keywords: analytical solution, combined channel, exponential channel, side weirs, trapezoidal channel, water surface profile

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Authors: Tet H. Yeap

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An analog restricted Hopfield Network is presented in this paper. It consists of two layers of nodes, visible and hidden nodes, connected by directional weighted paths forming a bipartite graph with no intralayer connection. An energy or Lyapunov function was derived to show that the proposed network will converge to stable states. By introducing hidden nodes, the proposed network can be trained to store patterns and has increased memory capacity. Training to be an associative memory, simulation results show that the associative memory performs better than a classical Hopfield network by being able to perform better memory recall when the input is noisy.

Keywords: restricted Hopfield network, Lyapunov function, simultaneous perturbation stochastic approximation

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Authors: Farouk Metiri, Halim Zeghdoudi, Mohamed Riad Remita

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Credibility theory is an experience rating technique in actuarial science which can be seen as one of quantitative tools that allows the insurers to perform experience rating, that is, to adjust future premiums based on past experiences. It is used usually in automobile insurance, worker's compensation premium, and IBNR (incurred but not reported claims to the insurer) where credibility theory can be used to estimate the claim size amount. In this study, we focused on a popular tool in credibility theory which is the Bayesian premium estimator, considering Lindley distribution as a claim distribution. We derive this estimator under entropy loss which is asymmetric and squared error loss which is a symmetric loss function with informative and non-informative priors. In a purely Bayesian setting, the prior distribution represents the insurer’s prior belief about the insured’s risk level after collection of the insured’s data at the end of the period. However, the explicit form of the Bayesian premium in the case when the prior is not a member of the exponential family could be quite difficult to obtain as it involves a number of integrations which are not analytically solvable. The paper finds a solution to this problem by deriving this estimator using numerical approximation (Lindley approximation) which is one of the suitable approximation methods for solving such problems, it approaches the ratio of the integrals as a whole and produces a single numerical result. Simulation study using Monte Carlo method is then performed to evaluate this estimator and mean squared error technique is made to compare the Bayesian premium estimator under the above loss functions.

Keywords: bayesian estimator, credibility theory, entropy loss, monte carlo simulation

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Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Mahdi Rahimi, Mohammad Mahdi Mojahedian, Mohammad Reza Aref

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In this paper, a cellular communication scenario with a transmitter and an authorized user is considered to analyze its secrecy in the face of eavesdroppers and the interferences propagated unintentionally through the communication network. It is also assumed that some D2D pairs and eavesdroppers are randomly located in the cell. Assuming hardware impairment, perfect connection probability is analytically calculated, and upper bound is provided for the secrecy outage probability. In addition, a method based on random activation of D2Ds is proposed to improve network security. Finally, the analytical results are verified by simulations.

Keywords: physical layer security, stochastic geometry, device-to-device, hardware impairment

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Authors: Hasik Lee, Seung-Young Kho

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Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices.

Keywords: destination estimation, Kernel density estimation, smart card data, validation

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Authors: Luiz G. Véras, Felipe L. Medeiros, Lamartine F. Guimarães

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This work approaches the automatic planning of paths for Unmanned Aerial Vehicles (UAVs) through the application of the Rapidly Exploring Random Tree Star-Smart (RRT*-Smart) algorithm. RRT*-Smart is a sampling process of positions of a navigation environment through a tree-type graph. The algorithm consists of randomly expanding a tree from an initial position (root node) until one of its branches reaches the final position of the path to be planned. The algorithm ensures the planning of the shortest path, considering the number of iterations tending to infinity. When a new node is inserted into the tree, each neighbor node of the new node is connected to it, if and only if the extension of the path between the root node and that neighbor node, with this new connection, is less than the current extension of the path between those two nodes. RRT*-smart uses an intelligent sampling strategy to plan less extensive routes by spending a smaller number of iterations. This strategy is based on the creation of samples/nodes near to the convex vertices of the navigation environment obstacles. The planned paths are smoothed through the application of the method called quintic pythagorean hodograph curves. The smoothing process converts a route into a dynamically-viable one based on the kinematic constraints of the vehicle. This smoothing method models the hodograph components of a curve with polynomials that obey the Pythagorean Theorem. Its advantage is that the obtained structure allows computation of the curve length in an exact way, without the need for quadratural techniques for the resolution of integrals.

