A multi-objective approach to facility layout problem by genetic search algorithm and Electre method

Classical approaches to layout design problem tend to maximise the efficiency of layout, measured by the handling cost related to the interdepartmental flow and to the distance among the departments. However, the actual problem involves several conflicting objectives hence requiring a multi-objective formulation. Multi-objective approaches, recently proposed, in most cases lead to the maximisation of a weighted sum of score functions. The poor practicability of such an approach is due to the difficulty of normalising these functions and of quantifying the weights. In this paper, this difficulty is overcome by approaching the problem in two subsequent steps: in the first step, the Pareto-optimal solutions are determined by employing a multi-objective constrained genetic algorithm and the subsequent selection of the optimal solution is carried out by means of the multi-criteria decision-making procedure Electre. This procedure allows the decision maker to express his preferences on the basis of the knowledge of candidate solution set. Quantitative (handling cost) and qualitative (adjacency and distance requests between departments) objectives are considered referring to a bay structure-based layout model, that allows to take into account also practical constraints such as the aspect ratio of departments. Results obtained confirm the effectiveness of the proposed procedure as a practicable support tool for layout designers.     

A -based layout design for gratings allocation

In the design process of gratings, the grating layout design is the most important activity influencing manufacturing cost. In the grating layout design, the key to saving manufacturing cost is to find a design with a minimal number of cutting operations. This paper presents an application of the A^* algorithm in grating layout design. The design problem is represented as the space search problem of design alternatives. A^* explores the feasible alternatives within the space, until an optimal solution is obtained.     

A method of object-oriented information management for layout design

During the layout design process, the spatial arrangement of components in an engineering system is developed in order to meet design goals and constraints. Proper organization of the information involved in this process enhances the quality of layout designs. The organization of information is accomplished through the use of the techniques and methods existing in the information management field. Using the object-oriented technique, a method was developed to guide the design of information management systems that support the layout design process. The method was developed in a case study involving, the layout design of automobile engine bay compoenents.     

CLASS: An algorithm for cellular manufacturing system and layout design using sequence data

Cell formation problem in CMS design has received the attention of researchers for more than three decades. However, use of sequence data for cell formation has been a least researched area. Sequence data provides valuable information about the flow patterns of various jobs in a manufacturing system. Therefore, it is only natural to expect that use of sequence data must result in not only identifying the part families and machine groups but also the layout (sequence) of the machines within each cell. Unfortunately, such an approach has not been taken in the past while solving CMS design problem using sequence data. In this paper, we fill this gap in the literature by developing an algorithm that not only identifies the cells but also the sequence of machines in the cells in a simultaneous fashion. The numerical computations of the algorithm with the available problems in the literature indicate the usefulness of the algorithm. Further, it also points to the untapped potential of such an approach to solve CMS design and layout problem using sequence data.     

A revised electromagnetism-like mechanism for layout design of reconfigurable manufacturing system

The layout design problem is one of the most important issues for manufacturing system design and control. A revised electromagnetism-like mechanism (REM) is proposed in this paper for the layout design of reconfigurable manufacturing systems utilizing automated guided vehicle. First, the formal model considering both loaded and empty flows is given. Then the REM is developed to solve the proposed model. In the REM, particles are encoded discretely. The charge of a particle is calculated according the total material handling cost of the particle. In the local search procedure, variable neighbourhood search strategy based on Hamming distance is adopted. In the moving procedure, the particles are moved according to the ordering of each element. To verify the effect of the proposed method, several computation cases are carried out. The computation results show that the proposed method is able to get optimal solutions for small scale problems and near optimal solutions within limited computation time for large scale problems. This indicates that the proposed method is effective and efficient.     

Genetic algorithm for buffer size and work station capacity in serial-parallel production lines

Recently, many production lines that have complicated structures such as parallel, reworks, feed-forward, etc., have become widely used in high-volume industries. Among them, the serial-parallel production line (S-PPL) is one of the more common production styles in many modern industries. One of the methods used for studying the S-PPL design is through a genetic algorithm (GA). One of the important jobs in using a GA is how to express a chromosome. In this study, we attempt to find the nearest optimal design of a S-PPL that will maximize production efficiency by optimizing the following three decision variables: buffer size between each pair of work stations, machine numbers in each of the work stations, and machine types. In order to do this we present a new GA-simulation-based method to find the nearest optimal design for our proposed S-PPL. For efficient use of a GA, our GA methodology is based on a technique that is called the gene family arrangement method (GFAM), which arranges the genes inside individuals. An application example shows that after a number of operations based on the proposed simulator, the nearest optimal design of a S-PPL can be found. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Solving an extended multi-row facility layout problem with fuzzy clearances using GA

