A review of the statistical foundation of a class of probabilistic planning models

There exists a large group of probabilistic managerial planning models whose statistical problem consists of deriving the stochastic characteristics of a performance criterion defined as a function of several other random variables. This paper provides a unified statistical framework for such models by blending together various concepts and techniques in distribution theory and random-variate generation.     

A Realistic Approach for Modeling Stochastic Lead Time Distributions

This paper presents some techniques for the realistic treatment of stochastic lead-time-demands in inventory models. First, we suggest the use of the first four moments to describe the diversified distribution forms of the lead times and the daily demands; and formulas for deriving a lead-time-demand's first four moments are presented. Next, we demonstrate the use of a lead-time-demand's first four moments in conjunction with the Johnson et al.'s tables to obtain various probability estimates. Thirdly, we discuss the use of the versatile four-parameter Schmeiser-Deutsch curves to fit a lead-time-demand distribution. The computational advantages of using the fitted Schmeiser-Deutsch curves in solving inventory models are then illustrated.     

Energy consumption modeling analysis and environmental impact assessment of model house in Thessaloniki—Greece

The purpose of this study is to evaluate the annual energy consumption of an existing house in the city of Thessaloniki, Greece. The software tool HOT2000 was employed for this purpose. This work involves the prediction of the annual energy cost in the monetary units of euro. Hence, the estimated outputs are compared to the actual values retrieved from the annual utility bills in order to check the accuracy of the estimated results with the real values during the year. In this work, life cycle analysis of the fuel used is included and the environmental impact assessment is being calculated. The emission of the electricity production are all recorded and used in the analysis. It has been shown that climatic conditions and the type of building materials play a crucial role in energy savings.     

Bi-linear adaptive estimation of Fuzzy Cognitive Networks

Fuzzy Cognitive Networks (FCNs) have been introduced by the authors as an operational extension of Fuzzy Cognitive Maps (FCMs), initially introduced by Kosko to model complex behavioral systems in various scientific areas. FCNs rely on the admission that the underlying cognitive graph reaches a certain equilibrium point after an initial perturbation. Weight conditions for reaching equilibrium points have been recently derived in [54] along with an algorithm for weight estimation. In this paper, the conditions are extended to take into account not only the weights of the map but also the inclination parameters of the involved sigmoid functions, increasing the structural flexibility of the network. This in turn gives rise to the development of a new adaptive bilinear weight and sigmoid parameter estimation algorithm, which employs appropriate weight projection criteria to assure that the equilibrium is always achieved.     

New maximum power point tracker for PV arrays using fuzzy controller in close cooperation with fuzzy cognitive networks

The studies on the photovoltaic (PV) generation are extensively increasing, since it is considered as an essentially inexhaustible and broadly available energy resource. However, the output power induced in the photovoltaic modules depends on solar radiation and temperature of the solar cells. Therefore, to maximize the efficiency of the renewable energy system, it is necessary to track the maximum power point of the PV array. In this paper, a maximum power point tracker using fuzzy set theory is presented to improve energy conversion efficiency. A new method is proposed, by using a fuzzy cognitive network, which is in close cooperation with the presented fuzzy controller. The new method gives a very good maximum power operation of any PV array under different conditions such as changing insolation and temperature. The simulation studies show the effectiveness of the proposed algorithm.     

A Cost-Oriented Approach to Stochastic Line Balancing1

This paper presents a new heuristic for balancing paced assembly lines involving tasks which exhibit significant time variations. Labor and incompletion costs are explicitly considered in grouping tasks into work assignments to achieve a near minimum cost line design. The procedure is computationally attractive because its calculations are comparably efficient to those of heuristics which consider task times deterministically.     

A Total Operating Cost Model for Paced Lines with Stochastic Task Times

This paper presents a method for evaluating the often neglected component, the expected incompletion cost, of the operating cost of a paced line with stochastic task times. This incompletion cost is developed through a three stage process. First presented is a procedure for generating all possible combinations of incompleted tasks. Next is an evaluation of the probabilities and costs of their occurrence. Included are not only a mechanism for generating only those incompletion combinations with a significant probability of occurring, but also various techniques for reducing computer storage requirements and computation time. Finally, these make possible an evaluation of the operating cost of paced lines.