Keywords: path planning, path smoothing, Pythagorean hodograph curve, RRT*-Smart

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Authors: Sven Tackenberg, Sönke Duckwitz, Andreas Petz, Christopher M. Schlick

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This paper introduces an extension to the well-established Resource-Constrained Project Scheduling Problem (RCPSP) to apply it to complex maintenance problems. The problem is to assign technicians to a team which has to process several tasks with multi-level skill requirements during a work shift. Here, several alternative activities for a task allow both, the temporal shift of activities or the reallocation of technicians and tools. As a result, switches from one valid work process variant to another can be considered and may be selected by the developed evolutionary algorithm based on the present skill level of technicians or the available tools. An additional complication of the observed scheduling problem is that the locations of the construction sites are only temporarily accessible during a day. Due to intensive rail traffic, the available time slots for maintenance and repair works are extremely short and are often distributed throughout the day. To identify efficient working periods, a first concept of a Bayesian network is introduced and is integrated into the extended RCPSP with pre-emptive and non-pre-emptive tasks. Thereby, the Bayesian network is used to calculate the probability of a maintenance task to be processed during a specific period of the shift. Focusing on the domain of maintenance of the railway infrastructure in metropolitan areas as the most unproductive implementation process at construction site, the paper illustrates how the extended RCPSP can be applied for maintenance planning support. A multi-criteria evolutionary algorithm with a problem representation is introduced which is capable of revising technician-task allocations, whereas the duration of the task may be stochastic. The approach uses a novel activity list representation to ensure easily describable and modifiable elements which can be converted into detailed shift schedules. Thereby, the main objective is to develop a shift plan which maximizes the utilization of each technician due to a minimization of the waiting times caused by rail traffic. The results of the already implemented core algorithm illustrate a fast convergence towards an optimal team composition for a shift, an efficient sequence of tasks and a high probability of the subsequent implementation due to the stochastic durations of the tasks. In the paper, the algorithm for the extended RCPSP is analyzed in experimental evaluation using real-world example problems with various size, resource complexity, tightness and so forth.

Keywords: maintenance management, scheduling, resource constrained project scheduling problem, genetic algorithms

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Authors: Funda Kul, İsmail Gür

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Pension scheme providers have to price mortality risk by accurate mortality forecasting method. There are many mortality-forecasting methods constructed and used in literature. The Lee-Carter model is the first model to consider stochastic improvement trends in life expectancy. It is still precisely used. Mortality forecasting is done by mortality index in the Lee-Carter model. It is assumed that mortality index fits ARIMA time series model. In this paper, we propose and use dynamic normal inverse gaussian distribution to modeling mortality indes in the Lee-Carter model. Using population mortality data for Italy, France, and Turkey, the model is forecasting capability is investigated, and a comparative analysis with other models is ensured by some well-known benchmarking criterions.

Keywords: mortality, forecasting, lee-carter model, normal inverse gaussian distribution

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Authors: S. Ghasemi, K. Khorasani

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In this paper, the problem of fault detection and isolation in the attitude control subsystem of spacecraft formation flying is considered. In order to design the fault detection method, an extended Kalman filter is utilized which is a nonlinear stochastic state estimation method. Three fault detection architectures, namely, centralized, decentralized, and semi-decentralized are designed based on the extended Kalman filters. Moreover, the residual generation and threshold selection techniques are proposed for these architectures.

Keywords: component, formation flight of satellites, extended Kalman filter, fault detection and isolation, actuator fault

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Authors: Johannes J. Schneider, Mathias S. Weyland, Peter Eggenberger Hotz, William D. Jamieson, Oliver Castell, Alessia Faggian, Rudolf M. Füchslin

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Agglomerations of polydisperse systems of spherical particles are created in computer simulations using a simplified stochastic-hydrodynamic model: Particles sink to the bottom of the cylinder, taking into account gravity reduced by the buoyant force, the Stokes friction force, the added mass effect, and random velocity changes. Two types of particles are considered, with one of them being able to create connections to neighboring particles of the same type, thus forming a network within the agglomeration at the bottom of a cylinder. Decreasing the fraction of these particles, a percolation transition occurs. The critical regime is determined by investigating the maximum cluster size and the percolation susceptibility.

