Cell Formation Problem Research Articles

Optimization of the material handling costs and the machine reliability in cellular manufacturing system using cuckoo search algorithm

Lean manufacturing is a systematic method to improve productivity and reduce costs for each industrial. One of the tools for lean is the cellular manufacturing that divides the production system into smaller subsystems. The objective is to reduce inventory, setup times, and material handling costs. The manufacturing cell formation problem presents the first issue faced in designing a cellular manufacturing system. In this paper, the presence of alternative process routings and machine reliability in addition to production volume, operation sequence and production time makes the problem more complex but also more realistic and appealing to real-world applications. Most authors solve this kind of problems by mathematical programming approaches that require large amounts of computational efforts. Therefore, a meta-heuristic algorithm, entitled Hybrid cuckoo search for the generalized cell formation problem, is considered in this paper. A set of benchmark problems with various scales is used to further evaluate the performance of the proposed method. Comparison of the results obtained with those obtained by LINGO and by the simulated annealing method reveals the better effectiveness and efficiency of our method.

GRASP with path relinking for the manufacturing cell formation problem considering part processing sequence

In this study, a hybrid heuristic is proposed for the Manufacturing Cell Formation Problem considering part processing sequence. The hybrid heuristic is based on a GRASP heuristic and then it uses a path relinking strategy on a set of elite solutions obtained by GRASP. The proposed GRASP heuristic constructs feasible solutions using a two-stage randomised greedy procedure, that are improved with a local search procedure. This procedure explores two neighbourhoods: machine reassignments and machine interchanges. The path relinking is used as an intensification strategy to improve the GRASP solutions. The performance of the proposed heuristic is evaluated with data-sets from the literature and a set of larger size instances randomly generated. The obtained results show that the GRASP heuristic provides optimal or near optimal solutions with very small computational burden. These results are further improved with a Path Relinking strategy. The most remarkable features of the proposed heuristic are its simplicity and its ease of implementation.

Genetic Algorithm approach for Machine Cell Formation with Alternative Routings

In cellular manufacturing systems, study and optimization of machine cell formation (CF) problems have long drawn attention of researchers. Optimum CF results in reduction of overall processing time, material handling cost, labor cost, in-process inventories and number of set-ups requirements. Also, it simplifies process plans and improves product quality. Since the modern manufacturing machines in a cell are generally multifunctional, the processing of parts are performed following alternative processing routes. The objective of study is to determine the optimal alternative processing route in order to minimize the total intercellular movements of parts in CF problems. Intercellular movements of parts depend on many factors such as parts volume including batch size and number of batches, sequence of processes and routes of production. In this paper, a genetic algorithm heuristic is presented for the CF problem with multiple process routes, sequence of processes and parts volume. Computational experimentation was performed with five benchmark problems. The results demonstrate that the performance of the proposed approach in terms of total intercellular movements of parts and best route selection are either better or competitive with the well-known existing methods.

Voids Based Approach for Solving Cell Formation Problems

Cellular Manufacturing Systems is finding wide application in industries now-a-days. Cell Formation is the vital aspect of Cellular Manufacturing Systems design. Various Approaches are proposed by different authors to the Cell Formation problems using traditional and heuristic approaches. However some of those approaches have deficiencies and do not always produce proper machine cells and part families from the initial machine-component incidence matrix. In this paper, a new approach is proposed based upon voids. This approach is simple, will take less time and overcomes many limitations of some traditional based approaches. This new proposed method is tested against well known approach Rank Order Clustering (ROC) in solving Cell Formation Problems and test results will conclude that the approach is efficient

A Comparative Investigation of Similarity Coefficients Applied to the Cell Formation Problem using Hybrid Clustering Algorithms

