A genetic algorithm approach to cellular manufacturing systems

A genetic algorithm (GA) metaheuristic-based cell formation procedure is presented in this paper. The cell formation problem solved here is to simultaneously group machines and part-families into cells so that intercellular movements are minimized. An option for considering the minimization of cell load variation is included and another, which combines minimization of intercellular movements and cell load-variation, exists. The algorithm solves this problem through improving a cell configuration using the GA metaheuristic. The designer is allowed to specify the number of cells required a priori and impose lower and upper bounds on cell size. This makes the GA scheme flexible for solving the cell formation problems. The solution procedure was found to perform well on tested large-scale problems and published data sets. Moreover, the proposed procedure compares very favorably to a well-known algorithm, and another TSP-based heuristic available in the literature. The results of computational tests presented are very encouraging.     

A fuzzy-based negotiation approach for collaborative planning in manufacturing supply chains

The coordination of the planning operations across the manufacturing supply chains (MSC) is considered as a major component of supply chain management. As centralized coordination requires relevant information sharing, alternative approaches are needed to synchronize production plans between partners of MSC characterized by decentralized decision making systems with limited information sharing. In this paper, a bi-level fuzzy-based negotiation approach is proposed in order to model collaborative planning between MSC partners. During negotiation, each manufacturer is optimizing a bi-objective planning model. In order to generate optimal production plans, a genetic algorithm is used. To evaluate the exchanged proposals and the satisfaction degree of each partner, the fuzzy logic approach is adopted in the both negotiation levels. The main result of the developed approach consists in a collaborative decision making mechanism allowing the MSC partners to define their optimal production plans while considering the whole negotiating process with the pre-negotiation and post-negotiation stages. Computational tests done for different MSC structures show the effectiveness of the proposed mechanism, which ensures the satisfaction of the manufacturers and the optimality of the final solution. By comparing the results with the ones obtained considering a centralized planning approach, it is shown that the developed negotiation mechanism yields to near optimal solutions with insignificant gaps from the global optimal solutions.     

A genetic approach for adaptive multiagent control in heterarchical manufacturing systems

In this paper, we apply genetic algorithms to adapt the decision strategies of autonomous controllers in a part-driven heterarchical manufacturing system. The control agents use pre-assigned decision rules only for a limited amount of time, and obey a rule replacement policy propagating the most successful rules to the subsequent populations of concurrently operating agents. The twofold objective of this approach is to automatically optimize the performance of the control system during the steady-state unperturbed conditions of the manufacturing floor, and to improve the reactions of the agents to unforeseen disturbances (e.g., failures, shortages of materials) by adapting their decision strategies. Results on a detailed discrete event model of a multiagent heterarchical manufacturing system confirm the effectiveness of the approach.     

A combined dispatching criteria approach to scheduling semiconductor manufacturing systems

The scheduling problem of semiconductor manufacturing systems has multiple responses of interest. The objective is to simultaneously optimize these different responses or to find the best-compromised solution. Most previous research in the area of semiconductor manufacturing systems has focused on optimizing a single performance measure. Dabbas and Fowler proposed a modified dispatching approach that combines multiple dispatching criteria into a single rule with the objective of simultaneously optimizing multiple objectives. In this paper, we validate their proposed approach using two different fab models at different levels of complexity: a hypothetical six stage-five machines Mini-Fab model and a full scale wafer fab model adapted from an actual Motorola wafer fab. We also discuss the actual implementation of the proposed dispatching algorithm into a scheduler for daily operation at a Motorola wafer fabrication facility. Results show an average 20% improvement for all responses when using the proposed dispatching approach.     

Modelling manufacturing systems: A birth-death approach

An approximation method for modelling a manufacturing system is introduced. The system is considered as a queueing network, where each queue is limited in size, and interarrival and processing times are exponentially distributed. The birth-death approach is considered and an approximation method to reduce the dimension of the model is developed. The results are the marginal probability distribution of the number of units in each queue; other performance indices, such as mean queue lengths, utilizations of the working stations, and throughput can be easily obtained. The general procedure is applied to model, for example, queues in tandem, a split node, and a more complex network of queues. Simulation and, when possible, comparison with the exact solution show an acceptable error level of the proposed method.     