Keywords: binary system, maximum cluster size, percolation, polydisperse

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Authors: Benjamin Sonnenreich

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Brittle fracture failure probability is best described using a stochastic approach which is based on the 'weakest link concept' and the connection between a microstructure and macroscopic fracture scale. A general theoretical and experimental framework is presented to predict the uniaxial strength distribution according to independent uniaxial test data. The framework takes as input the applied stresses, the geometry, the materials, the defect distributions and the relevant random variables from uniaxial test results and gives as output an overall failure probability that can be used to improve the reliability of practical designs. Additionally, the method facilitates comparisons of strength data from several sources, uniaxial tests, and sample geometries.

Keywords: brittle fracture, strength distribution, uniaxial, weakest link concept

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Authors: A. Bentabet, A. Aydin, N. Fenineche

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In this work, we try to determine the best analytical approximation of differential cross sections, used generally in Monte Carlo simulation, to study the electron/positron slowing down in solid targets in the energy range up to 10 keV. Actually, our comparative study was carried out on the angular distribution of the scattering angle, the elastic total and the first transport cross sections which are the essential quantities used generally in the electron/positron transport study by using both stochastic and deterministic methods. Indeed, the obtained results using the relativistic partial wave expansion method and the backscattering coefficient experimental data are used as criteria to evaluate the used model.

Keywords: differential cross-section, backscattering coefficient, Rutherford cross-section, Vicanek and Urbassek theory

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Authors: Mesias Alfeus, Kirsty Fitzhenry, Alessia Lederer

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The present paper provides empirical studies to estimate defaultable bonds in the South African financial market. The main goal is to estimate the unobservable factors affecting bond yields for South African major banks. The maximum likelihood approach is adopted for the estimation methodology. Extended Kalman filtering techniques are employed in order to tackle the situation that the factors cannot be observed directly. Multi-dimensional Cox-Ingersoll-Ross (CIR)-type factor models are considered. Results show that default risk increased sharply in the South African financial market during COVID-19 and the CIR model with jumps exhibits a better performance.

Keywords: default intensity, unobservable state variables, CIR, α-CIR, extended kalman filtering

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Authors: Tomáš Vyčítal

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In a simulation model of a railway system it is important, besides other crucial algorithms, to have correct behaviour of train overtaking in stochastic conditions. This problem is being addressed in many simulation tools focused on railway traffic, however these are not very human-realistic. The goal of this paper is to create a more human-realistic overtaking decision support system for the use in railway traffic simulations. A fuzzy system has been chosen for this task as fuzzy systems are well-suited for human-like decision making. The fuzzy system designed takes into account timetables, train positions, delays and buffer times as inputs and provides an instruction to overtake or not overtake.

Keywords: decision-making support, fuzzy systems, simulation, railway, transport

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Authors: Olufunke Olufunmilayo Ilemobayo, Barakat. O Abdulazeez

