Each company struggles with the same question: How can I provide – at the lowest possible costs and with an acceptable delivery time – products or services that add maximum value for my customers? Firms must develop strategic objectives which result in a competitive advantage in the market place. There are many different methods which address this problem: Lean (the value adding organization), Six Sigma (the perfect organization), TOC (the unlimited organization), TPM (the smooth organization), RCM (the reliable organization) and QRM (the cellular organization). In addition, combinations of these methods exist, like Lean Six Sigma (value adding and perfect organization) and World Class (value adding and perfect and smooth organization). The aim of this work is to present establishments of the basic model of WCM for the logistics system in the automotive industry in order to improve the work standards. The result of this research was to develop principles on strategic objectives, performance measurement systems and performance measurement system linkages for improved organizational coordination.
In the present study, efforts have been made to optimize the three physical processvariables viz; pH, temperature and agitation speed for enhanced polyhydroxybutyrate (PHB) production in batch cultivation by Alcaligenes sp. which serves as precursor for bioplastic (PHB) production. Strain election was done by viable staining method using nile blue A dye. Agro-industrial by products; ane molasses and urea were used as carbon and nitrogen source for PHB production. Optimization of physical process variables was done by central composite rotatable design (CCRD) using design expert (DX 8.0.6) software.Shake flask cultivation performed under optimum physical ondition viz; 34.5 C temperature, 6.54 pH and agitation speed of 3.13 Hz, gave PHB mass fraction yield of 76.80% ondry molasses substrate and showed 98.0% resemblance with the predicted percentage yield of 77.78%. Batch cultivation further performed in 7.5 L lab scale bioreactor (working volume: 5.6 L) under optimized condition gave maximum cell biomass of 11 0.5 g L1 with a PHB content of 8.8 0.4 g L1 after 48.0 h of fermentation. Scale up study on bioreactor gave maximum PHB yield (YP/x) and productivity of 0.78 and 0.19 g L1 h, which are higher than previous reports under similar condition. Characterization of PHB wasdone by FTIR.
The increasing development of large-scale offshore wind farms around the world has caused many new technical and economic challenges to emerge. The capital cost of the electrical network that supports a large offshore wind farm constitutes a significant proportion of the total cost of the wind farm. Thus, finding the optimal design of this electrical network is an important task, a task that is addressed in this paper. A cost model has been developed that includes a more realistic treatment of the cost of transformers, transformer substations, and cables. These improvements make this cost model more detailed than others that are currently in use. A novel solution algorithm is used. This algorithm is based on an improved genetic algorithm and includes a specific algorithm that considers different cable cross sections when designing the radial arrays. The proposed approach is tested with a large offshore wind farm; this testing has shown that the proposed algorithm produces valid optimal electrical network designs.
A crucial step in the modeling of a system is to determine the values of the parameters to use in the model. In this paper we assume that we have a set of measurements collected from an operational system, and that an appropriate model of the system (e.g., based on queueing theory) has been developed Not infrequently proper values for certain parameters of this model may be difficult to estimate from available data (because the corresponding parameters have unclear physical meaning or because they cannot be directly obtained from available measurements, etc.). Hence, we need a technique to determine the missing parameter values, i.e., to calibrate the model. As an alternative to unscalable “brute force” technique, we propose to view model calibration as a nonlinear optimization problem with constraints. The resulting method is conceptually simple and easy to implement. Our contribution is twofold. First, we propose improved definitions of the “objective function” to quantify the “distance” between performance indices produced by the model and the values obtained from measurements. Second, we develop a customized derivative-free optimization (DFO) technique hose original feature is the ability to allow temporary constraint violations. This technique allows us to solve this optimization problem accurately, thereby providing the “right” parameter values. We illustrate
our method using two simple real-life case studies.
In this paper we propose some models for solving optimization problems which arise in finance and insurance. First the general framework for Mean-Risk models is introduced. Then several approaches for multiobjective programming, such as Mean-Value-at-Risk and Mean-Conditional Value-at-Risk are used for building the model Mean-Value-at-Risk-Conditional Value-at-Risk using both Value-at-Risk and Conditional Value-at-Risk simultaneously for risk assessment. A two stage portfolio optimization model is developed, using Value-at-Risk and also Conditional Value-at-Risk measures in two stages separately.
