Optimal feeding profile in fed-batch bioreactors using a genetic algorithm

An optimal feeding profile for a fed-batch process was designed based on an evolutionary algorithm. Usually the presence of multiple objectives in a problem leads to a set of optimal solutions, commonly known as Pareto-optimal solutions. Evolutionary algorithms are well suited for deriving multi-objective optimisation since they evolve a set of non-dominated solutions distributed along the Pareto front. Several evolutionary multi-objective optimisation algorithms have been developed, among which the Non-dominated Sorting Genetic Algorithm NSGA-II is recognised to be very effective in overcoming a variety of problems. To demonstrate the applicability of this technique, an optimal control problem from the literature was solved using several methods considering the single-objective dynamic optimisation problem.     

Galerkin based generalized ANOVA for the solution of stochastic steady state diffusion problems

This work presents a novel approach, referred here as Galerkin based generalized analysis of variance decomposition (GG-ANOVA), for the solution of stochastic steady state diffusion problems. The proposed approach utilizes generalized ANOVA (G-ANOVA) expansion to represent the unknown stochastic response and Galerkin projection to decompose the stochastic differential equation into a set of coupled differential equations. The coupled set of partial differential equations obtained are solved using finite difference method and homotopy algorithm. Implementation of the proposed approach for solving stochastic steady state diffusion problems has been illustrated with three numerical examples. For all the examples, results obtained are in excellent agreement with the benchmark solutions. Additionally, for the second and third problems, results obtained have also been compared with those obtained using polynomial chaos expansion (PCE) and conventional G-ANOVA. It is observed that the proposed approach yields highly accurate result outperforming both PCE and G-ANOVA. Moreover, computational time required using GG-ANOVA is in close proximity of G-ANOVA and less as compared to PCE.     

Multi-objective unrelated parallel machine scheduling: a Tabu-enhanced iterated Pareto greedy algorithm

This work proposes a high-performance algorithm for solving the multi-objective unrelated parallel machine scheduling problem. The proposed approach is based on the iterated Pareto greedy (IPG) algorithm but exploits the accessible Tabu list (TL) to enhance its performance. To demonstrate the superior performance of the proposed Tabu-enhanced iterated Pareto greedy (TIPG) algorithm, its computational results are compared with IPG and existing algorithms on the same benchmark problem set. Experimental results reveal that incorporating the accessible TL can eliminate ineffective job moves, causing the TIPG algorithm to outperform state-of-the-art approaches in the light of five multi-objective performance metrics. This work contributes a useful theoretical and practical optimisation method for solving this problem.     

Steady and unsteady incompressible flow in a double driven cavity using the artificial compressibility (AC)‐based characteristic‐based split (CBS) scheme

In this paper, the explicit characteristic‐based split (CBS) scheme has been employed to solve both steady and unsteady flows inside a non‐rectangular double driven cavity. This problem is recently suggested as a benchmark problem for incompressible flows. Both unstructured and structured meshes have been employed in the present study to make sure that the predicted results are as close to reality as possible. The results obtained show the existence of steady state at lower Reynolds numbers (?1000) and transient states at higher Reynolds numbers. The flow approaches a turbulent state as the Reynolds number is increased to 10 000. Copyright © 2005 John Wiley & Sons, Ltd.     

Application of three­dimensional numerical simulation to analysis of development of deformation zone at beginning of aluminium extrusion process

Abstract

Understanding the thermomechanical phenomena that occur during aluminium extrusion with respect to the variations of temperature, flow stress, strain, and strain rate is of importance for process optimisation. Conventional analytical methods are restricted to the steady state stage of the process and thus cannot provide an insight into the dynamic changes taking place during the initial stage. In the present work, three­dimensional simulations using the finite element method were carried out to analyse the development of the deformation zone at the die front and the temperature evolution, before the process attains the steady state. The analysis revealed that a change in friction factor at the billet/container interface from 0.3 to 0.9 enlarges the dead metal zone. However, its size appears unaffected by a change of die orifice shape from round to square at the same reduction ratio. The increase in friction results in an increase of initial extrusion load of about 6%.     

Inventory-shortage driven optimisation for product configuration variation

Inventory control is a critical problem in manufacturing systems. Inventory shortage significantly affects system productivity, while excessive stocks increase the operation cost. It is difficult to avoid fully inventory shortage under mass customisation manufacturing based on product configuration. In this paper, we propose a new approach for inventory-shortage driven optimisation of dynamic product configuration variation to meet the requirements of product configuration change and find suitable combination of parts by considering cost, lead-time and inventory variation. The multi-objective optimisation model uses a multi-objective genetic algorithm and adds impact cost, lead-time and inventory factors to the normal configuration optimisation model. An industrial case study demonstrates the practicality and effectiveness of the proposed approach. By means of this research, valid solutions for configuration variation are available to the decision makers.     

