In this paper, we explore a nonlinear MATHEMATICAL MODEL to study the spread of asthma due to inhaled pollutants from industry as well as tobacco smoke from smokers in a variable size population. The MODEL is analyzed using stability theory of differential equations and computer simulation. It is shown that with an increase in the level of air pollutants concentration, the asthmatic (diseased) population increases. It is also shown that along with pollutants present in the environment, smoking (active or passive) also helps in the spread of asthma. Moreover, with the increase in the rate of interaction between susceptibles and smokers, the persistence of the spread of asthma is higher. A numerical study of the MODEL is also performed to see the role of certain key parameters on the spread of asthma and to support the analytical results.

Truck-Shovel fleet, as the most common transportation system in open-pit mines, has a significant part of mining costs, for which an optimal management can lead to a substantial cost reduction. Among the available dispatch MATHEMATICAL MODELs, a multi-stage approach is well-suited for allocating trucks to the respected shovels in a dynamic dispatching program. However, with this kind of MODELing, sequencing the allocated trucks is not possible, though it is important to find out the best solution to get the minimum cost. To comply with the shortcoming of the traditional MODEL, in this work, a new hybrid MODEL is developed and applied to the Sungon Copper Mine in Iran, in which, for each truck, an allocation matrix is considered as the input to the genetic algorithm implemented to determine the best solution. According to the results obtained, the optimal sequencing of the trucks can result in a significant (31%) cost reduction in a shift.

Design of sewer networks involves many constraints and technical criteria. These issues make the problem complicated and the feasible alternatives difficult to achieve. Besides, the methods of handling these constraints play a major role in optimization approaches for the least cost design. In this work an integrated analysis-design MODEL is developed thereby all constraints are automatically satisfied during designing. A normal design alternative is defined herein which is a vector of values between zero and one. This vector represents the sewers diameters, slopes and pump station locations. The normal alternative is then decoded to real design parameters based on the hydraulics rules and the problem constraints. Using the proposed MODEL every normal alternative is feasible and consequently the input design parameters are totally unconditional. At the end the abilities of the MODEL is shown introducing and solving a relatively large sewer network. Furthermore, to demonstrate the capability and easiness of integrating the MODEL with optimization tools, a Simulated Annealing (SA) algorithm is also applied to obtain the least cost design of the example. The results show that the optimum design and the rate of optimization convergence are significantly improved using the proposed method.

We can call "EFQM excellence MODEL" as a transition from the great number of different MODELs and approaches to unity in context of organizational improvement and excellence. Doing self-assessment to find areas for improvement (AFIs) is the most important application of this MODEL. Those organizations which are on lower level of total quality management will encounter lots of AFIs. So finding key problems is an important challenge for them. Reviewing the literature shows that there is no specific and systematic MODEL to solve this challenge.We intended to introduce a MATHEMATICAL screening MODEL in order to help organizations to find their key problems. The proposed MODEL was designed in fuzzy context, because decision making situation of most of the organizations is in vague and ambiguity.

A MATHEMATICAL MODEL to break the life cycle of anopheles mosquito Is derived, aimed at controlling or eradicating mosquito by introduction of natural enemy “copepods” (an organism that eats up mosquito at larva stage). Thereby reducing the menace of malaria parasite in our societies. The MODEL equations are derived using the MODEL parameters. The stability of the free equilibrium states is analyzed using the idea of Beltrami and Diekmann. From the stability analysis we observe that RO<1 which implies that the MODEL free equilibrium state is locally asymptotically stable. Hence the number of larva that would transform to pupa is almost zero and that means the life cycle can be broken at the larva stage with the introduction of natural enemy.

Drug design is generally achieved through trial and error methods, which is a time and resource consuming process and novel methods are needed to improve it. MATHEMATICAL MODELling is one of the possible solutions to speed up this process. In this study, we have presented Box-Jenkins MODEL, to predict the vasorelaxant activity (pEC50) of a set of benzopyrane compounds. We used the HyperChem software to extract the molecular features of these compounds (51 molecules); then these features were used to generate the Box-Jenkins MODEL. The dataset was divided into three groups: 37 for training, 9 for prediction and 5 for validation. The absolute relative deviation of the predicted values was 4.8% and 4.1% for validation and predicton sets, respectively. The correlation coefficient between the predicted points and the experimental values was 0.9657, revealing that the proposed MODEL is capable of representing pEC50 of benzopyranes and could be used to speed up drug discovery processes.

This paper investigates the problem of selecting and scheduling a set of projects among available projects. Each project consists of several tasks and to perform each one some resource is required. The objective is to maximize total benefit. The paper constructs a MATHEMATICAL formulation in form of mixed integer linear programming MODEL. Three effective metaheuristics in form of the imperialist competitive algorithm, simulated annealing and genetic algorithm are developed to solve such a hard problem. The proposed algorithms employ advanced operators. The performance of the proposed algorithms is numerically evaluated. The results show the high performance of the imperialist competitive algorithm outperforms the other algorithms.

This paper is in search of designing the cellular manufacturing systems (CMSs) under dynamic and flexible environment. CM is proper for small-to-medium lot production environment that helps the companies to produce variable kind of productions with at least scraps. The most important benefits of CM are decline in material handling, reduction in work-in-process, reduction in set-up time, increment in flexibility, improved quality, and shorter lead time. In this research A multi-objective mixed integer MODEL is presented that considers some real-world critical conditions same as costs of multi-period cell formation and production planning, human resource assignment to cells and balancing workload of cells. This MODEL groups the parts and machines concurrently with labor assignment This study aims to 1) minimize various costs including reassignment cost of human resource, the batch inter-cell material handling cost, constant and variable cost of machines, relocation and purchase cost of machines, 2) minimize cell load variation and 3) maximize utilization rate of human resource. The MODEL is complicate, so it is verified with Lingo 8.0. Soft ware. Since particle swarm optimization approach less than many other metaheuristic approaches have been applied to solve multi-objective CMS problems so far, we utilize this method to solve our MODEL. The results are presented at the last part.

Cell formation problem in Cellular Manufacturing System (CMS) design has derived the attention of researchers for more than three decades. However, use of sequence data for cell formation has been the least investigated area. Sequence data provides valuable information about the flow patterns of various jobs in a manufacturing system. This paper presents a new MATHEMATICAL MODEL to solve a cell formation problem based on sequence data in CMS. The objective is to minimize the total costs of inter and intra-cell movements. This MODEL dependson the attitude of the decision maker towards the minimum utilization level of each cell in sucha way that the part-machine grouping can be changed significantly. A number of examples from the literature are solved by the LINGO software package to validate and verify the proposed MODEL. Finally, computational results are reported and analyzed.