Cell Production System Design: A Literature Review

Purpose In a cell production system, a number of machines that differ in function are housed in the same cell. The task of these cells is to complete operations on similar parts that are in the same group. Determining the family of machine parts and cells is one of the major design problems of production cells. Cell production system design methods include clustering, graph theory, artificial intelligence, meta-heuristic, simulation, mathematical programming. This article discusses the operation of methods and research in the field of cell production system design. Methodology: To examine these methods, from 187 articles published in this field by authoritative scientific sources, based on the year of publication and the number of restrictions considered and close to reality, which are searched using the keywords of these restrictions and among them articles Various aspects of production and design problems, such as considering machine costs and cell size and process routing, have been selected simultaneously. Findings: Finally, the distribution diagram of the use of these methods and the limitations considered by their researchers, shows the use and efficiency of each of these methods. By examining them, more efficient and efficient design fields of this type of production system can be identified. Originality/Value: In this article, the literature on cell production system from 1972 to 2021 has been reviewed

A mathematical model in cellular manufacturing system considering subcontracting approach under constraints

In this paper, a new mathematical model in cellular manufacturing systems (CMSs) has been presented. In order to increase the performance of manufacturing system, the production quantity of parts has been considered as a decision variable, i.e. each part can be produced and outsourced, simultaneously. This extension would be minimized the unused capacity of machines. The exceptional elements (EEs) are taken into account and would be totally outsourced to the external supplier in order to remove intercellular material handling cost. The problem has been formulated as a mixed-integer programming to minimize the sum of manufacturing variable costs under budget, machines capacity and demand constraints. Also, to evaluate advantages of the model, several illustrative numerical examples have been provided to compare the performance of the proposed model with the available classical approaches in the literature

Petri Networks in the Planning of Discrete Manufacturing Processes

This chapter puts forward characteristics of selected issues of manufacturing processes planning using the Petri networks technique. It includes references to the extensive literature concerning the use of Petri networks in computer aided planning of discrete production processes. Diversity of these problems is high as it refers both to the methods of modeling and simulation of the course of manufacturing processes, the issue of optimizing these processes and production systems, representation of knowledge on production parts of equipment and integration of planning and production activities in general. The work puts forward example use of a temporary, priority Petri network for modeling and optimizing production systems and manufacturing operations as well as an example of fuzzy interference using the Petri network mechanism

An integrated model of cellular manufacturing and supplier selection considering product quality

Today’s business environment has forced manufacturers and plants to produce high-quality products at low cost and the shortest possible delivery time. To cope with this challenge, manufacturing organizations need to optimize the manufacturing and other functions that are in logical association with each other. Therefore, manufacturing system design and supplier selection process are linked together as two major and interrelated decisions involved in viability of production firm. As a matter of fact, production and purchasing functions interact in the form of an organization’s overall operation and jointly determine corporate success. In this research, we tried to show the relationship between designing cellular manufacturing system (CMS) and supplier selection process by providing product quality considerations as well as the imprecise nature of some input parameters including parts demands and defects rates. A unified fuzzy mixed integer linear programming model is developed to make the interrelated cell formation and supplier selection decisions simultaneously and to obtain the advantages of this integrated approach with product quality and consequently reduction of total cost. Computational results also display the efficiency of proposed mathematical model for simultaneous consideration of cellular manufacturing design and supplier selection as compared to when these two decisions separately taken into account

A Case Study for Financial Feasibility of Automated Costing Support in A Small Machine Shop

A knowledge-based cost estimating expert system is chosen by a Mexican machine shop. Differences between the traditional experience-based system employed and the automated system are studied. Data is gathered to analyze time effectiveness, accuracy and payback of the software. Data from seventy part models is recorded to study the time experiment, and data from fifty part models is used to study the accuracy and consistency. Data is analyzed by calculating mean, standard deviation, and test of hypothesis. The results indicate that the software is faster than the traditional quoting system; however, the payback point is high. Also, results show the software has a smaller average time-to-manufacture percentage difference between the automated system and the actual time-to-manufacture (TTM) compared to the percentage difference between the traditional’s TTM and actual TTMs, and this difference is statistically significant. The standard deviation for the automated system is also less implying better consistency

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154106/1/kampfner1998.pd

Optimal design of mesostructured materials under uncertainty

The main objective of the topology optimization is to fulfill the objective function with the minimum amount of material. This reduces the overall cost of the structure and at the same time reduces the assembly, manufacturing and maintenance costs because of the reduced number of parts in the final structure. The concept of reliability analysis can be incorporated into the deterministic topology optimization method; this incorporated scheme is referred to as Reliability-based Topology Optimization (RBTO). In RBTO, the statistical nature of constraints and design problems are defined in the objective function and probabilistic constraint. The probabilistic constraint can specify the required reliability level of the system. In practical applications, however, finding global optimum in the presence of uncertainty is a difficult and computationally intensive task, since for every possible design a full stochastic analysis has to be performed for estimating various statistical parameters. Efficient methodologies are therefore required for the solution of the stochastic part and the optimization part of the design process. This research will explore a reliability-based synthesis method which estimates all the statistical parameters and finds the optimum while being less computationally intensive. The efficiency of the proposed method is achieved with the combination of topology optimization and stochastic approximation which utilizes a sampling technique such as Latin Hypercube Sampling (LHS) and surrogate modeling techniques such as Local Regression and Classification using Artificial Neural Networks (ANN). Local regression is comparatively less computationally intensive and produces good results in case of low probability of failures whereas Classification is particularly useful in cases where the reliability of failure has to be estimated with disjoint failure domains. Because classification using ANN is comparatively more computationally demanding than Local regression, classification is only used when local regression fails to give the desired level of goodness of fit. Nevertheless, classification is an indispensible tool in estimating the probability of failure when the failure domain is discontinuous. Representative examples will be demonstrated where the method is used to design customized meso-scale truss structures and a macro-scale hydrogen storage tank. The final deliverable from this research will be a less computationally intensive and robust RBTO procedure that can be used for design of truss structures with variable design parameters and force and boundary conditions.M.S.Committee Chair: Choi, Seung-Kyum; Committee Member: Muhanna, Rafi; Committee Member: Rosen, Davi