Cellular manufacturing system plays a vital role in improving organizational productivity. The efficiency and effectiveness of machine-component cell formation of cellular manufacturing system are measured in terms of grouping efficiency and grouping efficacy, respectively. These measures are affected by inputs as well as methods used to form the machine-component cells. The input may be in the form of 0-1 matrix, which is called as machine-component incidence matrix or in terms of similarity coefficients among machines as well as among components. In this paper, the machine-component cell formation problem using “similarity coefficient” as an input is considered. The objective is to compare the effect of five different similarity coefficients on the grouping efficiency as well as on the grouping efficacy. Two different algorithms, viz. Hybrid Principle Component Analysis and Hybrid Agglomerative Clustering Algorithm are used to solve the machine-component cell formation problem. In each of these algorithms, Rank Order Clustering algorithm is used as a local search algorithm to form the final machine-component cells. A complete factorial experiment with 10 problem sizes, five different similarity coefficients, and two different algorithms is designed with two replications under each of the experimental combinations to check the main effects of the factors and their interaction effects on the grouping measures of the machine-component cell formation problem. The inferences of the complete factorial experiment are reported.

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.

Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information

The purpose of this paper is to advance the similarity coefficient method to solve cell formation (CF) problems in two aspects. Firstly, while numerous similarity coefficients have been proposed to incorporate different production factors in literature, a weighted sum formulation is applied to aggregate them into a nonbinary matrix to indicate the dependency strength among machines and parts. This practice allows flexible incorporation of multiple production factors in the resolution of CF problems. Secondly, a two-mode similarity coefficient is applied to simultaneously form machine groups and part families based on the classical framework of hierarchical clustering. This practice not only eliminates the sequential process of grouping machines (or parts) first and then assigning parts (or machines), but also improves the quality of solutions. The proposed clustering method has been tested through twelve literature examples. The results demonstrate that the proposed method can at least yield solutions comparable to the solutions obtained by metaheuristics. It can yield better results in some instances, as well.

A Tabu Search strategy to solve cell formation problem with ratio level data

A Tabu Search strategy to solve cell formation problem with ratio level data

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 New Mathematical Model in Cell Formation Problem with Consideration of Inventory and Backorder: Genetic and Particle Swarm Optimization Algorithms

Cell Formation (CF) is the initial step in the configuration of cell assembling frameworks. This paper proposes a new mathematical model for the CF problem considering aspects of production planning, namely inventory, backorder, and subcontracting. In this paper, for the first time, backorder is considered in cell formation problem. The main objective is to minimize the total fixed and variable costs, including the machine related costs, intercellular movements, deviation between the levels of cell utilizations, inventory, backorder, and sub-contracting costs. The presented mathematical model is validated using GAMS software, and various test problems are solved by Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) algorithm. The performance of the algorithms is compared with the results obtained by the GAMS. The results demonstrate, there is no significant difference between the results of algorithms. Finally, some sensitive analyses are carried out to analyze the effects of backorder and inventory holding costs.

Incorporating dynamic cellular manufacturing into strategic supply chain design

For increasing the efficiency of the supply chain (SC), it is necessary to take into account the interactions and relationships between the stages of procurement of raw materials, manufacturing the products, and distributing them. An integrated framework is proposed in this paper for companies interested in meeting the demand for different products in the customer zones by establishing a number of plants and distributors at the candidate sites and in having SC design with reconfiguration capability based on changes in demand and more proper economic opportunities. For this purpose, a geographically distributed cell design is proposed for the selection of the proper location for each of the facilities and the production process of the products. A mixed integer linear programming model is presented here for the integration of the sectors for procurement, production, and distribution of the products in the SC. In light of the NP-hard class of the cell formation problem, a new algorithm titled hybrid genetic ant lion optimization (HGALO) algorithm is presented for finding the optimal or near-optimal solutions. A comparison is also made here between the proposed algorithm and the genetic algorithm (GA) for demonstration of the efficiency of the proposed algorithm. The quality of the solutions generated based on the HGALO algorithm demonstrates the capability and effectiveness of the algorithm in finding high quality solutions.

