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.     

Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations

The cellular manufacturing system (CMS) is considered as an efficient production strategy for batch type production. The CMS relies on the principle of grouping machines into machine cells and grouping machine parts into part families on the basis of pertinent similarity measures. The bacteria foraging optimization (BFO) algorithm is a modern evolutionary computation technique derived from the social foraging behavior of Escherichia coli bacteria. Ever since Kevin M. Passino invented the BFO, one of the main challenges has been the employment of the algorithm to problem areas other than those of which the algorithm was proposed. This paper investigates the first applications of this emerging novel optimization algorithm to the cell formation (CF) problem. In addition, for this purpose matrix-based bacteria foraging optimization algorithm traced constraints handling (MBATCH) is developed. In this paper, an attempt is made to solve the cell formation problem while considering cell load variations and a number of exceptional elements. The BFO algorithm is used to create machine cells and part families. The performance of the proposed algorithm is compared with a number of algorithms that are most commonly used and reported in the corresponding scientific literature such as K-means clustering, the C-link clustering and genetic algorithm using a well-known performance measure that combined cell load variations and a number of exceptional elements. The results lie in favor of better performance of the proposed algorithm.     

Formation of machine cells and part families in cellular manufacturing systems using a linear assignment algorithm

This paper presents a linear assignment algorithm for machine-cell and part-family formation for the design of cellular manufacturing systems. The present approach begins with the determination of part-family or machine-cell representatives by means of comparing similarity coefficients between parts or machines and finding a set of the least similar parts or machines. Using the group representatives and associated similarity coefficients, a linear assignment model is formulated for solving the formation problem by allocating the remaining parts or machines and maximizing a similarity index. Based on the formulated linear assignment model, a group formation algorithm is developed. The results of a comparative study based on multiple performance criteria and many existing data sets show that the present approach is very effective and efficient, especially in dealing with large-sized problems.     

A new genetic algorithm for the machine/part grouping problem involving processing times and lot sizes

This paper reports a new genetic algorithm (GA) for solving a general machine/part grouping (GMPG) problem. In the GMPG problem, processing times, lot sizes and machine capacities are all explicitly considered. To evaluate the solution quality of this type of grouping problems, a generalized grouping efficacy index is used as the performance measure and fitness function of the proposed genetic algorithm. The algorithm has been applied to solving several well-cited problems with randomly assigned processing times to all the operations. To examine the effects of the four major factors, namely parent selection, population size, mutation rate, and crossover points, a large grouping problem with 50 machines and 150 parts has been generated. A multi-factor (3⁴) experimental analysis has been carried out based on 324 GA solutions. The multi-factor ANOVA test results clearly indicate that all the four factors have a significant effect on the grouping output. It is also shown that the interactions between most of the four factors are significant and hence their cross effects on the solution should be also considered in solving GMPG problems.     

Forming part families by using genetic algorithm and designing machine cells under demand changes

This study develops a methodology which can be used to form manufacturing cells using both a new similarity coefficient based on the number of alternative routes during machine failure and demand changes for multiple periods. The methodology is divided into two phases. A new similarity coefficient, which considers the number of available alternative routes when available during machine failure, is suggested in Phase I. The primary objective of Phase I is to identify part families based on the new similarity coefficient by using a genetic algorithm. One of the major factors contributing to the success of cell implementation is flexibility for demand changes. It is difficult to reorganize the cells according to changes in demand, available machine capacity, and due date. Most of the suggested approaches in the literature tend to use a fixed demand for cellular manufacturing systems. Due to demand changes, cell design should include more than the one period that most researchers of cellular manufacturing systems consider. A new methodology for cell formation, which considers the scheduling and operational aspects in cell design under demand changes, is introduced in Phase II. Machines are assigned to part families by using an optimization technique. This optimization technique employs sequential and simultaneous mixed integer programming models for a given period to minimize the total costs which are related to the scheduling and operational aspects.     

A genetic algorithm for multiple sequence alignment

Multiple sequence alignment is an important tool in molecular sequence analysis. This paper presents genetic algorithms to solve multiple sequence alignments. Several data sets are tested and the experimental results are compared with other methods. We find our approach could obtain good performance in the data sets with high similarity and long sequences.The software can be found in http://rsdb.csie.ncu.edu.tw/tools/msa.htm.     

Application of genetic algorithm to computer-aided process planning in distributed manufacturing environments

In a distributed manufacturing environment, factories possessing various machines and tools at different geographical locations are often combined to achieve the highest production efficiency. When jobs requiring several operations are received, feasible process plans are produced by those factories available. These process plans may vary due to different resource constraints. Therefore, obtaining an optimal or near-optimal process plan becomes important. This paper presents a genetic algorithm (GA), which, according to prescribed criteria such as minimizing processing time, could swiftly search for the optimal process plan for a single manufacturing system as well as distributed manufacturing systems. By applying the GA, the computer-aided process planning (CAPP) system can generate optimal or near-optimal process plans based on the criterion chosen. Case studies are included to demonstrate the feasibility and robustness of the approach. The main contribution of this work lies with the application of GA to CAPP in both a single and distributed manufacturing system. It is shown from the case study that the approach is comparative or better than the conventional single-factory CAPP.     

A hybrid method based on genetic algorithm and dynamic programming for solving a bi-objective cell formation problem considering alternative process routings and machine duplication

Cell formation is one of the first and most important steps in designing a cellular manufacturing system. It consist of grouping parts with similar design features or processing requirements into part families and associated machines into machine cells. In this study, a bi-objective cell formation problem considering alternative process routings and machine duplication is presented. Manufacturing factors such as part demands, processing times and machine capacities are incorporated in the problem. The objectives of the problem include the minimization of the total dissimilarity between the parts and the minimization of the total investment needed for the acquisition of machines. A normalized weighted sum method is applied to unify the objective functions. Due to the computational complexity of the problem, a hybrid method combining genetic algorithm and dynamic programming is developed to solve it. In the proposed method, the dynamic programming is implemented to evaluate the fitness value of chromosomes in the genetic algorithm. Computational experiments are conducted to examine the performance of the hybrid method. The computations showed promising results in terms of both solution quality and computation time.     

考虑偏好不确定的合作性单元制造优化研究

企业为加速产品的迭代更新,可采取合作生产及研发制造一体化的战略。该研究在产品设计方面考虑顾客偏好的动态变化和竞争产品的影响,在生产方面引入单元化制造资源合作共享思想。通过建立考虑顾客偏好不确定的合作性单元制造模型,以最大化单位成本所带来的顾客效用为目标,提升消费者福祉。提出了混合候鸟优化算法(HMBO)进行求解,并将它与经典候鸟优化算法(MBO)和模拟退火算法(SA)进行了比较。算例及大量数值实验验证了该方法的正确性和合理性,结果表明在相同运行时间内HMBO优于MBO和SA。     

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.     

Solving facilities location problem in the presence of alternative processing routes using a genetic algorithm

Facilities location problem deals with the optimization of location of manufacturing facilities like machines, departments, etc. in the shop floor. This problem greatly affects performance of a manufacturing system. It is assumed in this paper that there are multiple products to be produced on several machines. Alternative processing routes are considered for each product and the problem is to determine the processing route of each product and the location of each machine to minimize the total distance traveled by the materials within the shop floor. This paper presents a mixed-integer non-linear mathematical programming formulation to find optimal solution of this problem. A technique is used to linearize the formulated non-linear model. However, due to the NP-hardness of this problem, even the linearized model cannot be optimally solved by the conventional mathematical programming methods in a reasonable time. Therefore, a genetic algorithm is proposed to solve the linearized model. The effectiveness of the GA approach is evaluated with numerical examples. The results show that the proposed GA is both effective and efficient in solving the attempted problem.     

Comparison of layered cellular manufacturing system design approaches

In this study, a mathematical programming approach is proposed to design a layered cellular manufacturing system in highly fluctuated demand environment. A mathematical model is developed to create dedicated, shared and remainder cells with the objective of minimizing the number of cells. In contrast with classical cellular manufacturing systems, in layered cellular systems, some cells can serve to multiple part families. A five-step hierarchical methodology is employed: (1) formation of part families, (2) calculation of expected cell utilizations and demand coverage probabilities, (3) specification cell types as dedicated, shared, and remainder cells, (4) simulation of proposed layered systems to evaluate their performance with respect to average flowtime and work-in-process inventory, and (5) statistical analysis to find the best layered cellular design among alternatives. It is found that designs with higher number of part families tend to have less number of machines. Similar results are also observed with respect to average flowtime and work-in-process inventory measures. The results are also compared with a heuristic approach from the literature. None of the approaches is dominant with respect to all of the performance measures. Mathematical modeling approach performs better in terms of number of machines for most of the alternative designs. However, heuristic approach yields better average flowtime and work-in-process inventory for most of the designs.     

