Genetic algorithms for match-up rescheduling of the flexible manufacturing systems

Scheduling plays a vital role in ensuring the effectiveness of the production control of a flexible manufacturing system (FMS). The scheduling problem in FMS is considered to be dynamic in its nature as new orders may arrive every day. The new orders need to be integrated with the existing production schedule immediately without disturbing the performance and the stability of existing schedule. Most FMS scheduling methods reported in the literature address the static FMS scheduling problems. In this paper, rescheduling methods based on genetic algorithms are described to address arrivals of new orders. This study proposes genetic algorithms for match-up rescheduling with non-reshuffle and reshuffle strategies which accommodate new orders by manipulating the available idle times on machines and by resequencing operations, respectively. The basic idea of the match-up approach is to modify only a part of the initial schedule and to develop genetic algorithms (GAs) to generate a solution within the rescheduling horizon in such a way that both the stability and performance of the shop floor are kept. The proposed non-reshuffle and reshuffle strategies have been evaluated and the results have been compared with the total-rescheduling method.     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

Location allocation modeling for healthcare facility planning in Malaysia

Malaysia has seen tremendous growth in the standard of living and household per capita income. The demand for a more systematic and efficient planning has become increasingly more important, one of the keys to achieving a high standard in healthcare. In this paper, a Maximal Covering Location Problem (MCLP) is used to study the healthcare facilities of one of the districts in Malaysia. We address the limited capacity of the facilities and the problem is formulated as Capacitated MCLP (CMCLP). We propose a new solution approach based on genetic algorithm to examine the percentage of coverage of the existing facilities within the allowable distance specified/targeted by Malaysian government. The algorithm was shown to generate good results when compared to results obtained using CPLEX version 12.2 on a medium size problem consisting of 179 nodes network. The algorithm was extended to solve larger network consisting of 809 nodes where CPLEX failed to produce non-trivial solutions. We show that the proposed solution approach produces significant results in determining good locations for the facility such that the population coverage is maximized.     

Sink placement on a 3D terrain for border surveillance in wireless sensor networks

With their distributed nature and redundant operation capability, wireless sensor networks are very suitable for border surveillance scenarios that track intruders trying to breach to a safe side. In such scenarios, keeping the operation going on for as long as possible is the most important aspect of the network. We propose that by placing sink at a carefully selected coordinate will results in a longer living network. We also place restrictions on the candidate locations so that the sensing quality of the network is above a useful predetermined value and the sink is placed in a relatively safe location to avoid destruction. In order to find the suitable coordinates we propose a modified lifetime metric which takes quality and safety measures into account. We also propose a genetic algorithm which uses a discrete event simulator-in-the-loop over a three dimensional terrain to find locations for the sink that fits the given quality and safety restrictions. Using a three dimensional underlying terrain makes the proposed approach more realistic. The results obtained for various sensor network scenarios indicate that the proposed algorithm can find locations that increase the lifetime by also considering the sensing quality and safety.     

Genetic programming for multibiometrics

Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture… One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the same biometric modality, multiple feature extraction algorithms, multiple biometric modalities…). In this paper, we are interested in score level fusion functions application (i.e., we use a multibiometric authentication scheme which accept or deny the claimant for using an application). In the state of the art, the weighted sum of scores (which is a linear classifier) and the use of an SVM (which is a non linear classifier) provided by different biometric systems provide one of the best performances. We present a new method based on the use of genetic programming giving similar or better performances (depending on the complexity of the database). We derive a score fusion function by assembling some classical primitives functions (+, ∗, −, … ). We have validated the proposed method on three significant biometric benchmark datasets from the state of the art.     

Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side

This work presents an electricity consumption-forecasting framework configured automatically and based on an Adaptative Neural Network Inference System (ANFIS). This framework is aimed to be implemented in industrial plants, such as automotive factories, with the objective of giving support to an Intelligent Energy Management System (IEMS). The forecasting purpose is to support the decision-making (i.e. scheduling workdays, on-off production lines, shift power loads to avoid load peaks, etc.) to optimize and improve economical, environmental and electrical key performance indicators. The base structure algorithm, the ANFIS algorithm, was configured by means of a Multi Objective Genetic Algorithm (MOGA), with the aim of getting an automatic-configuration system modelling. This system was implemented in an independent section of an automotive factory, which was selected for the high randomness of its main loads. The time resolution for forecasting was the quarter hour. Under these challenging conditions, the autonomous configuration, system learning and prognosis were tested with success.     

Application of evolutionary optimisers in data-based calibration of Activated Sludge Models

Modelling activated sludge systems has become an accepted practice in Wastewater Treatment Plant (WWTP) design, teaching and research, and Activated Sludge Models (ASM) are by far the most widely used models for activated sludge systems. In most ASM applications, calibration is based on more or less ad-hoc and trial and error approaches. Calibration of the ASMs remains the weakest link in the overall process of modelling biological wastewater treatment. In this paper, a calibration approach is proposed where the need for expert knowledge and modeller effort is significantly reduced. The calibration approach combines identifiability analysis and evolutionary optimisers to automate the ASM calibration. Identifiability analysis is used to deal with poor identifiability of the model structures and evolutionary optimisers are used to identify the model parameters. The applied evolutionary optimisers are Genetic Algorithms and Differential Evolution. Performance of the evolutionary optimisers is compared with a previously proposed calibration approach based on Monte Carlo simulations. All methods were capable of calibrating the model when given enough computation time. However, some of the evolutionary optimisation methods had an advantage in terms of computation time against the Monte Carlo method.     

地暖系统的量子遗传约束广义预测控制

针对地板辐射采暖系统具有大惯性、参数不确定和外部随机干扰等特点,提出了一种基于量子遗传算法(QGA)的约束广义预测控制(GPC)方法。该方法将GPC的控制增量作为量子染色体的基因,通过量子旋转门扩展种群多样性,并滚动优化GPC的控制增量Δu(k)。试验表明,与常规广义预测控制相比,该控制方法具有良好的控制效果和更强的鲁棒性。     

Solving the traveling salesman problem using cooperative genetic ant systems

The travelling salesman problem (TSP) is a classic problem of combinatorial optimization and has applications in planning, scheduling, and searching in many scientific and engineering fields. Ant colony optimization (ACO) has been successfully used to solve TSPs and many associated applications in the last two decades. However, ACO has problem in regularly reaching the global optimal solutions for TSPs due to enormity of the search space and numerous local optima within the space. In this paper, we propose a new hybrid algorithm, cooperative genetic ant system (CGAS) to deal with this problem. Unlike other previous studies that regarded GA as a sequential part of the whole searching process and only used the result from GA as the input to subsequent ACO iterations, this new approach combines both GA and ACO together in a cooperative manner to improve the performance of ACO for solving TSPs. The mutual information exchange between ACO and GA in the end of the current iteration ensures the selection of the best solutions for next iteration. This cooperative approach creates a better chance in reaching the global optimal solution because independent running of GA maintains a high level of diversity in next generation of solutions. Compared with results from other GA/ACO algorithms, our simulation shows that CGAS has superior performance over other GA and ACO algorithms for solving TSPs in terms of capability and consistency of achieving the global optimal solution, and quality of average optimal solutions, particularly for small TSPs.     

改进CGA在3D动漫造型设计中的应用

为自动生成新颖多样的3D动漫造型,提出一种改进的元胞遗传算法(CGA)。使用ACIS规则表达式对已有的3D动漫造型在3个方向上实施非均匀缩放变形,采用树结构编码生成长度和内容变化较大的规则表达式,通过人机交互的方式,利用专家知识确定个体适应度值。基于3D动漫造型创新系统ECTDS的实验结果表明,该算法可以生成一系列生动的创新造型。