A systems approach to task allocation of human-robot interaction in manufacturing

Robots are sharing the manufacturing work environment with humans at an ever increasing rate. As such it has become imperative to analyze the manufacturing tasks and allocate them properly between humans and robots. A systems approach to task allocation has been taken in this paper that includes inventory of anticipated common tasks in manufacturing, design of products to be manufactured, allocation of tasks between humans and robots, and iterative improvement in product design.     

A lagrangean relaxation approach to machine allocation in cellular manufacturing systems

The allocation of machines to part families in cellular manufacturing systems is formulated as a 0–1 integer programming model, and a solution procedure based on Lagrangean relaxation is presented. The application of the formulation is illustrated using large, randomly generated problems.     

模糊逻辑遗传算法的新方法

模糊逻辑是近年来提出的一种自适应调整策略,可以用来动态调整遗传算法的参数,以提高其性能.在此提出一种模糊逻辑遗传算法(FGA)的新模糊控制系统,它根据种群的进化速度和多样性的反馈信息,通过模糊逻辑控制器来对交叉率Pc和变异率Pm进行动态的自适应控制.实验结果表明,提出的FGA相对于简单遗传算法(SGA),不仅在与实际最优值差值上获得高1～3个数量级的精度,而且还提高了收敛的速度,较好地解决了SGA容易陷入早熟状态、某些函数进化速度慢等问题.     

基于Petri网的柔性制造系统无死锁遗传调度算法

对以最小化加工时间为目标的柔性制造系统无死锁调度问题, 提出了一种遗传调度算法. 算法考虑到同类工件具有预先确定的相同加工路径, 而各工序的处理时间与工件有关. 用Petri网对工序和资源分配进行逻辑建模,利用遗传算法, 采用工序自然编码方式, 基于系统的最佳避免死锁Petri网控制器, 检测染色体的可行性, 修复不可行染色体使其对应的调度满足资源约束和无死锁控制约束, 从而保证算法所利用的所有染色体都对应系统的可行调度. 仿真结果表明了算法的可行性和有效性.     

A novel approach to fault diagnosis for time-delay systems

In this paper, a novel fault detection and identification (FDI) scheme for time-delay systems is presented. Different from the existing FDI design methods, the proposed approach utilizes fault tracking approximator (FTA) and iterative learning algorithm to obtain estimates of the fault functions. Performance of the FTA is rigorously analyzed by investigating its stability and fault tracking sensitivity properties in the presence of slowly developing or abrupt faults for state delayed dynamic systems. A novel feature of the FTA is that it can simultaneously detect and identify the shape and magnitude of the faults. Additionally, an extension to a class of nonlinear time-delay systems is made by using nonlinear control theories. Finally, the applicability and effectiveness of the proposed FDI scheme is illustrated by a practical industrial process.     

A genetic algorithm approach to measurement prescription in fault diagnosis

To fully discriminate among all possible diagnoses in a fault diagnosis task, one needs to take measurements from the system being diagnosed. The primary effects of taking one measurement in diagnosis based on first principles were presented in A. Reiter [Artificial Intelligence (32) (1987) 57–95] and a more detailed, formal account was given in A. Hou [Artificial Intelligence (65) (1994) 281–328]. However, the order in which measurements are to be taken is an issue. We propose a genetic algorithm to determine a good measurement order for a diagnosis task. The method applies operators such as selection, crossover, and mutation to evolve an initial population of measurement sequences. The quality of a measurement sequence is evaluated based on the cost taken for the measurement sequence to find the final diagnosis. Experiments on testing circuits have shown that the quality of measurement sequences is greatly improved after evolution.     

