Machine-part Cell Formation Problem Research Articles

A A Hybrid PSO Based Algorithm for Solving the Machine-Part Cell Formation Problem

A A Hybrid PSO Based Algorithm for Solving the Machine-Part Cell Formation Problem

CoCAT: a new similarity coefficient for solving the machine-part cell formation problem

CoCAT: a new similarity coefficient for solving the machine-part cell formation problem

Multi-objective machine cell formation by NSGA-II

Non-dominated Sorting Genetic Algorithm (NSGA)-II is applied for multiple objective machine part cell formation problem. Minimization of cell load variation and minimization of total moves are considered as two conflicting objectives. Pareto optimal solutions are obtained for some test problems. Results are compared with those obtained by other methods. The comparison reveals that Pareto optimal fronts obtained are better than reported and reveal the strength of NSGA-II algorithm suited for integer multiple objective cell formation problems in Cellular Manufacturing Systems.

An improved genetic algorithm for the machine-part cell formation problem

An improved genetic algorithm for the machine-part cell formation problem

The Machine-Part Cell Formation Problem with Non-Binary Values: A MILP Model and a Case of Study in the Accounting Profession

The traditional machine-part cell formation problem simultaneously clusters machines and parts in different production cells from a zero–one incidence matrix that describes the existing interactions between the elements. This manuscript explores a novel alternative for the well-known machine-part cell formation problem in which the incidence matrix is composed of non-binary values. The model is presented as multiple-ratio fractional programming with binary variables in quadratic terms. A simple reformulation is also implemented in the manuscript to express the model as a mixed-integer linear programming optimization problem. The performance of the proposed model is shown through two types of empirical experiments. In the first group of experiments, the model is tested with a set of randomized matrices, and its performance is compared to the one obtained with a standard greedy algorithm. These experiments showed that the proposed model achieves higher fitness values in all matrices considered than the greedy algorithm. In the second type of experiment, the optimization model is evaluated with a real-world problem belonging to Human Resource Management. The results obtained were in line with previous findings described in the literature about the case study.

A Reactive Population Approach on the Dolphin Echolocation Algorithm for Solving Cell Manufacturing Systems

In this paper, we integrate the autonomous search paradigm on a swarm intelligence algorithm in order to incorporate the auto-adjust capability on parameter values during the run. We propose an independent procedure that begins to work when it detects a stagnation in a local optimum, and it can be applied to any population-based algorithms. For that, we employ the autonomous search technique which allows solvers to automatically re-configure its solving parameters for enhancing the process when poor performances are detected. This feature is dramatically crucial when swarm intelligence methods are developed and tested. Finding the best parameter values that generate the best results is known as an optimization problem itself. For that, we evaluate the behavior of the population size to autonomously be adapted and controlled during the solving time according to the requirements of the problem. The proposal is testing on the dolphin echolocation algorithm which is a recent swarm intelligence algorithm based on the dolphin feature to navigate underwater and identify prey. As an optimization problem to solve, we test a machine-part cell formation problem which is a widely used technique for improving production flexibility, efficiency, and cost reduction in the manufacturing industry decomposing a manufacturing plant in a set of clusters called cells. The goal is to design a cell layout in such a way that the need for moving parts from one cell to another is minimized. Using statistical non-parametric tests, we demonstrate that the proposed approach efficiently solves 160 well-known cell manufacturing instances outperforming the classic optimization algorithm as well as other approaches reported in the literature, while keeping excellent robustness levels.

A Novel Genetic Algorithm for Solving Machine Part Cell Formation Problem considering alternative Process Plans

A novel genetic algorithm (GA) is proposed in this paper for solving the machine-part cell formation problem in the presence of alternative process plans. Parent chromosomes with number of genes equal to the number of parts are generated based on the correlation value calculated using a ranking index. Crossover is carried out on the 60% of the parent chromosomes and mutation is carried out at the weakly correlated part (gene) in the chromosomes. Performance of the algorithm was tested using 20 test instances from the literature. The proposed genetic algorithm is superior in terms of solution quality for 10% of the total test instances and equal to the best solution achieved by the other algorithms for the remaining 90% of the test instances.

Machine Cell Formation with Alternative Routings based on Genetic Algorithm

In automated batch type production systems, machine-part cell formation problems (CFP) have long drawn attention of researchers. The objective of CFP in cellular manufacturing system (CMS) is to identity machine cells and part families in order to minimize the intercellular movements of parts as well as maximize the utilization of machines. Optimum cell formation results reduction in total production times, in-process inventories, material handling cost, labor cost/times, paper works, number of machine set-ups, set-up times. It also simplifies process plans, management and improves product quality, productivity, utilization of resources. Since the modern manufacturing machines are generally multifunctional, so the processing of parts can be performed by number of alternative routes. The objectives of this study are to determine the optimal machine cells with optimal processing route and balanced machine cells (minimum cell load variation). Here a genetic algorithm heuristic approach is presented for six benchmark problems with multiple process routes, sequence of processes and parts volume. Computational outcome show that the proposed heuristic gives better result comparatively with the well-known existing methods in terms of total intercellular movements.

CORA - a heuristic approach to machine-part cell formation in the presence of alternative process plans

In this paper, a three-stage heuristic is proposed to solve the machine-part cell formation (MPCF) problem in which parts have alternative process plans, commonly known as the generalized group technology problem. In the first stage, the best process plan (part route) for each part is selected using the proposed route rank index (RRI), a ranking measure calculated from the correlation among the process plans (CoRa – Correlation based ranking). In the second stage, machine-part cells are identified with an objective to maximize the grouping efficacy. A fine-tuning module validates the covering set in the third stage. Computational performance of the proposed heuristic on a set of generalized group technology dataset available in the literature is presented. The process plans identified by CORA resulted in a higher grouping efficacy for 25% of the test instances and for the other test instances the grouping efficacy achieved was as good as the best results reported in literature.

A heuristic approach based on Euclidean distance matrix for the machine-part cell formation problem

Cellular manufacturing system (CMS) is an example of application of group technology in manufacturing systems. The cell formation problem (CFP) in CMS aims to identify part families and machine cells in order to minimize the intercellular movement and to maximize the machine utilization within a cell. Previous study in CFP generally motivated on maximizing grouping efficacy (GC) by minimizing exceptional elements as well as void elements. In this paper, a heuristic approach based on Euclidean Distance matrixis proposed. Computational experiments were performed with 20 benchmark problem sets taken from the literature. Computational results demonstrate that the performances of the proposed heuristic in terms of GC are either better than or competitive with the well-known existing algorithms.

An Ant Colony Optimization Algorithm for Cellular Manufacturing System

In this paper, an ant colony algorithm is used to solve the machine part cell formation problem based on the machine – index - based local procedure and part assignment rule for the better clustering of machines in to machine cells and parts in to part families. The main objective of this paper is to increase the grouping efficacy which is one of the good performance measures for the cell formation problem. The performance assessment of the ant colony algorithm was carried out by testing with the benchmark problems from the literature. From this assessment it clearly shows that this machine index based local procedure is capable to solve the machine part cell formation problem efficiently.

Efficient Parallel Sorting for Migrating Birds Optimization When Solving Machine-Part Cell Formation Problems

The Machine-Part Cell Formation Problem (MPCFP) is a NP-Hard optimization problem that consists in grouping machines and parts in a set of cells, so that each cell can operate independently and the intercell movements are minimized. This problem has largely been tackled in the literature by using different techniques ranging from classic methods such as linear programming to more modern nature-inspired metaheuristics. In this paper, we present an efficient parallel version of the Migrating Birds Optimization metaheuristic for solving the MPCFP. Migrating Birds Optimization is a population metaheuristic based on the V-Flight formation of the migrating birds, which is proven to be an effective formation in energy saving. This approach is enhanced by the smart incorporation of parallel procedures that notably improve performance of the several sorting processes performed by the metaheuristic. We perform computational experiments on 1080 benchmarks resulting from the combination of 90 well-known MPCFP instances with 12 sorting configurations with and without threads. We illustrate promising results where the proposal is able to reach the global optimum in all instances, while the solving time with respect to a nonparallel approach is notably reduced.