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Profit making among poultry egg farmers has been a challenge to efficient distribution of scarce farm resources over the years, due majorly to low capital base, inefficient management, technical inefficiency, economic inefficiency, thus poultry egg production has moved into an underperformed situation, characterised by low profit margin. Though previous studies focus mainly on broiler production and efficiency of its production, however, paucity of information exist in the areas of profit efficiency in the study area. Hence, determinants of profit efficiency among poultry egg farmers in Ondo State, Nigeria were investigated. A purposive sampling technique was used to obtain primary data from poultry egg farmers in Owo and Akure local government area of Ondo State, through a well-structured questionnaire. socio-economic characteristics such as age, gender, educational level, marital status, household size, access to credit, extension contact, other variables were input and output data like flock size, cost of feeder and drinker, cost of feed, cost of labour, cost of drugs and medications, cost of energy, price of crate of table egg, price of spent layers were variables used in the study. Data were analysed using descriptive statistics, budgeting analysis, and stochastic profit function/inefficiency model. Result of the descriptive statistics shows that 52 per cent of the poultry farmers were between 31-40 years, 62 per cent were male, 90 per cent had tertiary education, 66 per cent were primarily poultry farmers, 78 per cent were original poultry farm owners and 55 per cent had more than 5 years’ work experience. Descriptive statistics on cost and returns indicated that 64 per cent of the return were from sales of egg, while the remaining 36 per cent was from sales of spent layers. The cost of feeding take the highest proportion of 69 per cent of cost of production and cost of medication the lowest (7 per cent). A positive gross margin of N5, 518,869.76, net farm income of ₦ 5, 500.446.82 and net return on investment of 0.28 indicated poultry egg production is profitable. Equipment’s cost (22.757), feeding cost (18.3437), labour cost (136.698), flock size (16.209), drug and medication cost (4.509) were factors that affecting profit efficiency, while education (-2.3143), household size (-18.4291), access to credit (-16.027), and experience (-7.277) were determinant of profit efficiency. Education, household size, access to credit and experience in poultry production were the main determinants of profit efficiency of poultry egg production in Ondo State. Other factors that affect profit efficiency were cost of feeding, cost of labour, flock size, cost of drug and medication, they positively and significantly influenced profit efficiency in Ondo State, Nigeria.

Keywords: cost and returns, economic inefficiency, profit margin, technical inefficiency

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Authors: Mazura Mokhtar, Adibah Shuib, Daud Mohamad

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Portfolio optimization problem has received a lot of attention from both researchers and practitioners over the last six decades. This paper provides an overview of the current state of research in portfolio optimization with the support of mathematical programming techniques. On top of that, this paper also surveys the solution algorithms for solving portfolio optimization models classifying them according to their nature in heuristic and exact methods. To serve these purposes, 40 related articles appearing in the international journal from 2003 to 2013 have been gathered and analyzed. Based on the literature review, it has been observed that stochastic programming and goal programming constitute the highest number of mathematical programming techniques employed to tackle the portfolio optimization problem. It is hoped that the paper can meet the needs of researchers and practitioners for easy references of portfolio optimization.

Keywords: portfolio optimization, mathematical programming, multi-objective programming, solution approaches

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Authors: Alluri Swaroopa, L. V. N. Prasad

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Alone with fast urbanization in world, traffic control problem became a big issue in urban construction. Having an efficient and reliable traffic control system is crucial to macro-traffic control. Traffic signal is used to manage conflicting requirement by allocating different sets of mutually compatible traffic movement during distinct time interval. Many approaches have been made proposed to solve this discrete stochastic problem. Recognizing the need to minimize right-of-way impacts while efficiently handling the anticipated high traffic volumes, the proposed alternative system gives effective design. This model allows for increased traffic capacity and reduces delays by eliminating a step in maneuvering through the freeway interchange. The concept proposed in this paper involves construction of bridges and ramps at intersection of four roads to control the vehicular congestion and to prevent traffic breakdown.

Keywords: bridges, junctions, ramps, urban traffic control

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Authors: Ahmed Al-Sultan

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The check-in area of airport terminal is one of the busiest sections at airports at certain periods. The passengers are subjected to queues and delays during the check-in process. These delays and queues are due to constraints in the capacity of service facilities. In this project, the airport terminal is decomposed into several check-in areas. The airport check-in scheduling problem requires both a deterministic (integer programming) and stochastic (simulation) approach. Integer programming formulations are provided to minimize the total number of counters in each check-in area under the realistic constraint that counters for one and the same flight should be adjacent and the desired number of counters remaining in each area should be fixed during check-in operations. By using simulation, the airport system can be modeled to study the effects of various parameters such as number of passengers on a flight and check-in counter opening and closing time.