Solving multi-objective rescheduling problems in dynamic permutation flow shop environments with disruptions

In multi-objective optimisation problems, optimal decisions need to be made in the presence of trade-offs among conflicting objectives which may sometimes be expressed in different units of measure. This makes it difficult to reduce the problem to a single-objective optimisation. Furthermore, when disruptive changes emerge in manufacturing environments, such as the arrival of new jobs or machine breakdowns, the scheduling system should be adapted by responding quickly. In this paper, we propose a rescheduling architecture for solving the problem based on a predictive-reactive strategy and a new method to calculate the reactive schedule in each rescheduling period. Additionally, we developed a methodology that allows the use of multi-objective performance metrics to evaluate dispatching rules. These rules are applied at a benchmark specifically designed for this paper considering three objective functions: makespan, total weighted tardiness and stability. Three types of disruptions are also considered: arrivals of new jobs, machine breakdowns and variations in job processing times. Results showed that the RANDOM rule provides a better behaviour compared to other evaluated rules and a lower ratio of non-dominated solutions compared to ATC (apparent tardiness cost) and FIFO (first-in-first-out) rules. Moreover, the behaviour of the hypervolume metric depends on the problem dimensions.     

枝晶生长的稳态理论新进展

本文从自由生长和强制生长两个方面着重评述了单相合金枝晶生长的稳态理论新进展。发现稳态理论近年来有两个重要的进展，一是比较好地解决了自由生长枝晶尖端的稳态扩散场问题；另一是理论分析和实验研究的结果都表明，强制生长枝晶一次间距选择一个较宽的容许范围，平均一次间距在这个范围内的具体取值与凝固系统所经历的历史明显相关     

Elasto-plastic strain analysis by a semi-analytical method

The aim of this paper is to develop a simulation model of large deformation problems following a semi-analytical method, incorporating the complications of geometric and material non-linearity in the formulation. The solution algorithm is based on the method of energy principle in structural mechanics, as applicable for conservative systems. A one-dimensional solid circular bar problem has been solved in post-elastic range assuming linear elastic, linear strain hardening material behaviour. Type of loading includes uniform uniaxial loading and gravity loading due to body force, whereas the geometry of the bar is considered to be non-uniformly taper. Results are validated successfully with benchmark solution and some new results have also been reported. The location of initiation of elasto-plastic front and its growth are found to be functions of geometry of the bar and loading conditions. Some indicative results have been presented for static and dynamic problems and the solution methodology developed for one-dimension has been extended to the elasto-plastic analysis of two-dimensional strain field problems of a rotating disk.     

Performance of different vector potential formulations in solvingmultiply connected 3-D eddy current problems

The authors describe their numerical experiences in applying FEM (finite-element method) solution techniques to a 3-D (three-dimensional) eddy-current problem with a coil-driven multiply connected conductor, the benchmark problem No.7 of the International TEAM Workshops. Several formulations have been tried using a magnetic vector and electric scalar potential or an electric vector and a magnetic scalar in the conductor and a magnetic vector or scalar potential outside. The problem has been solved at two frequencies. The authors briefly describe the formulations used and compare the performance. Magnetic field and current density plots are also compared. The advantages and disadvantages of the various versions are pointed out. The use of a magnetic scalar potential H rather than a magnetic vector potential A outside the conductor and the hole substantially reduces the number of degrees of freedom and thus the computational effort. The versions using it in the conductor yield relatively ill-conditioned systems. Also, at the higher frequency, the conditioning deteriorates considerably     

Migration NSGA: method to improve a non-elitist searching of Pareto front,with application in magnetics

The paper describes a migration strategy to improve classical non-dominated sorting genetic algorithm (NSGA) to find optimal solution of a multi-objective problem. Migration NSGA has been tested to assess its performance using analytical functions for which the Pareto front is known in analytical form, as well as two case studies in electromagnetics, for which the Pareto front is not known a priori. This strategy improves the approximation of the Pareto-optimal solutions of a multi-objective problem by introducing new individuals in the population miming the effect of migrations.     

Solidification of a plane ingot in a wedge-like mold with a feeder head

The nonstationary problem of the solidification of metal in a plane wedge-like mold with a feeder head has been solved by the variational method using the concept of the local thermodynamic potential, and the positions of the solidification front at any instants of time have been determined.     

Heat transfer analysis of the continuous casting process by the front tracking BEM

Heat transfer problem in the strand during continuous casting is considered in the paper. Steady state temperature field in a coordinate system attached to the mould has been analyzed. Phase change is assumed to take place at constant temperature. The nonlinearity in this problem is caused by the unknown location of the solid–liquid interface. To determine both, this location and the resulting temperature field in the strand, a novel BEM front tracking algorithm has been developed.Numerical examples like one-dimensional (1D) phase change benchmark (modeled as two-dimensional (2D)) and several 2D problems of continuous casting of copper are included and discussed.     

