Cell Load Variation Research Articles

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.

Calibration of GA Parameters for Layout Design Optimization Problems Using Design of Experiments

In manufacturing-cell-formation research, a major concern is to make groups of machines into machine cells and parts into part families. Extensive work has been carried out in this area using various models and techniques. Regarding these ideas, in this paper, experiments with varying parameters of the popular metaheuristic algorithm known as the genetic algorithm have been carried out with a bi-criteria objective function: the minimization of intercell moves and cell load variation. The probability of crossover (A), probability of mutation (B), and balance weight factor (C) are considered parameters for this study. The data sets used in this paper are taken from benchmarked literature in this field. The results are promising regarding determining the optimal combination of the genetic parameters for the machine-cell-formation problems considered in this study.

Nested Bi-level metaheuristic algorithms for cellular manufacturing systems considering workers’ interest

Due to the competitive nature of the market and the various products production requirements with short life cycles, cellular manufacturing systems have found a special role in manufacturing environments. Creativity and innovation in products are the results of the mental effort of the workforces in addition to machinery and parts allocation. Assignment of the workforce to cells based on the interest and ability indices is a tactical decision while the cell formation is a strategic decision. To make the correct decision, these two problems should be solved separately while considering their impacts on each other classically. For this reason, a novel bi-level model is designed to make decentralized decisions. Because of the importance of minimizing voids and exceptional element in the cellular manufacturing system, it is considered as a leader at the first level and the assignment of human resources is considered as a follower at the second level. To achieve product innovation and synergy among staff in the objective function at the second level, increasing the worker’s interest in order to cooperate with each other is considered too. Given the NP-Hard nature of cell formation and bi-level programming, nested bi-level genetic algorithm and particle swarm optimization are developed to solve the mathematical model. Various test problems have been solved by applying these two methods and validated results have been shown the efficiency of the proposed model. Also, real experimental comparisons have been presented. These results in contrast with previous works have been shown the minimum amount of computational time, cell load variation, total intercellular movements, and total intracellular movements of this new method. These effects have an important role in order to the improvement of cellular manufacturing behavior.

Acoustical Characteristics of Proton Exchange Membrane Fuel Cells

In order to optimise the performance of Proton Exchange Membrane Fuel Cells (PEMFCs) and to maximise their life cycle, water content has to be properly controlled. This paper presents a model-based method to determine the effect of load on PEMFC water content in PEMFCs using Acoustic Emission (AE) measurements. The developed model was implemented in COMSOL and verified using a single proton exchange membrane fuel cell (FC) operated under various loads. Acoustical events originating from water bubble formation have been identified and assessed using statistical parameters determined in the time and frequency domains. The feasibility of using AE techniques to detect and monitor the impact of a cell's load variation on water content is assessed. As the model results were in good agreement with experimental data, it was concluded that an AE based method could serve as an effective monitoring and control tool of water content in PEMFCs. Statically, the root mean square for acoustic emission activity has a significant relation with load characteristics and can, potentially, be a candidate for determining the AE behavior in a PEMFC.

A simulated annealing for the cell formation problem with ratio level data

In this paper, the cell formation problem is considered with ratio level data with an objective of minimising the cell load variation. The attempt has been made to propose a simulated annealing (SA) based on the perturbation scheme as random insertion perturbation scheme (RIPS). The ratio level data is distinguished by utilising the workload information gathered from process times, production quantity of parts and also from the capacity of the machines. A modified grouping efficiency (MGE) is used to measure the performance of the system. From the results it is observed that the simulated annealing produces the solution does not differ significantly from the optimal solutions for the benchmark problems. The algorithms which we have chosen the benchmark problems are K-means, modified ART1 and genetic algorithm taken from the literature.

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.

Machine cell formation using simulated annealing algorithm in cellular manufacturing system

Group technology is a production strategy and it has the ability to get solution for certain problems of complexity and long manufacturing lead times in batch manufacturing. This can be achieved by using a systematic method of equipments into machine cells and components into part families, based on the suitable similarity criteria. The present work deals with the machine cell formation problem with the aim of minimising the aggregate cell load variation and aggregate inter cellular move. The quantity of parts, operation sequences, processing time, capacity of machines, and workload of machineries were considered as parameters. In order to group the machineries, one of the metaheuristic search methodologies the simulated annealing (SA) algorithm is considered. For computational processing 40 machines and 100 part types are being used. From the findings it is observed that, in most of the literature problems SA provides better result than that of other methods in the literature and in few cases it yields equal result to the best one of the other methods.

Machine cell formation using simulated annealing algorithm in cellular manufacturing system

Group technology is a production strategy and it has the ability to get solution for certain problems of complexity and long manufacturing lead times in batch manufacturing. This can be achieved by using a systematic method of equipments into machine cells and components into part families, based on the suitable similarity criteria. The present work deals with the machine cell formation problem with the aim of minimising the aggregate cell load variation and aggregate inter cellular move. The quantity of parts, operation sequences, processing time, capacity of machines, and workload of machineries were considered as parameters. In order to group the machineries, one of the metaheuristic search methodologies the simulated annealing (SA) algorithm is considered. For computational processing 40 machines and 100 part types are being used. From the findings it is observed that, in most of the literature problems SA provides better result than that of other methods in the literature and in few cases it yields equal result to the best one of the other methods.