A genetic algorithm for optimization of laminated dies manufacturing

Laminated tooling is based on taking sheets of metal and stacking them to produce the final product, after cutting each layer profile using laser or other techniques. CNC machining removes the extra material and brings the final product to specific tolerances. To reduce the cost of laminated dies manufacturing, the amount of the extra material and the number of slices must likewise be reduced. This is considered an optimization problem, which can be solved by genetic algorithms (G.A.). However, in most instances, premature convergence prevents the system from searching for a more optimal solution, a common problem in many G.A. applications. To address this problem, a new niching method is presented in this paper. Using the proposed method, results show not only a significant improvement in the quality of the optimum solution but also a substantial reduction in the processing time.     

实时分布处理系统的分层遗传调度方法

提出了一种将分层分组调度算法融入实时分布处理系统的并行设计,并结合遗传算法进行任务调度的方法。首先对初始系统有向非循环图(directed acyclic graph,DAG)分层分组;然后在层间进行均衡化的乒乓流水设计,重构系统,使系统均衡化;最后针对重构的系统编制相适应的二维染色体码,运用遗传算法进行调度。实验结果表明较之单纯的分层分组方法和遗传算法,系统的时延得到明显优化,算法的收敛速度提高。     

Robust design of flexible manufacturing systems using, colored Petri net and genetic algorithm

A method is presented for the robust design of flexible manufacturing systems (FMS) that undergo the forecasted product plan variations. The resource allocation and the operation schedule of a FMS are modeled as a colored Petri net and an associated transition firing sequence. The robust design of the colored Petri net model is formulated as a multi-objective optimization problem that simultaneously minimizes the production costs under multiple production plans (batch sizes for all jobs), and the reconfiguration cost due to production plan changes. A genetic algorithm, coupled with the shortest imminent operation time (SIO) dispatching rule, is used to simultaneously find the near-optimal resource allocation and the event-driven schedule of a colored Petri net. The resulting Petri net is then compared with the Petri nets optimized for a particular production plan in order to address the effectiveness of the robustness optimization. The simulation results suggest that the proposed robustness optimization scheme should be considered when the products are moderately different in their job specifications so that optimizing for a particular production plan creates inevitably bottlenecks in product flow and/or deadlock under other production plans.     

Reliability-based robust design optimization: A general methodology using genetic algorithm

In this paper, we present an improved general methodology including four stages to design robust and reliable products under uncertainties. First, as the formulation stage, we consider reliability and robustness simultaneously to propose the new formulation of reliability-based robust design optimization (RBRDO) problems. In order to generate reliable and robust Pareto-optimal solutions, the combination of genetic algorithm with reliability assessment loop based on the performance measure approach is applied as the second stage. Next, we develop two criteria to select a solution from obtained Pareto-optimal set to achieve the best possible implementation. Finally, the result verification is performed with Monte Carlo Simulations and also the quality improvement during manufacturing process is considered by identifying and controlling the critical variables. The effectiveness and applicability of this new proposed methodology is demonstrated through a case study.     

Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation

This paper addresses parallel machine scheduling problems with fuzzy processing times. A robust genetic algorithm (GA) approach embedded in a simulation model is proposed to minimize the maximum completion time (makespan). The results are compared with those obtained by using the “longest processing time” rule (LPT), which is known as the most appropriate dispatching rule for such problems. This application illustrates the need for efficient and effective heuristics to solve such fuzzy parallel machine scheduling problems (FPMSPs). The proposed GA approach yields good results quickly and several times in one run. Moreover, because it is a search algorithm, it can explore alternative schedules providing the same results. Thanks to the simulation model, several robustness tests are conducted using different random number sets, and the robustness of the proposed approach is demonstrated.     

A hybrid multi-objective approach based on the genetic algorithm and neural network to design an incremental cellular manufacturing system

One important issue related to the implementation of cellular manufacturing systems (CMSs) is to decide whether to convert an existing job shop into a CMS comprehensively in a single run, or in stages incrementally by forming cells one after the other, taking the advantage of the experiences of implementation. This paper presents a new multi-objective nonlinear programming model in a dynamic environment. Furthermore, a novel hybrid multi-objective approach based on the genetic algorithm and artificial neural network is proposed to solve the presented model. From the computational analyses, the proposed algorithm is found much more efficient than the fast non-dominated sorting genetic algorithm (NSGA-II) in generating Pareto optimal fronts.     

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.