An efficient exact model for the cell formation problem with a variable number of production cells

The Cell Formation Problem has been studied as an optimization problem in manufacturing for more than 90 years. It consists of grouping machines and parts into manufacturing cells in order to maximize loading of cells and minimize movement of parts from one cell to another. Many heuristic algorithms have been proposed which are doing well even for large-sized instances. However, only a few authors have aimed to develop exact methods and most of these methods have some major restrictions such as a fixed number of production cells for example. In this paper we suggest a new mixed-integer linear programming model for solving the cell formation problem with a variable number of manufacturing cells. The popular grouping efficacy measure is used as an objective function. To deal with its fractional nature we apply the Dinkelbach approach. Our computational experiments are performed on two testsets: the first consists of 35 well-known instances from the literature and the second contains 32 instances less popular. We solve these instances using CPLEX software. Optimal solutions have been found for 63 of the 67 considered problem instances and several new solutions unknown before have been obtained. The computational times are greatly decreased comparing to the state-of-art approaches.

Developing a cellular manufacturing model considering the alternative routes, tool assignment, and machine reliability

The cell formation (CF) is one of the most important steps in the design of a cellular manufacturing system (CMS), which it includes machines’ grouping in cells and part grouping as separate families, so that the costs are minimized. The various aspects of the problem should be considered in a CF. The machine reliability and the tool assigned to them are the most important problems which have to be modeled correctly. Another important aspect in CMS is material handling costs that they consist of inter-cell and intra-cell movement costs. Moreover, setup and tool replacement costs can be effective in CF decision making. It is obvious that CF cannot be completed without considering the number of demand. With considering of all of the above aspects, an extended linear integer programming is represented for solving the cell formation problem (CFP) in this study. The objective is to minimize the sum of inter-cell movement, intra-cell movement, tool replacement, machine breakdown, and setup costs. In the other terms, for states that cost of movement is higher than tool-changing cost, although a part can have the inter- and/or intra-cell movements, the model tries to find a solution which part is allocated to one cell and with changing the tools, processes of that part is completed. In addition, to validate the model and show its efficiency and performance, several examples are solved by branch and bound (B&B) method.

Integrated Dynamic Cell Formation-Production Planning: A New Mathematical Model

In this paper a dynamic cell formation problem is presented considering some new and special characteristics. The concept of machine requirement by lucky parts, the parts which are allowed to be produced in a specific period, is combined with the depreciable property of machines. Therefore, purchasing and selling of machines according to their book-value and generating income have been taken into account. This leads to a new vantage characteristic in cell formation where in each period we are only dealing with the types and number of machines required. The new mathematical model is presented and solved by exact and ant colony optimization methods for three problem sizes.

A matheuristic for the cell formation problem

In this paper we propose a GRASP matheuristic coupled with an Integer Programming refinement based on Set Partitioning to solve the Cell Formation Problem. We use the grouping efficacy measure to evaluate the solutions. As this measure is nonlinear, we propose a fractional Set Partitioning approach and its linearization. Our method is validated on a set of 35 instances from the literature. The experiments found four unknown solutions. For all instances with known optima, our method is able to determine the optimum solutions.

A bi-level mathematical programming for cell formation problem considering workers’ interest

A bi-level mathematical programming for cell formation problem considering workers’ interest

A clonal selection algorithm for the generalized cell formation problem considering machine reliability and alternative routings

The cell formation is the first step in the design of Cellular Manufacturing systems. It consists of grouping parts with similar processing needs into cells and identifying the set of machines needed to process these parts. The aim is to minimize the material handling costs and maximize the use of the machines. In this paper, the machine reliability and the alternative process routings are taken into account to form the production cells. The presence of these factors in addition to the production volume, operation sequence and production time makes the problem more realistic but also more complex. Most authors solve this kind of problems by mathematical programming approaches that require large amounts of computational efforts. Therefore, a modified version of the Clonal Selection Algorithm is introduced and a local search mechanism is adopted in this paper. The obtained results are compared with those of the Branch and Bound (B&B) method using LINGO software. The comparison reveals the effectiveness and the efficiency of the proposed method in terms of both solution quality and computation time required.