The cellular manufacturing layout problem

In this paper we study a semiconductor packaging line at IBM Bromont. At the line, modules are assembled and then tested in a burn-in oven. The burn-in oven is a batch processing station. We outline a procedure to determine order release schedule and lot sizes for the various work stations in the line, such that total manufacturing lead time is minimized. The internal parameters of the procedure are set by simulation experiments and by heuristics. Sensitivity analysis is carried out to determine the robustness of the procedure with respect to various external parameter settings.

An evolutionary algorithm for manufacturing cell formation

This paper presents an algorithm for the design of manufacturing cells and part families. This algorithm is suitable for arriving at a good block diagonal structure for a cellular manufacturing design problem with part machine incidence matrix as input. The objective of this algorithm is the maximisation of grouping efficacy (GE), which is one of the most widely used measures of quality for cellular configurations. Assignment of machines to cells is using genetic algorithm, and part assignment heuristic is based on an effective customised rule. A comparison of the proposed algorithm is made with seven other methods of cell formation by taking 36 problems from the literature and found that the proposed algorithm is performing much better than the others. Finally, the algorithm is extended to form configurations with good GE when there are alternative routes.

This paper deals with the cell formation problem that is the first step in designing cellular manufacturing system (CMS). The previous methods that were based on the array-based clustering for solving the cell formation problem have used the part-machine incidence matrix (PMIM) which was indicated the set of machines for processing each part. The main problem of these methods is the grouping parts and machines regardless of production volume, operational sequences, production cost, inventory and other production system's limitations. In this paper we intend to implement the multiple attribute decision making (MADM) concepts for covering the cause of drawback in the previous array-based clustering methods by considering two definitions of the arrays in PMIM. Four methods are presented that two methods are related to the binary arrays in PMIM and other two methods are considered the operational time as well as the operational sequencing in the definition of the arrays in PMIM. The results of the presented methods that are compared with a well-known approach are also reported.

Cellular manufacturing (CM) is now an established international practice to integrate: equipment, people, and systems into 'focused factories', 'mini-businesses' or 'cells' with clear customers, responsibilities and boundaries. The major element in exploiting the benefits of CM is efficient layout designs. In this paper, problem of machine assignment is considered in a cellular layout. The focus is on the intra-cell machines allocation, because the inter-cell material flows should be minimal in CM. The modified artificial bee colony (MABC) algorithm is proposed to solve the layout problem. The algorithm tackles one cell at a time with a maximum of nine machines, which will be assigned into a 3 × 3 grid space according to the ranking result. This problem involves five parts and five quantitative demand periods are considered. To examine the performance of the proposed MABC, datasets are taken from the literature and the results are compared with those of other approaches. The proposed MABC provides an optimal layout exactly.

International Engineering Consortium predicted that cellular manufacturing would become the main production model of manufacturing enterprises in the future. In recent years, studies both at home and abroad were primarily around cellular manufacturing system design and equipment layout, while research on cellular manufacturing management pattern was relatively small. Based on elaborating the basic concepts and organizational forms of cellular manufacturing, this paper gives autonomous management models of cellular manufacturing, and proposes optimization management method, so as to expand production management theory and provide reference to cellular manufacturing enterprises.

This paper is directed toward the development of models, methods and tools suitable for effective, profitable, and autonomous cellular manufacturing as a new vision for 21st Century. The cellular manufacturing is mainly concerned with the efficient integration of three major concerns : (1) planning-oriented predictive cellular layout design; (2) an unexpected order-adapted cellular manufacturing through the integration between manufacturing decision processes and cellular layout, and (3) a contingency-driving shop floor adaptation for supporting continuous improvement. The system supports the user with five modules : real-time status monitoring, data acquisition and analysis, diagnosis through intelligent decision support system, simulation optimization, and results evaluation and performance measurement.

Most of the garment productions line behaves as one entity, empowerment, and team work are complicated to be promoted. Most of garment firms are international export oriented earnings in Tirupur. It plays an important contribution to state government budget. Weightless machines are needed to produce the garments in selected industry which is effortless to implement cellular manufacturing design layout. By implementing lean design and implementation of a valuable team work concept and cellular manufacturing layout design in selected garment firm to reduce motion of employee during working hours, defects of garment items and proper utilization of employee for achieving more earnings. This research article address the implementation of design of cellular manufacturing layout and a valuable team work concept in selected garment manufacturing firms.

The human factor in cellular manufacturing

Cellular manufacturing is a lean manufacturing approach that involves grouping machines, processes, and workstations into self-contained cells to reduce material handling, waiting times, and inventory. The layout design of cellular manufacturing facilities has a significant impact on their efficiency, productivity, and throughput. However, traditional trial-and-error methods for facility layout design can be time-consuming, costly, and risky. Digital twin simulation offers a powerful alternative by allowing designers to create virtual models of the facility and test different layout configurations before physically implementing them. Our study shows that digital twin simulation can help identify the optimal layout design that maximizes productivity, minimizes waste, and ensures the smooth flow of materials and products. Additionally, it enables us to evaluate the impact of changes in the production system on the facility's performance, helping decision-makers make informed decisions. Overall, this paper highlights the potential of digital twin simulations as a powerful tool for optimizing cellular manufacturing facility layout design and improving operational performance.

In this research, an evolutionary genetic algorithm based approach is proposed for designing independent machine cells in cellular manufacturing in the presence of operation based alternative process plans for parts. Manufacturing parameters, such as production volume, usage factor for process plans, machine flexibility and cell size and machine-operation compatibility in terms of a normalised rating factor are also considered during cell design. The problem is formulated as a multi-objective optimisation model and solved using a non-dominated sorting genetic algorithm (NSGA). A unique feature of the proposed method is that it takes into account the unequal efficiency of machines in performing different operations. Some new similarity and diversity measures among operations and machines are also proposed during the clustering of machines. The formation of machine cells has been treated as a minimisation of inter-cell traffic while maximising a defined efficiency function. An illustrated problem and comparisons with existing data are given.

Genetic algorithms for integrating cell formation with machine layout and scheduling

The phrase ‘rapid casting’ is being increasingly used to represent specific casting processes designed to reduce the total manufacturing lead time, by expediting selected critical steps. Rapid Prototyping (RP) is the means through which this is achieved, either directly or indirectly. There have been sporadic reports on this topic in the recent literature, but since the approach has the potential for considerable savings in production time, it offers opportunities for more design freedom in terms of patternless moulding, a scientific understanding of the process needs to be developed. This paper is an attempt in this direction, considering the rapid casting achieved by direct printing of sand moulds from Computer Aided Design (CAD) models, using 3D printing. The mould performance when used to cast light metals such as aluminium and magnesium under varying conditions is studied in terms of mechanical characteristics and surface quality of the castings. Taguchi L9 experimental design is used to consider the total number of factors and the size of the resulting experimental designs. The results indicate the suitability of patternless moulds for casting aluminium and magnesium alloys without any loss of essential characteristics, but the process variables have a complex influence on the overall outcome in each case and the best results can only be obtained by the optimum combination of factors in each alloy system.

A Combined Multicriteria Approach for Cellular Manufacturing Layout

Improvement of material handling and resources utilization for a small manufacturer: an application of cellular manufacturing layout and genetic algorithm-based scheduling