Sustainability Formation of Machine Cells in Group Technology Systems Using Modified Artificial Bee Colony Algorithm

The efficiency and sustainability of a cellular manufacturing system (CMS) in batch type manufacturing is highly valued. This is done using a systematic method of equipment into machine cells, and components into part families, based on the suitable similar criteria. The present work discusses the cell formation problem, with the objective of minimizing the cumulative cell load variation and cumulative intercellular moves. The quantity of parts, operation sequences, processing time, capacity of machines, and workload of machineries were considered as parameters. For the grouping of equipment, the modified artificial bee colony (MABC) algorithm is considered. The computational procedure of this approach is explained by using up to 40 machines and 100 part types. The result obtained from MABC is compared with the findings acquired from the genetic algorithm (GA) and ant colony system (ACS) in the literature.

A tabu search strategy to solve cell formation problem with ratio level data

This paper concentrates on the cell formation problem for the ratio level data to the design of cellular manufacturing system. The aim of this paper is to identify the machine cells and part family and as a result to create production cells in order to reduce the cell load variation. A competent Tabu Search (TS) algorithm is proposed to investigate the search space of all possible solutions with a chain of moves. This method is an iterative process for seeking a global optimum for the discrete combinatorial optimisation problems. The ratio level data is calculated in terms of time in seconds based on the data collected from the processing time of the part, production volume of the part and availability of the machine. A modified grouping efficiency (MGE) is used to compute the cell formation problem. The results clearly indicated that this proposed TS yield good results compared to the chosen benchmark problems.

A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty

Cell-load variation is considered as a major shortcoming in cellular manufacturing systems. It can cause long queues in front of machines and impose extra costs to the cellular layouts. In this paper the impact of inflation on cell-load variation in cellular manufacturing systems is examined. For this purpose, a new method is proposed for scheduling dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim is finding the trade-off values between in-house manufacturing and using outsource services while system costs are not deterministic and may be varied from period to period by inflation. To solve the model, a hybrid genetic and simulated annealing algorithms is developed because of the high potential of outcomes to be trapped in the local optima. The results are then compared in LINGO® 12.0 software. In continue a Taguchi method (an L9 orthogonal optimization) is used to estimate parameters of the proposed method in order to solve experiments derived from literature. Our findings show that the condition of dynamic costs affects the routing of materials in process and may induce machine-load variation that yield to cell-load diversity. An increase in changing costs causes the loading level of each cell to vary, which in turn results in the development of “complex dummy sub-cells.” To measure the level of cell-load variation a new mathematical index is developed. Then, a new method is proposed for minimizing cell-load variation in the mentioned condition by using control lines. The results indicate that the proposed method can significantly reduce the level of cell-load variation in CMS.

A multi-period scheduling method for trading-off between skilled-workers allocation and outsource service usage in dynamic CMS

In this paper, a new method is proposed for short-term period scheduling of dynamic cellular manufacturing systems in a dual resource constrained environment. The aim of this method is to find best production strategy of in-house manufacturing using worker assignment (both temporary and skilled workers) and outsourcing, while part demands are uncertain and can be varied periodically. For this purpose, a multi-period scheduling model has been proposed which is flexible enough to use in real industries. To solve the proposed problem, a number of metaheuristics are developed including Branch and Bound; a hybrid Tabu Search and Simulated Annealing algorithms and a hybrid Ant Colony Optimization and Simulated Annealing algorithms. A Taguchi method (L27 orthogonal optimisation) is used to estimate parameters of the proposed method in order to solve experiments derived from the literature. For evaluating the system imbalance in dynamic market demands, a new measuring index is developed. Our findings indicate that the uncertain market demands affects the part allocating which may induce workstation-load variations that yield to cell-load variation accordingly. To solve this problem, two methods are offered. The results show that promoting staff and using freezing technique are promising ways to reduce system imbalance while confronting with the mentioned condition. The outcomes also show the superiority of the proposed hybrid method in providing solutions with better quality.

A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost

In this paper, a new method is proposed for short-term period scheduling of dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim of this method is to find best production strategy of in-house manufacturing and outsourcing in small and medium scale cellular manufacturing companies. For this purpose, a multi-period scheduling model has been proposed which is flexible enough to be used in real industries. To solve the proposed problem, a number of metaheuristics are developed including Branch and Bound; Simulated Annealing algorithms; Fuzzy Art Control; Ant Colony Optimization and a hybrid Multi-layer Perceptron and Simulated Annealing algorithms. Our findings indicate that the uncertain condition of system costs affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. The results showed that the proposed method can significantly reduce cell load variation while finding the best trading off values between in-house manufacturing and outsourcing.