Multi-row facility layout problem (MRFLP) is a class of facility layout problems, which decides upon the arrangement of facilities in some fixed numbers of rows in order to minimize material handling cost. Nowadays, according to the new layout requirements, the facility layout problems (FLPs) have many applications such as hospital layout, construction site layout planning and layout of logistics facilities. Therefore, we study an extended MRFLP, as a novel layout problem, with the following main assumptions: 1) the facilities are arranged in a two-dimensional area and without splitter rows, 2) multiple products are available, 3) distance between each pair of facilities, due to inaccurate and flexible manufacturing processes and other limitations (such as WIPs, industrial instruments, transportation lines and etc.), is considered as fuzzy number, and 4) the objective function is considered as minimizing the material handling and lost opportunity costs. To model these assumptions, a nonlinear mixed-integer programming model with fuzzy constraints is presented and then converted to a linear mixed-integer programming model. Since the developed model is an NP-hard problem, a genetic algorithm approach is suggested to find the best solutions with a minimum cost function. Additionally, three different crossover methods are compared in the proposed genetic algorithm and finally, a sensitivity analysis is performed to discuss important parameters.     

Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm

This paper presents a mixed-integer programming model for a multi-floor layout design of cellular manufacturing systems (CMSs) in a dynamic environment. A novel aspect of this model is to concurrently determine the cell formation (CF) and group layout (GL) as the interrelated decisions involved in the design of a CMS in order to achieve an optimal (or near-optimal) design solution for a multi-floor factory in a multi-period planning horizon. Other design aspects are to design a multi-floor layout to form cells in different floors, a multi-rows layout of equal area facilities in each cell, flexible reconfigurations of cells during successive periods, distance-based material handling cost, and machine depot keeping idle machines. This model incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. The objective is to minimize the total costs of intra-cell, inter-cell, and inter-floor material handling, purchasing machines, machine processing, machine overhead, and machine relocation. Two numerical examples are solved by the CPLEX software to verify the performance of the presented model and illustrate the model features. Since this model belongs to NP-hard class, an efficient genetic algorithm (GA) with a matrix-based chromosome structure is proposed to derive near-optimal solutions. To verify its computational efficiency in comparison to the CPLEX software, several test problems with different sizes and settings are implemented. The efficiency of the proposed GA in terms of the objective function value and computational time is proved by the obtained results.     

Integration of PSO and GA for optimum design of fuzzy PID controllers in a pendubot system

In this paper, a novel auto-tuning method is proposed to design fuzzy PID controllers for asymptotical stabilization of a pendubot system. In the proposed method, a fuzzy PID controller is expressed in terms of fuzzy rules, in which the input variables are the error signals and their derivatives, while the output variables are the PID gains. In this manner, the PID gains are adaptive and the fuzzy PID controller has more flexibility and capability than the conventional ones with fixed gains. To tune the fuzzy PID controller simultaneously, an evolutionary learning algorithm integrating particle swarm optimization (PSO) and genetic algorithm (GA) methods is proposed. The simulation results illustrate that the proposed method is indeed more efficient in improving the asymptotical stability of the pendubot system. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

An integrated neuro-genetic approach incorporating the Taguchi method for product design

Product design is a multidisciplinary activity that requires the integration of concurrent engineering approaches into a design process that secures competitive advantages in product quality. In concurrent engineering, the Taguchi method has demonstrated an efficient design approach for product quality improvement. However, the Taguchi method intuitively uses parameters and levels in measuring the optimum combination of design parameter values, which might not guarantee that the final solution is the most optimal. This work proposes an integrated procedure that involves neural network training and genetic algorithm simulation within the Taguchi quality design process to aid in searching for the optimum solution with more precise design parameter values for improving the product development. The concept of fractals in computer graphics is also considered in the generation of product form alternatives to demonstrate its application in product design. The stages in the general approach of the proposed procedures include: (1) use of the Taguchi experimental design procedure, (2) analysis of the neural network and genetic algorithm process, and (3) generation of design alternatives. An electric fan design is used as an example to describe the development and explore the applicability of the proposed procedures. The results indicate that the proposed procedures could enhance the efficiency of product design efforts by approximately 7.8%. It is also expected that the proposed design procedure will provide designers with a more effective approach to product development.     