For the design of magnetic shields for induction heating, it is useful to analyze not only the magnetic field reduction, but also the temperature behaviour of the shield. The latter is heated by its electromagnetic losses and by thermal radiation from the workpiece. A coupled thermal-electromagnetic axisym metric finite element model is used to study the temperature of a shield for an axisymmetric induction heater, highlighting the effect of the radius, height, thickness and material of the shield on its temperature and magnetic shielding factor. Also the effect of the frequency and the workpiece dimensions is investigated. The model is validated by measuring the magnetic induction, the induced currents in the shield and the temperature of the shield on the experimental setup. The temperature is unacceptably high for shields close to the excitation coil, especially if the shield height is lower than the workpiece height. Although the study is carried out for one specific induction heater geometry, the paper indicates the effect on the shield temperature of parameters such as geometry, material and frequency so that the results are useful also for other induction heating configurations .
Distribution networks will experience a deep mutation concerning their planning and operation rules due to the expected increase of distributed generation (DG) interconnection to the grid. Indeed, the opening of the electricity market or the growing global concern for environmental issues will lead to a massive development of DGs. Yet, a too large amount of DGs could raise technical problems on distribution networks which have not been planned to operate with bi-directional power flow. The existing solutions to solve marginal DG connections could be no longer relevant. The distribution network definitely has to evolve towards a smarter and more flexible network. Two possible ways to reach this goal are through new architectures and developing intelligent systems. This paper focuses on new architectures and operating modes. The traditional radial distribution network could accept more DGs by introducing appropriately specific loops. A new hybrid structure enabling the coexistence of the radial and meshed operation is proposed. It is equipped with autonomous circuit-breakers and automated switches that improve its reliability. A heuristic algorithm is also proposed to build this new architecture while ensuring the equality of consumers with respect to the continuity of service and while minimizing the global cost.
Decentralized electricity generation by renewable energy sources offer greater security of supply for consumers while respecting the environment. But the random nature of these sources requires us to develop sizing rules and use these systems to exploit them. This paper proposes an integrated PV/wind hybrid system optimization model, which utilizes the iterative optimization technique following the Deficiency of Power Supply Probability (DPSP), the Relative Excess Power Generated (REPG), the Total Net Present Cost (TNPC), the Total Annualized Cost (TAC) and Break-Even Distance Analysis (BEDA) for power reliability and system costs. The flow chart of the hybrid optimal sizing model is also illustrated. With this merged model, the optimal size of PV/wind hybrid energy conversion system using battery bank can be performed technically and economically according to the system reliability requirements. Additionally, a sensitivity analysis was carried out in order to appreciate the most important parameters influencing the economic performances of the hybrid system. A case study is conducted to analyze one hybrid project, which is designed to supply small residential household situated in the area of the Center for Renewable Energy Development (CDER) localized in Bouzare´ah, Algeria (36480N, 310E, 345 m).
The aim of this work was to optimize the ball mill based refining process of chocolate, in terms of refining time and energy consumption. Experiments were planned following a central composite design (CCD), considering refining time (rt) and agitator shaft speed (as) as factors. The experimental variables measured were chosen from the main characteristics that describe unmoulded chocolate. A complete second-order model was fitted to the experimental data. The most significant coefficients were that of energy consumption, iron content and particle size. Optimization consists in a bound minimization of refining time using the desirability function. Before experiments, working conditions were 70 rpm for as and 55 min for rt. The optimum conditions calculated by optimization were as follows: 58 rpm for as and 38.5 min for rt. The new working conditions identified for the ball mill considered enabled to rise output from 109 kg/h to 156 kg/h, with a 43% increase in productivity. A control experiment carried out in the optimized conditions to corroborate the results obtained, confirmed calculated expectations of response variables.
Due to recent developments in traceability systems, it is now possible to exchange significant amounts of data through food supply chains. Farming practices applied by cocoa farmers at the beginning of the chocolate supply chain strongly influence several quality parameters of the finished chocolate. However, information regarding these practices does not normally reach the chocolate manufacturer. As a consequence, many specifications of the raw material cannot be taken into consideration in the operational decision making processes related to chocolate production. In recent years many studies have been investigating the influence of certain farming practices on cocoa beans and the subsequent chocolate quality parameters. However, no comprehensive analysis of the process variables in the chain and their effects on the quality can be found. In this paper we review and classify the available literature on the topic in terms of process variables throughout the chain, and their effects on quality and flavour aspects of cocoa beans and the eventual chocolate product. After analyzing the literature, we are able to identify potential benefits of using data regarding the farming practices into the chocolate production process. These potential benefits especially concern product quality and production yield, giving directions for the future of chocolate production.