Developing a multi-objective,multi-item inventory model and three algorithms for its solution

We develop a multi-objective model in a multi-product inventory system.The proposed model is a joint replenishment problem(JRP) that has two objective functions.The first one is minimization of total ordering and inventory holding costs,which is the same objective function as the classic JRP.To increase the applicability of the proposed model,we suppose that transportation cost is independent of time,is not a part of holding cost,and is calculated based on the maximum of stored inventory,as is the case in many real inventory problems.Thus,the second objective function is minimization of total transportation cost.To solve this problem three efficient algorithms are proposed.First,the RAND algorithm,called the best heuristic algorithm for solving the JRP,is modified to be applicable for the proposed problem.A multi-objective genetic algorithm(MOGA) is developed as the second algorithm to solve the problem.Finally,the model is solved by a new algorithm that is a combination of the RAND algorithm and MOGA.The performances of these algorithms are then compared with those of the previous approaches and with each other,and the findings imply their ability in finding Pareto optimal solutions to 3200 randomly produced problems.     

From Nonlinear to Fuzzy Approaches in Trajectory Tracking Control of Wheeled Mobile Robots

In this paper, two knowledge based controllers are proposed to overcome the difficulties of a computed torque nonlinear controller (NC) in perfect trajectory tracking of nonholonomic wheeled mobile robots (WMRs). First, the effects of different dynamic models developed in angular and Cartesian coordinate systems are fully examined on the persistent excitation condition and consequently on the trajectory tracking performance of WMRs. Using the dynamic model coordinated in the Cartesian frame as the base of the NC results in perfect compensation of large position off‐tracks and unbiased estimation of the plant's unknown parameters. However, using the WMR's dynamic model with rotation angles of driving wheels as the base of nonlinear and fuzzy controllers leads to accurate orientation tracking. Through replacing the proportional and differential terms of the NC by fuzzy functions, a fuzzy nonlinear controller (FNC) is generated. Due to the complicated dynamics of the WMR in which the center of mass does not coincide with the center of rotation, the expert knowledge of fuzzy controllers is extracted considering the rotation angles and rates of driving wheels as input variables. Fuzzy tuning of the NC results in a superior tracking performance against measurement noises, though the control torques are decreased and smoothed significantly. Second, a complete fuzzy controller (FC) is generated to make perfect tracking of the WMR's position and orientation. The local stability analysis of fuzzy controllers is examined considering the corresponding analytical structures as nonlinear controllers. The superior performances of the proposed fuzzy controllers compared to those of the NCs are evaluated through simulations.     

Mathematical Knowledge of Architecture in the Works of Kâshânî

Ghiyath al-D?n Jamsh?d Kashan? (al-Kash?) is the prominent mathematician and astronomer of ninth/fifteenth century of Islamic civilization who founded the scientific methodology in practical and theoretical knowledge of mathematics in architecture. He played a significant role in the interaction and concurrence among mathematicians and architects, by addressing the relation between architecture and mathematics in two areas of knowledge, theoretical and practical, the latter itself including theoretical practice and practical practice. This paper addresses the question of how Kashan?’s research has led to the foundation of the first theoretical basis for the application of mathematics in architecture in the form of easily practicable solutions for drawing, and measuring different types of ceilings, arches, vaults, domes, and ornaments, as well as estimating required materials. Further, we will discuss how his knowledge in mathematics and astronomy were utilized in management, design and construction of Samarkand observatory and its astronomical instruments. By exploring how the mathematical knowledge of a Persian scientist was utilized in architecture and craftsmanship, we will shed some light on the hidden layers of Kashan?’s architectural life, who until now has been considered only a mathematician and astronomer.     

Bronchoalveolar lavage fluid proteomic patterns of sulfur mustard-exposed patients

Sulfur mustard is an alkylating agent that reacts with ocular, respiratory, cutaneous, and bone marrow tissues. Main late respiratory complications are chronic obstructive pulmonary disease, bronchiectasis, asthma, and bronchiolitis obliterans. The aim of the present study was to identify differentially expressed proteins in bronchoalveolar lavage (BAL) fluid of control healthy and sulfur mustard-exposed lung disease patients. The BAL protein profile of ten healthy and 30 exposed patients with mild, moderate, and severe conditions (ten males in each group) were separated with 2-D SDS-PAGE and differentially expressed protein spots were successfully identified with MALDI TOF TOF MS. Among the differentially expressed proteins we observed a significant increase in vitamin D binding protein isoforms, haptoglobin isoforms, and fibrinogen especially in exposed moderate and severe lung diseases patients (p<0.01). Moreover, compared with healthy controls, significant decreases was noted in calcyphosine, surfactant protein A, and transthyretin in these patients (p<0.01). Apolipoprotein A1 was detected in all patients' BAL fluid but none of the healthy controls. Furthermore, S100 calcium-binding protein A8 was only detected in BAL fluid of moderate and severe groups. These findings will be useful to improve current methods of monitoring and helps to identify new therapeutic targets for treatment of this complicated illness.     

Nonlinear modeling of soil deformation modulus through LGP-based interpretation of pressuremeter test results

Soil deformation modulus is an essential parameter for the analysis of behavior of substructures. Despite its importance, little attention is paid to developing empirical models for predicting the deformation moduli obtained from the field tests. To cope with this issue, this paper presents the development of a new prediction model for the pressuremeter soil deformation modulus utilizing a linear genetic programming (LGP) methodology. The LGP model relates the soil secant modulus obtained from the pressuremeter tests to the soil index properties. The best model was selected after developing and controlling several models with different combinations of the influencing parameters. The experimental database used for developing the models was established upon several pressuremeter tests conducted on different soil types at depths of 3–40 m. To verify the applicability of the derived model, it was employed to estimate the soil moduli of portions of test results that were not included in the analysis. Further, the generalization capability of the model was verified via several statistical criteria. The sensitivity of the soil deformation modulus to the influencing variables was examined and discussed. Moisture content and soil dry unit weight were found to be efficient representatives of the initial state and consolidation history of the soil for determining its deformation modulus. The results indicate that the LGP approach accurately characterizes the soil deformation modulus leading to a very good prediction performance. The correlation coefficients between the experimental and predicted soil modulus values are equal to 0.908 and 0.901 for the calibration and testing data sets, respectively.     

Low cost method for hot embossing of microstructures on PMMA by SU-8 masters

Polymer based microfabrication technologies are used extremely in Bio-MEMS, especially in Microfluidic devices in recent years. In this paper, a novel method for fabrication of microstructures on a polymeric material using hot embossing lithography process is presented. The proposed method involves usage of low cost materials and procedure with respect to previous methods and can be processed in a short time. The master is made from SU-8 on an inexpensive glass substrate which is patterned by standard lithography. The embossing pressure can be increased in our master as the glass substrate used in this paper is more robust than Silicon. Master robustness and SU-8 to glass adhesion is optimized by some substrate pretreatments and SU-8 baking time and temperatures. Microchannels are replicated on a Polymethylmetacrylate (PMMA) stamp which is a plexiglass sheet with thickness of 1 mm. Significant embossing parameters including temperature, pressure and time are discussed and optimum values are determined. Microchannels are imprinted by depth of 50 μm and minimum width of 15 μm and aspect ratio more than 3. The microchannels are sealed by a PMMA cap using thermal annealing bonding.     

Design and analysis of genetic fuzzy systems for intrusion detection in computer networks

The capability of fuzzy systems to solve different kinds of problems has been demonstrated in several previous investigations. Genetic fuzzy systems (GFSs) hybridize the approximate reasoning method of fuzzy systems with the learning capability of evolutionary algorithms. The objective of this paper is to design and analysis of various kinds of genetic fuzzy systems to deal with intrusion detection problem as a new real-world application area which is not previously tackled with GFSs. The resulted intrusion detection system would be capable of detecting normal and abnormal behaviors in computer networks. We have presented three kinds of genetic fuzzy systems based on Michigan, Pittsburgh and iterative rule learning (IRL) approaches to deal with intrusion detection as a high-dimensional classification problem. Experiments were performed with DARPA data sets which have information on computer networks, during normal and intrusive behaviors. The paper presents some results and compares the performance of different generated fuzzy rule sets in detecting intrusion in a computer network according to three different types of genetic fuzzy systems.     

Statistical inference for the lifetime performance index based on generalised order statistics from exponential distribution

In manufacturing industries, the lifetime of an item is usually characterised by a random variable X and considered to be satisfactory if X exceeds a given lower lifetime limit L. The probability of a satisfactory item is then η_L := P(X ≥ L), called conforming rate. In industrial companies, however, the lifetime performance index, proposed by Montgomery and denoted by C_L, is widely used as a process capability index instead of the conforming rate. Assuming a parametric model for the random variable X, we show that there is a connection between the conforming rate and the lifetime performance index. Consequently, the statistical inferences about η_L and C_L are equivalent. Hence, we restrict ourselves to statistical inference for C_L based on generalised order statistics, which contains several ordered data models such as usual order statistics, progressively Type-II censored data and records. Various point and interval estimators for the parameter C_L are obtained and optimal critical regions for the hypothesis testing problems concerning C_L are proposed. Finally, two real data-sets on the lifetimes of insulating fluid and ball bearings, due to Nelson (1982) Nelson, W. (1982), Applied Life Data Analysis, New York: John Wiley &; Sons.[Crossref] , [Google Scholar] and Caroni (2002) Caroni, C. (2002), ‘The correct “ball bearings” data’, Lifetime Data Analysis, 8, 395–399.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar], respectively, and a simulated sample are analysed.     

A survey: hybrid evolutionary algorithms for cluster analysis

Clustering is a popular data analysis and data mining technique. It is the unsupervised classification of patterns into groups. Many algorithms for large data sets have been proposed in the literature using different techniques. However, conventional algorithms have some shortcomings such as slowness of the convergence, sensitive to initial value and preset classed in large scale data set etc. and they still require much investigation to improve performance and efficiency. Over the last decade, clustering with ant-based and swarm-based algorithms are emerging as an alternative to more traditional clustering techniques. Many complex optimization problems still exist, and it is often very difficult to obtain the desired result with one of these algorithms alone. Thus, robust and flexible techniques of optimization are needed to generate good results for clustering data. Some algorithms that imitate certain natural principles, known as evolutionary algorithms have been used in a wide variety of real-world applications. Recently, much research has been proposed using hybrid evolutionary algorithms to solve the clustering problem. This paper provides a survey of hybrid evolutionary algorithms for cluster analysis.     

Simultaneous coordination of order quantity and reorder point in a two-stage supply chain

In this paper, we investigate the simultaneous coordination of order quantity and reorder point in a two-stage supply chain (SC). While coordination of order quantity has received much attention in the supply chain management literature, coordination of the reorder point has been less-studied. The retailer's reorder point has a direct impact on product availability and customer service level (CSL) and therefore has a great impact on SC profitability. Our proposed model adopts a two-stage SC with stochastic demand and lead times over multiple periods. The proposed coordination model assures global optimization of order quantity–reorder point decisions. Using a pricing scheme with a discount factor, we extract conditions in which both downstream and upstream members have sufficient motivation to participate in the coordination scheme. Numerical experiments demonstrate that the proposed model can achieve channel coordination. Results of the modeling and analyses show that coordination of both reorder point and order quantity can lead to increased SC profitability as well as CSL improvement.