Improved genetic algorithm for VLSI floorplan design with non-slicing structure

Floorplan design is an important engineering problem. This problem can be modeled as a combinatorial optimization problem, in which a given set of rectangles in floorplan must be arranged. The goal is to find the arrangement with minimum area and minimum interconnection. Floorplans can be classified into slicing structure and non-slicing structure problems. In both problem types, it is difficult to obtain a true optimal solution in a time that could be used in practice. We proposed the new immune algorithm for optimization of the slicing structure problem in the past. In this paper, we focus on and analyze the improved points considered to be especially effective, and propose a new genetic algorithm for the non-slicing structure problem. The proposed method is compared with existing methods using well-known benchmark problems.     

Robust manufacturing line design with controlled moderate robustness in bottleneck buffer time to manage stochastic inter-task times

Task time variations in a manufacturing line can result in a longer time to complete tasks than a given cycle time, leading to line stoppage and loss of production time. In practice, a portion of the cycle time is often allocated as a predefined fixed-size buffer time, determined based on experience, to account for such uncertain variations for a paced line without storage-buffers between stations. However, the size of the required buffer time in each station depends on the variation levels of task times and the desired conservatism level for preventing cycle time violation. In addition, there are uncertainties in other non-value adding activity times in stations, known as inter-task times. Although many studies on stochastic manufacturing line design focused on minimizing the cost incurred when the cycle time is exceeded due to task time variations, they mostly disregarded the inter-task times. Furthermore, it is worth studying the simultaneous effect of the manufacturing time uncertainty level and that of the conservatism level on the cycle time. This paper proposes a new method for moderately robust manufacturing line design that incorporates the conservatism level and uncertainties in the task and inter-task times. A new interpretation of the non-productive times in stations is presented by introducing the concept of the α-fractal buffer time to manage the effect of manufacturing time uncertainties. To overcome the problem of excessive robustness, a moderate robust approach with conservatism-level flexibility is used, focusing on the cycle time in the bottleneck station. The effect of the uncertainties and conservatism levels on the cycle time is analyzed through numerical examples. This study can be used for improving a manufacturing system in which uncertainties in tasks and inter-task times significantly degrade its productivity.     

Vibration reduction optimum design of a steam-turbine rotor-bearing system using a hybrid genetic algorithm

This paper describes the vibration optimum design for the low-pressure steam-turbine rotor of a 1007-MW nuclear power plant by using a hybrid genetic algorithm (HGA) that combines a genetic algorithm and a local concentration search algorithm using a modified simplex method. This algorithm not only calculates the optimum solution faster and more accurately than the standard genetic algorithm but can also find the global and local optimum solutions. The objective function is to minimize the resonance response (Q-factor) of the second occurring mode in the excessive vibration. Under the constraints of shaft diameter, bearing length and clearance, these factors play a very important role in the design of a rotor-bearing system. In the present work, the shaft diameter, bearing length and clearance are chosen as the design variables. The results show that the HGA can reduce the excessive response at the critical speed and improve the stability.     

Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings,lot splitting and flexible reconfiguration by simulated annealing

This paper presents a novel mixed-integer non-linear programming model for the layout design of a dynamic cellular manufacturing system (DCMS). In a dynamic environment, the product mix and part demands are varying during a multi-period planning horizon. As a result, the best cell configuration for one period may not be efficient for successive periods, and thus it necessitates reconfigurations. Three major and interrelated decisions are involved in the design of a CMS; namely cell formation (CF), group layout (GL) and group scheduling (GS). A novel aspect of this model is concurrently making the CF and GL decisions in a dynamic environment. The proposed model integrating the CF and GL decisions can be used by researchers and practitioners to design GL in practical and dynamic cell formation problems. Another compromising aspect of this model is the utilization of multi-rows layout to locate machines in the cells configured with flexible shapes. Such a DCMS model with an extensive coverage of important manufacturing features has not been proposed before and incorporates several design features including alternate process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine capacity, lot splitting, intra-cell layout, inter-cell layout, multi-rows layout of equal area facilities and flexible reconfiguration. The objective of the integrated model is to minimize the total costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead and machine processing. Linearization procedures are used to transform the presented non-linear programming model into a linearized formulation. Two numerical examples taken from the literature are solved by the Lingo software using a branch-and-bound method to illustrate the performance of this model. An efficient simulated annealing (SA) algorithm with elaborately designed solution representation and neighborhood generation is extended to solve the proposed model because of its NP-hardness. It is then tested using several problems with different sizes and settings to verify the computational efficiency of the developed algorithm in comparison with the Lingo software. The obtained results show that the proposed SA is able to find the near-optimal solutions in computational time, approximately 100 times less than Lingo. Also, the computational results show that the proposed model to some extent overcomes common disadvantages in the existing dynamic cell formation models that have not yet considered layout problems.     