Diffusion controlled smoulder propagation in a thin slab

Complex variable techniques are used to determine the shape of the smouldering reaction front and the concentration of the oxidizer behind the front for steady smoulder propagation in a solid slab of exothermically reacting material. It extends an earlier free boundary problem of Adler and Herbert which considered diffusion controlled smoulder propagation in a half-space. The region behind the reaction front is assumed to be porous, the oxidizer diffusing from both planar surfaces to the front, where its concentration vanishes. Suitable scaling allows the oxidizer concentration to be expanded in powers of a small parameter. The resulting coupled differential equations for the coefficients are solved in terms of functional equations. Some consideration is given to the regions where the front meets the planar surfaces. It is shown that, close to the leading edge, the surface concentration varies monotonically with distance from the edge.     

A hybrid dynamic pre-emptive and competitive neural-network approach in solving the multi-objective dispatching problem for TFT-LCD manufacturing

This research addresses a hybrid dynamic pre-emptive and competitive neural-network approach in solving the multi-objective dispatching problem. It optimises three performance criteria simultaneously, namely: cycle time, slack time, and throughput. A case study is adopted to illustrate the performance of applying the methodology. Thin film transistor-liquid crystal display (TFT-LCD) is a high-technology industry, with a growing market. The manufacturing process is complex. It involves multi-products, sequence-dependent set-ups, random breakdowns, and multiple-objectives, with bias-weighted optimisation problems. To determine appropriate dispatching strategies, under various system conditions, is a non-trivial challenge to control the complex systems. There has been little research on these problems aimed at solving them simultaneously. This paper presents an event-triggered dynamic dispatching system that combines artificial intelligence methods to archive optimum dispatching strategies under diverse shop-floor conditions. Results show this system to be superior to previous researches.     

Reliability evaluation of hybrid power markets based on multiple transaction matrix and optimal transaction curtailment

A Monte Carlo simulation technique evaluates the reliability indices of restructured power systems with a hybrid market. A model for optimal transaction curtailment for a contingency state in a hybrid market is developed to incorporate the changes brought about by deregulation. The objective of the contingency optimal transaction curtailment for each individual generation company is to minimise its revenue loss. The problem is formulated as a linear programming problem and solved using an optimisation technique. Customer load curtailment, which is the basic parameter for reliability evaluation, is determined using a load-shedding philosophy which is based on the results of the optimal transaction curtailment by the genco. Supply and demand transactions of the market participants are represented by a transaction matrix. The impact of the firm and nonfirm bilateral and reserve contracts on customer reliabilities have been studied. The technique has been illustrated by application to the IEEE Reliability Test System.     

Darwinian approach for dynamic spectrum allocation in next generation systems

The authors present the use of a genetic algorithm (GA) model as a solution approach to the dynamic spectrum allocation (DSA) problem considered as a difficult combinatorial optimisation problem. The proposed multi-objective GA model enhances overall spectral efficiency of the network, while optimising its own spectrum utilisation to generate accessible spectrum opportunities for other radio technologies. A novel two-dimensional encoding technique is defined to represent solutions in the problem domain and the technique enables significantly shorter convergence times. A simulation tool has been developed to model the GA-based DSA and to compare the new scheme with the conventional fixed spectrum allocation (FSA) scheme under both uniform and non-uniform traffic distributions. The proposed scheme significantly outperformed the FSA scheme both in terms of spectral efficiency gain and spectral utilisation.     

用于多目标优化的蚁群算法的构建及其应用

从蚁群的生物学行为出发,将成群募集和海量募集两种机制融入蚁群算法,并针对多目标优化的特性,综合考虑解的被支配度和分散度,抽提出一种启发式规则,用以评价食物源的优劣,进而构建多目标连续蚁群优化算法(MO-CACO).通过两个多目标典型函数的优化测试,验证了MO-CACO具有较强的多目标全局寻优能力,且稳健性良好,所求得的最优解集的多目标值能均匀地逼近Pareto最优前沿的各部分.将MO-CACO用于二甲苯异构化装置的操作优化,取得了满意的结果,MO-CACO可为化工过程多目标决策提供支持.     

Optimisation of a vehicle energy absorbing steel component with experimental validation

The paper deals with the optimisation of a tapered tubular steel component to be used as an energy-absorbing device in the front structure of a vehicle body. Aim of the optimisation problem is the minimisation of a load uniformity parameter evaluated as the ratio between the maximum and the average crushing loads. The optimisation problem takes into account two design variables describing a tapered geometrical configuration, the diameter of the component at one of its bounds and the tapering length. Two improved geometrical configurations have been found and have been experimentally tested to verify the numerical results with impact velocities up to 10 m/s.