Keywords: airport terminal, integer programming, scheduling, simulation

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Authors: F. Erdal, E. Dogan, F. E. Uz

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In this study, firefly optimization based optimum design algorithm is presented for the grillage systems. Naming of the algorithm is derived from the fireflies, whose sense of movement is taken as a model in the development of the algorithm. Fireflies’ being unisex and attraction between each other constitute the basis of the algorithm. The design algorithm considers the displacement and strength constraints which are implemented from LRFD-AISC (Load and Resistance Factor Design-American Institute of Steel Construction). It selects the appropriate W (Wide Flange)-sections for the transverse and longitudinal beams of the grillage system among 272 discrete W-section designations given in LRFD-AISC so that the design limitations described in LRFD are satisfied and the weight of the system is confined to be minimal. Number of design examples is considered to demonstrate the efficiency of the algorithm presented.

Keywords: firefly algorithm, steel grillage systems, optimum design, stochastic search techniques

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Authors: Oulad Naoui Noureddine, Cherif ELAmine, Djehiche Abdelkader

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Water resources management includes several disciplines; the modeling of rainfall-runoff relationship is the most important discipline to prevent natural risks. There are several models to study rainfall-runoff relationship in watersheds. However, the majority of these models are not applicable in all basins of the world.  In this study, a new stochastic method called The Only Corresponding Competitor method (OCC) was used for the hydrological modeling of M’ZAB   Watershed (South East of Algeria) to adapt a few empirical models for any hydrological regime.  The results obtained allow to authorize a certain number of visions, in which it would be interesting to experiment with hydrological models that improve collectively or separately the data of a catchment by the OCC method.

Keywords: modelling, optimization, rainfall-runoff relationship, empirical model, OCC

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Authors: M. J. Shofa, A. Hidayatno, O. M. Armand

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MRP as a production control system is appropriate for the deterministic environment. Unfortunately, most production systems such as customer demands are stochastic. Demand-Driven MRP (DDMRP) is a new approach for inventory control system, and it deals with demand uncertainty. The objective of this paper is to compare the MRP and DDMRP work for a long lead time and uncertain demand in terms of on-hand inventory levels. The evaluation is conducted through a discrete event simulation using purchased part data from an automotive company. The result is MRP gives 50,759 pcs / day while DDMRP gives 34,835 pcs / day (reduce 32%), it means DDMRP is more effective inventory control than MRP in terms of on-hand inventory levels.

Keywords: Demand-Driven MRP, long lead time, MRP, uncertain demand

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Authors: Steffen Illig, Alexander S. Eggeman, Alessandro Troisi, Stephen G. Yeates, John E. Anthony, Henning Sirringhaus

Abstract:

Large-amplitude intermolecular vibrations in combination with complex shaped transfer integrals generate a thermally fluctuating energetic landscape. The resulting dynamic disorder and its intrinsic presence in organic semiconductors is one of the most fundamental differences to their inorganic counterparts. Dynamic disorder is believed to govern many of the unique electrical and optical properties of organic systems. However, the low energy nature of these vibrations makes it difficult to access them experimentally and because of this we still lack clear molecular design rules to control and reduce dynamic disorder. Applying a novel technique based on electron diffraction we encountered strong intermolecular, thermal vibrations in every single organic material we studied (14 up to date), indicating that a large degree of dynamic disorder is a universal phenomenon in organic crystals. In this paper a new molecular design strategy will be presented to avoid dynamic disorder. We found that small molecules that have their side chains attached to the long axis of their conjugated core have been found to be less likely to suffer from dynamic disorder effects. In particular, we demonstrate that 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothio-phene (C8-BTBT) and 2,9-di-decyl-dinaphtho-[2,3-b:20,30-f]-thieno-[3,2-b]-thiophene (C10DNTT) exhibit strongly reduced thermal vibrations in comparison to other molecules and relate their outstanding performance to their lower dynamic disorder. We rationalize the low degree of dynamic disorder in C8-BTBT and C10-DNTT with a better encapsulation of the conjugated cores in the crystal structure which helps reduce large amplitude thermal motions. The work presented in this paper provides a general strategy for the design of new classes of very high mobility organic semiconductors with low dynamic disorder.

Keywords: charge transport, C8-BTBT, C10-DNTT, dynamic disorder, organic semiconductors, thermal vibrations

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