A synergistic approach to manufacturing systems control using machine learning and simulation

This paper describes a synergistic approach that is applicable to a wide variety of system control problems. The approach utilizes a machine learning technique, goal-directed conceptual aggregation (GDCA), to facilitate dynamic decision-making. The application domain employed is Flexible Manufacturing System (FMS) scheduling and control. Simulation is used for the dual purpose of providing a realistic depiction of FMSs, and serves as an engine for demonstrating the viability of a synergistic system involving incremental learning. The paper briefly describes prior approaches to FMS scheduling and control, and machine learning. It outlines the GDCA approach, provides a generalized architecture for dynamic control problems, and describes the implementation of the system as applied to FMS scheduling and control. The paper concludes with a discussion of the general applicability of this approach.     

An improved genetic heuristic to support the design of flexible manufacturing systems

In industry, when flexible manufacturing systems are designed within a group technology approach, numerous decision-taking processes emerge requiring control of the multiple characteristics of the system. In this context, several grouping problems are identified within the scope of combinatorial optimisation. Such is the case of the part families with precedence constraints problem, which is defined in order to set up families where the total dissimilarity among the parts placed in the same family is minimal and precedence constraints, as well as capacity constraints arise when grouping parts. The present paper describes the use of an improved genetic heuristic to tackle this problem. It comprises a standard genetic heuristic with appropriate operators, improved through specific local search. In order to study the performance of the improved genetic approach, a special purpose constructive heuristic plus an earlier version of the genetic heuristic were implemented. CPLEX software was used from a binary linear formulation for this problem. Computational results are given from the experiment performed using test instances partly taken from the literature while others were semi-randomly generated. The improved genetic heuristic produced optimal solutions for most of the shortest dimension test instances and acted positively in relation to the constructive heuristic results, over almost all the instances. As for the CPLEX it found optimal solutions only for the small instances, besides which for the higher dimensioned instances CPLEX failed to obtain any integer solutions at all, in 10h running time. Therefore, these experiments demonstrate that the improved genetic is a good tool to tackle high dimensioned test instances, when one does not expect an exact method to find an optimal solution in reasonable computing time.     

A genetic approach to the identification of linear dynamical systems with static nonlinearities

This paper investigates the use of genetic algorithms in the identification of linear systems with static nonlinearitites. Linear systems with static nonlinearities at the input known as the Hammerstein model, and linear systems with static nonlinearities at the output known as the Wiener model are considered in this paper. The parameters of the Hammerstein and the Wiener models are estimated using genetic algorithms from the input-output data by minimizing the error between the true model output and the identified model output. Using genetic algorithms, the Hammerstein and the Wiener models with known nonlinearity structure and unknown parameters can be identified. Moreover, systems with non-minimum phase characteristics can be identified. Extensive simulations have been used to study the convergence properties of the proposed scheme. Simulation examples are included to demonstrate the effectiveness and robustness of the proposed identification scheme.     

A multidimensional approach to manufacturing quality

In this paper, we examine the conditions under which it is profitable for a firm to invest in a quality-improvement program. To do this, we model quality along two dimensions: product performance (measured by deviation from an acceptable base performance level), and timeliness of product delivery (measured by time-related penalties such as lateness or tardiness). This explicit quality formulation is incorporated into a profit maximization framework in which it is necessary to weigh the revenue associated with each job against its cost in terms of time-related penalties and cost associated with defects. In this setting, we identify cases in which there is a positive net gain from altering the quality trade-off via a quality-improvement program such as Total Quality Management (TQM).     

Computing the productivity of multistation serial manufacturing systems: A heuristics approach

Analytical models to evaluate the performance of multistation serial production systems are difficult to build and solve. Based on the insights obtained from a two-station continuous materials flow production line, a heuristic is developed to approximate the production rate of longer lines.     

A self-organizing neural network approach for the design of cellular manufacturing systems

The crux problem of group technology (GT) is the identification of part families requiring similar manufacturing processes and the rearrangement of machines to minimize the number of parts that visit more than one machine cell. This paper presents an improved method for part family formation, machine cell identification, bottleneck machine detection and the natural cluster generation using a self-organizing neural network. In addition, the generalization ability of the neural network makes it possible to assign the new parts to the existing machine cells without repeating the entire computational process. A computer program is developed to illustrate the effectiveness of this heuristic method by comparing it with the optimal technique for large-scale problems.