INVENTORY CONTROL SYSTEM : Optimization of production system and reliability

The main idea of this research to solve the problem of inventory management for the paper industry SPM PVT limited. The aim of this research was to find a methodology by which the inventory of raw material could be kept at minimum level by means of buffer stock level.The main objective then lies in finding the minimum level of buffer stock according to daily consumption of raw material, finding the Economic Order Quantity (EOQ) reorders point and how much order will be placed in a year to control the shortage of raw material.In this project, we discuss continuous review model (Deterministic EOQ models) that includes the probabilistic demand directly in the formulation. According to the formula, we see the reorder point and the order up to model. The problem was tackled mathematically as well as simulation modeling was used where mathematically tractable solution was not possible.The simulation modeling was done by Awesim software for developing the simulation network. This simulation network has the ability to predict the buffer stock level based on variable consumption of raw material and lead-time. The data collection for this simulation network is taken from the industrial engineering personnel and the departmental studies of the concerned factory. At the end, we find the optimum level of order quantity, reorder point and order days.

Computational study of the step size parameter of the subgradient optimization method

The subgradient optimization method is a simple and flexible linear programming iterative algorithm. It is much simpler than Newton's method and can be applied to a wider variety of problems. It also converges when the objective function is non-differentiable. Since an efficient algorithm will not only produce a good solution but also take less computing time, we always prefer a simpler algorithm with high quality. In this study a series of step size parameters in the subgradient equation is studied. The performance is compared for a general piecewise function and a specific p-median problem. We examine how the quality of solution changes by setting five forms of step size parameter.

Heuristic optimization of the p-median problem and population re-distribution

This thesis contributes to the heuristic optimization of the p-median problem and Swedish population redistribution.   The p-median model is the most representative model in the location analysis. When facilities are located to a population geographically distributed in Q demand points, the p-median model systematically considers all the demand points such that each demand point will have an effect on the decision of the location. However, a series of questions arise. How do we measure the distances? Does the number of facilities to be located have a strong impact on the result? What scale of the network is suitable? How good is our solution? We have scrutinized a lot of issues like those. The reason why we are interested in those questions is that there are a lot of uncertainties in the solutions. We cannot guarantee our solution is good enough for making decisions. The technique of heuristic optimization is formulated in the thesis.   Swedish population redistribution is examined by a spatio-temporal covariance model. A descriptive analysis is not always enough to describe the moving effects from the neighbouring population. A correlation or a covariance analysis is more explicit to show the tendencies. Similarly, the optimization technique of the parameter estimation is required and is executed in the frame of statistical modeling. 

Optical Characterization and Optimization of Display Components : Some Applications to Liquid-Crystal-Based and Electrochromics-Based Devices

This dissertation is focused on theoretical and experimental studies of optical properties of materials and multilayer structures composing liquid crystal displays (LCDs) and electrochromic (EC) devices. By applying spectroscopic ellipsometry, we have determined the optical constants of thin films of electrochromic tungsten oxide (WOx) and nickel oxide (NiOy), the films’ thickness and roughness. These films, which were obtained at spattering conditions possess high transmittance that is important for achieving good visibility and high contrast in an EC device. Another application of the general spectroscopic ellipsometry relates to the study of a photo-alignment layer of a mixture of azo-dyes SD-1 and SDA-2. We have found the optical constants of this mixture before and after illuminating it by polarized UV light. The results obtained confirm the diffusion model to explain the formation of the photo-induced order in azo-dye films. We have developed new techniques for fast characterization of twisted nematic LC cells in transmissive and reflective modes. Our techniques are based on the characteristics functions that we have introduced for determination of parameters of non-uniform birefringent media. These characteristic functions are found by simple procedures and can be utilised for simultaneous determination of retardation, its wavelength dispersion, and twist angle, as well as for solving associated optimization problems. Cholesteric LCD that possesses some unique properties, such as bistability and good selective scattering, however, has a disadvantage – relatively high driving voltage (tens of volts). The way we propose to reduce the driving voltage consists of applying a stack of thin (~1µm) LC layers. We have studied the ability of a layer of a surface stabilized ferroelectric liquid crystal coupled with several retardation plates for birefringent color generation. We have demonstrated that in order to accomplish good color characteristics and high brightness of the display, one or two retardation plates are sufficient.

Modeling and forecasting long-term natural gas (NG) consumption in Iran, using particle swarm optimization (PSO)

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.

Optimization of cable cycles : a trade-off between reliability and cost

This paper elaborates the routing of cable cycle through available routes in a building in order to link a set of devices, in a most reasonable way. Despite of the similarities to other NP-hard routing problems, the only goal is not only to minimize the cost (length of the cycle) but also to increase the reliability of the path (in case of a cable cut) which is assessed by a risk factor. Since there is often a trade-off between the risk and length factors, a criterion for ranking candidates and deciding the most reasonable solution is defined. A set of techniques is proposed to perform an efficient and exact search among candidates. A novel graph is introduced to reduce the search-space, and navigate the search toward feasible and desirable solutions. Moreover, admissible heuristic length estimation helps to early detection of partial cycles which lead to unreasonable solutions. The results show that the method provides solutions which are both technically and financially reasonable. Furthermore, it is proved that the proposed techniques are very efficient in reducing the computational time of the search to a reasonable amount.