A new method for decreasing cell-load variation in dynamic cellular manufacturing systems

Cell load variation is considered a significant shortcoming in scheduling of cellular manufacturing systems. In this article, a new method is proposed for scheduling dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim of this method is to control cell load variation during the process of determining the best trading off values between in-house manufacturing and outsourcing. A genetic algorithm (GA) is developed because of the high potential of trapping in the local optima, and results are compared with the results of LINGO ® 12.0 software. The Taguchi method (an L_9 orthogonal optimization) is used to estimate parameters of GA in order to solve experiments derived from literature. An in-depth analysis is conducted on the results in consideration of various factors, and control charts are used on machine-load variation. Our findings indicate that the dynamic condition of product demands affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. An increase in product uncertainty level causes the loading level of each cell to vary, which in turn results in the development of “complex dummy sub-cells”. The effect of the complex sub-cells is measured using another mathematical index. The results showed that the proposed GA can provide solutions with limited cell-load variations.

Review of evolution of cellular manufacturing system’s approaches: Material transferring models

This paper presents a review of material transferring methods, related techniques, and their effects on cellular manufacturing systems (CMS). In-depth analysis has been conducted through a review of 95 dominant research papers available in the literature. The advantages, limitations, and drawbacks of material transferring methods have been discussed as well. The domains of the examined studies include some of the important problems in material transferring, such as exceptional elements, number of voids, machine distances, bottleneck machines and parts, machine location and relocation, part routing, cell load variation, inter and intracellular material transferring, cell reconfiguring, dynamic part demands, and operation and completion times. The results of this study can fill research gaps and clarify many related questions in CMS problems.

Evaluating impact of market changes on increasing cell-load variation in dynamic cellular manufacturing systems using a hybrid Tabu search and simulated annealing algorithms

In this paper, a new method is proposed for scheduling dynamic cellular manufacturing systems (D-CMS) in the presence of uncertain product demands. The aim of this method is to control the process of trading off between in-house manufacturing and outsourcing while product demands are uncertain and can be varied from period to period. To solve the proposed problem, a hybrid Tabu Search and Simulated Annealing are developed to overcome hardness of the proposed model and then results are compared with a Branch and Bound and Simulated Annealing algorithms. A Taguchi method (L_27 orthogonal optimization) is used to estimate parameters of the proposed method in order to solve experiments derived from literature. An in-depth analysis is conducted on the results in consideration of various factors. For evaluating the system imbalance in dynamic market demands, a new measuring index is developed. Our findings indicate that the uncertain condition of market demands affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. The results showed that the proposed hybrid method can provide solutions with better quality.

Proposal of a nonlinear multi-objective genetic algorithm using conic scalarization to the design of cellular manufacturing systems

This paper presents a nonlinear multi-objective mathematical model to obtain quality solutions for design problems of cellular manufacturing systems. The objectives of the multi-objective model are, simultaneously, (1) to minimize the number of exceptional elements among manufacturing cells, (2) to minimize the number of voids in a cell, and (3) to minimize cell load variation. In this paper, a new multi-objective genetic algorithm (GA) approach has been proposed to solve the multi-objective problem. In contrast to existing GA approaches, this GA approach contains some revised genetic operators and uses a conic scalarization method to convert the mathematical model’s objectives in a single objective function. This approach has been tested and compared with two test problems and some source models collected from the literature. The results have shown that the problem-solving performance of the proposed multi-objective approach is at least as good as the existing approaches in designing the cellular system, and in many cases better than them.

Optimization of Inter Cellular Movement of Parts in Cellular Manufacturing System Using Genetic Algorithm

In the modern manufacturing environment, Cellular Manufacturing Systems (CMS) have gained greater importance in job shop or batch-type production to gain economic advantage similar to those of mass production. Successful implementation of CMS highly depends on the determination of part families; machine cells and minimizing inter cellular movement. This study considers machine component grouping problems namely inter-cellular movement and cell load variation by developing a mathematical model and optimizing the solution using Genetic Algorithm to arrive at a cell formation to minimize the inter-cellular movement and cell load variation. The results are presented with a numerical example.

Fuzzy Demand Consideration In A Multi-objective Dynamic Cell Formation Problem Using A Robust Scatter Search

This paper presents a multi-objective cell formation problem considering alternative process routes and machine utilization with fuzzy demand. Two conflicting objectives include the total cell load variation and sum of the other costs consisting machine cost, inter-cell material handling cost, parts purchasing, operation, maintenance, and reconfiguration of machines cost are to be minimized simultaneously. Moreover, we consider demand in fuzzy condition, because it is more realistic to take into account the inexact and uncertain nature of demand. Due to the complexity of this problem, we develop a scatter search algorithm. Also by using the Taguchi as a robust parameter design method, we tune the effective factors of the developed algorithm on two sizes of benchmark problems that are generated randomly. NSGAII and Scatter Search evaluated and the related results confirm the efficiency and the effectiveness of our proposed Scatter Search provides good output according to some quality measures, especially for large-sized problems.

Some insight into Yasuda et al 's. “a grouping genetic algorithm for the multi-objective cell formation problem”

Yasuda et al. (2005, International Journal of Production Research, 43, 829–853) proposed an efficient method for solving the multi-objective cell formation problem. The objectives of their model are to minimize both the cell load variation and intercell flows. In this technical note, we point out and correct an error in their model. The efficacy of their grouping genetic algorithm is not influenced by the modified model.