Optimizing thermal design of data center cabinets with a new multi-objective genetic algorithm

There is an ever increasing need to use optimization methods for thermal design of data centers and the hardware populating them. Airflow simulations of cabinets and data centers are computationally intensive and this problem is exacerbated when the simulation model is integrated with a design optimization method. Generally speaking, thermal design of data center hardware can be posed as a constrained multi-objective optimization problem. A popular approach for solving this kind of problem is to use Multi-Objective Genetic Algorithms (MOGAs). However, the large number of simulation evaluations needed for MOGAs has been preventing their applications to realistic engineering design problems. In this paper, details of a substantially more efficient MOGA are formulated and demonstrated through a thermal analysis simulation model of a data center cabinet. First, a reduced-order model of the cabinet problem is constructed using the Proper Orthogonal Decomposition (POD). The POD model is then used to form the objective and constraint functions of an optimization model. Next, this optimization model is integrated with the new MOGA. The new MOGA uses a “kriging” guided operation in addition to conventional genetic algorithm operations to search the design space for global optimal design solutions. This approach for optimal design is essential to handle complex multi-objective situations, where the optimal solutions may be non-obvious from simple analyses or intuition. It is shown that in optimizing the data center cabinet problem, the new MOGA outperforms a conventional MOGA by estimating the Pareto front using 50% fewer simulation calls, which makes its use very promising for complex thermal design problems. Recommended by: Monem Beitelmal     

Plastic injection mould cooling system design by the configuration space method

The cooling system of an injection mould is very important to the productivity of the injection moulding process and the quality of the moulded part. Despite the various research efforts that have been directed towards the analysis, optimization, and fabrication of cooling systems, support for the layout design of the cooling system has not been well developed. In the layout design phase, a major concern is the feasibility of building the cooling system inside the mould insert without interfering with the other mould components. This paper reports a configuration space (C-space) method to address this important issue. While a high-dimensional C-space is generally required to deal with a complex system such as a cooling system, the special characteristics of cooling system design are exploited in the present study, and special techniques that allow C-space computation and storage in three-dimensional or lower dimension are developed. This new method is an improvement on the heuristic method developed previously by the authors, because the C-space representation enables an automatic layout design system to conduct a more systematic search among all of the feasible designs. A simple genetic algorithm is implemented and integrated with the C-space representation to automatically generate candidate layout designs. Design examples generated by the genetic algorithm are given to demonstrate the feasibility of the method.     

Human-competitive lens system design with evolution strategies

Lens system design provides ideal problems for evolutionary algorithms: a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. This paper demonstrates, through the use of two evolution strategies, namely non-isotropic Self-Adaptive evolution strategy (SA-ES) and Covariance Matrix Adaptation evolution strategy (CMA-ES), as well as multiobjective Non-Dominated Sort Genetic Algorithm 2 (NSGA-II) optimization, the human competitiveness of an approach where an evolutionary algorithm is hybridized with a local search algorithm to solve both a classic benchmark problem, and a real-world problem.     

板球系统的参数自调整反步控制

为解决系统初始值大范围变化,且控制量受到饱和特性约束的板球系统镇定控制问题,提出一种自动调整待设计参数的反步控制方法.分析了反步法偏差系统的平衡点特性;说明了反步法待设计参数与偏差系统动态间的关系;构造了Mamdani模糊逻辑,以自动调整反步法待设计参数;考虑了控制量受饱和特性限制而引入的约束条件,进而以遗传算法优化了模糊规则.仿真和实验结果均表明,自动调整待设计参数的反步控制方法降低了镇定控制中的位置偏差和位置超调.     

A computer software for PID tuning by new frequency domain design method

A software package has been developed for the PID controller tuning introduced by Wang et al. using new frequency domain method. It is user friendly and is illustrated by two practical examples. The study includes the scope and limitations of new frequency domain design method.     

Effective transfer function method for decentralized control system design of multi-input multi-output processes

In terms of relative gain and relative frequency, the effective transfer function for independent controller design for multi-input multi-output processes is provided in this paper. Differing from existing equivalent transfer functions, the proposed effective transfer function provides both gain and phase information for decentralized controller design in a simple and straightforward manner. The interaction effects for a particular loop from all other closed loops are directly incorporated into the effective transfer functions in four ways. Consequently, the decentralized controllers can be independently designed by employing the single loop tuning techniques. This design method is simple, straightforward, easy to understand and implement by field engineers. Several multivariable industrial processes with different interaction modes are employed to demonstrate the effectiveness and simplicity of the method.