Implementation of Machine Part Cell Formation Algorithm in Cellular Manufacturing Technology Using Neural Networks

The machine-part cell formation problem explains the topics surrounding the creation of part families based on the processing needs of the components, and the classification of machine groups based on their capacity to process exact part families in manufacturing. Allocate the specified machines and parts into cells so that the grouping efficiency is maximized. The objective of the paper is to cluster the parts based upon self-organizing map (SOM). After clustering, Minkowski distance is used to sort the machines and some bottlenecks are introduced during the clustering process which can be removed manually by rearranging. We are going to implement self-organizing map (SOM) which is a type of artificial neural network technique to solve this problem. The proposed Minkowski distance approach produced solution with a good group efficiency.

Machine-Part Cell Formation Problem Using a Group Based League Championship Algorithm

This article proposes a new algorithm for grouping problems that is a grouping version of league championship algorithm (GLCA). We compare the performance of GLCA with several well-known algorithms published in the present literature and select a set of 20 most widely used benchmarks of cell formation sample problems posing as a grouping problem. We used a truncated geometric algorithm to find the number of initial cells. Our computations reveal that GLCA can reach the best-known solution for 17 of the 20 benchmark problems, and improve the solution of three others with a 1.4% average gap.

Some clarifications on the use of Mahalanobis distance for the machine-part cell formation problem

In this paper, the clarifications regarding the usage of Mahalanobis distance (MD) for the machine-part cell formation (MPCF) are demonstrated. Perhaps, for the first time, part families are created using the correlation matrix derived from the covariance matrix of the given incidence matrix. The proposed MD-based method is compared with the existing popular methods for MPCF using the benchmark data sets from the literature. Also, in the present work, the procedure for calculation of MD for the purpose of machine-part cell formation in the event of high correlations is explained. The comparative performance analysis reveals that the grouping efficacy of the proposed MD-based method is at par or better when compared with the other popular algorithms available in the literature.

An Ant Colony Optimization Approach for the Machine–Part Cell Formation Problem

In this paper, the problem of grouping machines and parts into cells (machine-part cell formation problem) is considered with the objective of minimizing grouping efficacy. An ant colony optimization algorithm is developed to solve such problem. In the proposed algorithm, solutions are constructed in a new manner, two heuristic algorithms are used to determine part families and machine cells and the similarity between parts is used as heuristic information. The proposed algorithm is experimented on 35 test problems from the literature which shows its advantage over existing algorithms. The algorithm improves the best known values of the grouping efficacy for 5 problems.

An Ant Colony Optimization Approach for the Machine-Part Cell Formation Problem

An Ant Colony Optimization Approach for the Machine-Part Cell Formation Problem

Team formation based on group technology: A hybrid grouping genetic algorithm approach

This paper presents a new model for team formation based on group technology (TFPGT). Specifically, the model is applied as a generalization of the well-known Machine-Part Cell Formation problem, which has become a classical problem in manufacturing in the last few years. In this case, the model presented is especially well-suited for problems of team formation arising in R&D-oriented or teaching institutions. A parallel hybrid grouping genetic algorithm (HGGA) is also proposed in the paper to solve the TFPGT. The performance of the algorithm is shown in several synthetic TFPGT instances, and in a real problem: the formation of teaching groups at the Department of Signal Theory and Communications of the Universidad de Alcalá in Spain.

An Improved PBIL Algorithm for the Machine-Part Cell Formation

The machine-part cell formation is a NP- complete combinational optimization problem. Past research has shown that although the genetic algorithm (GA) can get high quality solutions, special selection strategy, crossover and mutation operators as well as the parameters must be defined previously to solve the problem efficiently and flexibly. In this paper, an improved permutation code PBIL is adopted to solve the machine-part cell formation problem. Simulation results on five well known problems show that the PBIL can get satisfied solutions more simply and efficiently.

An ant colony optimization metaheuristic for machine–part cell formation problems

In this paper we propose an ant colony optimization metaheuristic (ACO-CF) to solve the machine–part cell formation problem. ACO-CF is a MAX – MIN ant system, which is implemented in the hyper-cube framework to automatically scale the objective functions of machine–part cell formation problems. As an intensification strategy, we integrate an iteratively local search into ACO-CF. Based on the assignment of the machines or parts, the local search can optimally reassign parts or machines to cells. We carry out a series of experiments to investigate the performance of ACO-CF on some standard benchmark problems. The comparison study between ACO-CF and other methods proposed in the literature indicates that ACO-CF is one of the best approaches for solving the machine–part cell formation problem.