Convergence analysis of sampling-based decomposition methods for risk-averse multistage stochastic convex programs

We consider a class of sampling-based decomposition methods to solve risk-averse multistage stochastic convex programs. We prove a formula for the computation of the cuts necessary to build the outer linearizations of the recourse functions. This formula can be used to obtain an efficient implementation of Stochastic Dual Dynamic Programming applied to convex nonlinear problems. We prove the almost sure convergence of these decomposition methods when the relatively complete recourse assumption holds. We also prove the almost sure convergence of these algorithms when applied to risk-averse multistage stochastic linear programs that do not satisfy the relatively complete recourse assumption. The analysis is first done assuming the underlying stochastic process is interstage independent and discrete, with a finite set of possible realizations at each stage. We then indicate two ways of extending the methods and convergence analysis to the case when the process is interstage dependent.

Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures

We consider risk-averse convex stochastic programs expressed in terms of extended polyhedral risk measures. We derive computable con dence intervals on the optimal value of such stochastic programs using the Robust Stochastic Approximation and the Stochastic Mirror Descent (SMD) algorithms. When the objective functions are uniformly convex, we also propose a multistep extension of the Stochastic Mirror Descent algorithm and obtain con dence intervals on both the optimal values and optimal solutions. Numerical simulations show that our con dence intervals are much less conservative and are quicker to compute than previously obtained con dence intervals for SMD and that the multistep Stochastic Mirror Descent algorithm can obtain a good approximate solution much quicker than its nonmultistep counterpart. Our con dence intervals are also more reliable than asymptotic con dence intervals when the sample size is not much larger than the problem size.

Optimization of fibrolytic enzyme production by Aspergillus japonicus C03 with potential application in ruminant feed and their effects on tropical forages hydrolysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Optimal Conditions for Biomass and Recombinant Glycerol Kinase Production Using the Yeast Pichia pastoris

The extracellular glycerol kinase gene from Saccharomyces cerevisiae (GUT]) was cloned into the expression vector pPICZ alpha. A and integrated into the genome of the methylotrophic yeast Pichia pastoris X-33. The presence of the GUT1 insert was confirmed by PCR analysis. Four clones were selected and the functionality of the recombinant enzyme was assayed. Among the tested clones, one exhibited glycerol kinase activity of 0.32 U/mL, with specific activity of 0.025 U/mg of protein. A medium optimized for maximum biomass production by recombinant Pichia pastoris in shaker cultures was initially explored, using 2.31 % (by volume) glycerol as the carbon source. Optimization was carried out by response surface methodology (RSM). In preliminary experiments, following a Plackett-Burman design, glycerol volume fraction (phi(Gly)) and growth time (t) were selected as the most important factors in biomass production. Therefore, subsequent experiments, carried out to optimize biomass production, followed a central composite rotatable design as a function of phi(Gly) and time. Glycerol volume fraction proved to have a significant positive linear effect on biomass production. Also, time was a significant factor (at linear positive and quadratic levels) in biomass production. Experimental data were well fitted by a convex surface representing a second order polynomial model, in which biomass is a function of both factors (R(2)=0.946). Yield and specific activity of glycerol kinase were mainly affected by the additions of glycerol and methanol to the medium. The optimized medium composition for enzyme production was: 1 % yeast extract, 1 % peptone, 100 mM potassium phosphate buffer, pH=6.0, 1.34 % yeast nitrogen base (YNB), 4.10(-5) % biotin, 1 %, methanol and 1 %, glycerol, reaching 0.89 U/mL of glycerol kinase activity and 14.55 g/L of total protein in the medium after 48 h of growth.

Cyclodextrin glycosyltransferase from Bacillus licheniformis: Optimization of production and its properties

Cyclodextrin glycosyltransferase (EC 2.4.1.19) is an enzyme that produces cyclodextrins from starch via an intramolecular transglycosylation reaction. An alkalophilic Bacillus strain, isolated from cassava peels, was identified as Bacillus licheniformis. CGTase production by this strain was better when potato starch was used as carbon source, followed by cassava starch and amylopectin. Glucose and amylose, on the other hand, acted as synthesis repressors. When the cultivation was supplemented with sodium ions and had the pH adjusted between 6.0 and 9.0, the microorganism maintained the growth and enzyme production capacity. This data is interesting because it contradicts the concept that alkalophilic microorganisms do not grow in this pH range. After ultrafiltration-centrifugation, one protein of 85.2 kDa with CGTase activity was isolated. This protein was identified in plates with starch and phenolphthalein. Determination of the optimum temperature showed higher activities at 25 degrees C and 55 degrees C, indicating the possible presence of more than one CGTase in the culture filtrate. Km and Vmax values were 1.77 mg/mL and 0.0263 U/mg protein, respectively, using potato starch as substrate.

Multivariate optimization and validation of an analytical methodology by RP-HPLC for the determination of losartan potassium in capsules

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Stability and convergence analysis of a neural model applied in nonlinear systems optimization

A neural model for solving nonlinear optimization problems is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology.