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

Reference architecture for configuration, planning and control of 21st century manufacturing systems.

Today's dynamic marketplace requires flexible manufacturing systems capable of cost-effective high variety - low volume production in frequently changing product demand and mix. Several new paradigms, e.g. holonic, fractal, biological and responsive manufacturing, have recently been proposed and studied in the academic literature. These 'next generation of manufacturing systems' have been especially designed to meet the requirements of an unstable and unpredictable marketplace. However, very little in-depth research of the configuration, planning and control methodologies of these new concepts has been conducted. This research aims to improve the comprehension and implementation of these 21st century manufacturing systems by developing an integrated reference architecture from the combination of their distinctive features that would enable manufacturing enterprises to handle successfully the configuration/reconfiguration, planning and control activities under the conditions of uncertainty and continuous change.In the course of the research, a detailed investigation into the fractal, biological and responsive manufacturing systems is conducted in order to identify the strengths and weaknesses of each concept. The common and distinctive features of the paradigms are then used to merge them to create an integrated reference architecture. The fractal configuration, biological scheduling and 'resource element' representation of resource capabilities and product processing requirements are selected as the major elements of the new system. A detailed study of fractal layout design resulted in seven distinctive methods for structuring and managing fractal cellular systems. A design methodology that supports three types of dynamic scheduling is developed for biological manufacturing systems. Resource elements are used with fractal layouts and biological scheduling to enhance performance and to enable an integration of the concepts. The proposed reference architecture is modelled and evaluated using object-oriented programming, computer simulation and heuristic algorithms. The research results indicate that the performance of systems that employ biological scheduling and fractal layouts can be improved by using the concept of resource elements to utilise any hidden capabilities of resources and to achieve an optimal distribution of resources on the shop floor

Virtual production system

To satisfy customer\u27s demands in today\u27s market, industry and academe have invested considerable effort to make production systems more efficient and competitive. The production systems that have been implemented and identified in industry have their own unique advantages under certain conditions. In practice, once a production system is adopted in a shop, the operation mode of the shop will remain the same over time. However, in a changing product mix environment, what a shop really needs is an adaptable production system to gain the best performance that is possible for the shop;The objective of this study is to develop a systematic procedure to construct a virtual production system that allows an existing shop to switch its operation from one mode to another without physical reconfiguration of the shop. The machines and the material handling system of the shop are logically reorganized into various patterns to obtain different versions of virtual production systems. Actually, a virtual production system exists as a set of information in a computer database. A reconfiguration of the data in the database leads to a corresponding logical reorganization of the physical system. Hence, on one hand, while the layout of a shop still remains the same, on the other hand, the operation mode of the shop is logically changeable over time;In this thesis, the performance of virtual production systems and other forms of production systems are examined and compared to one another using three different measures. The results obtained show that virtual production systems are superior to traditional production systems and are competitive with production systems that allow for the physical rearrangement of the machines as the product mix changes. However, when one considers the cost that can be incurred to physically reconfigure a shop and the fact that movable machines are not usually employed in most industries, virtual production system provides a feasible and reasonable solution to improve a shop\u27s performance in a dynamic changing product mix environment

Application of genetic algorithms to group technology.

Lee Wai Hung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 108-115).Chapter 1 --- Introduction --- p.8Chapter 1.1 --- Introduction to Group Technology --- p.8Chapter 1.2 --- Cell design --- p.9Chapter 1.3 --- Objectives of the research --- p.11Chapter 1.4 --- Organization of thesis --- p.11Chapter 2 --- Literature review --- p.13Chapter 2.1 --- Introduction --- p.13Chapter 2.2 --- Standard models --- p.14Chapter 2.2.1 --- Array-based methods --- p.16Chapter 2.2.2 --- Cluster identification --- p.16Chapter 2.2.3 --- Graph-based methods --- p.17Chapter 2.2.4 --- Integer programming --- p.17Chapter 2.2.5 --- Seed-based --- p.18Chapter 2.2.6 --- Similarity coefficient --- p.18Chapter 2.2.7 --- Artificial intelligence methods --- p.19Chapter 2.3 --- Generalized models --- p.19Chapter 2.3.1 --- Machine assignment models --- p.20Chapter 2.3.2 --- Part family models --- p.20Chapter 2.3.3 --- Cell formation models --- p.21Chapter 3 --- Genetic cell formation algorithm --- p.22Chapter 3.1 --- Introduction --- p.22Chapter 3.2 --- TSP formulation for a permutation of machines --- p.23Chapter 3.3 --- Genetic algorithms --- p.26Chapter 3.3.1 --- Representation and basic crossover operators --- p.27Chapter 3.3.2 --- Fitness function --- p.28Chapter 3.3.3 --- Initialization --- p.29Chapter 3.3.4 --- Parent selection strategies --- p.30Chapter 3.3.5 --- Crossover --- p.31Chapter 3.3.6 --- Mutation --- p.37Chapter 3.3.7 --- Replacement --- p.38Chapter 3.3.8 --- Termination --- p.38Chapter 3.4 --- Formation of machine cells and part families --- p.39Chapter 3.4.1 --- Objective functions --- p.39Chapter 3.4.2 --- Machine assignment --- p.42Chapter 3.4.3 --- Part assignment --- p.43Chapter 3.5 --- Implementation --- p.43Chapter 3.6 --- An illustrative example --- p.45Chapter 3.7 --- Comparative Study --- p.49Chapter 3.8 --- Conclusions --- p.50Chapter 4 --- A multi-chromosome GA for minimizing total intercell and intracell moves --- p.55Chapter 4.1 --- Introduction --- p.55Chapter 4.2 --- The model --- p.57Chapter 4.3 --- Solution techniques to the workload model --- p.61Chapter 4.3.1 --- Logendran's original approach --- p.62Chapter 4.3.2 --- Standard representation - the GA approach --- p.63Chapter 4.3.3 --- Multi-chromosome representation --- p.65Chapter 4.4 --- Comparative Study --- p.70Chapter 4.4.1 --- Problem 1 --- p.70Chapter 4.4.2 --- Problem 2 --- p.71Chapter 4.4.3 --- Problem 3 --- p.75Chapter 4.4.4 --- Problem 4 --- p.76Chapter 4.5 --- Bi-criteria Model --- p.79Chapter 4.5.1 --- Experimental results --- p.85Chapter 4.6 --- Conclusions --- p.85Chapter 5 --- Integrated design of cellular manufacturing systems in the presence of alternative process plans --- p.88Chapter 5.1 --- Introduction --- p.88Chapter 5.1.1 --- Literature review --- p.90Chapter 5.1.2 --- Motivation --- p.92Chapter 5.2 --- Mathematical models --- p.93Chapter 5.2.1 --- Notation --- p.93Chapter 5.2.2 --- Objective functions --- p.95Chapter 5.3 --- Our solution --- p.96Chapter 5.4 --- Illustrative example and analysis of results --- p.98Chapter 5.4.1 --- Solution for objective function 1 --- p.101Chapter 5.4.2 --- Solution for objective function 2 --- p.102Chapter 5.5 --- Conclusions --- p.103Chapter 6 --- Conclusions --- p.104Chapter 6.1 --- Summary of achievements --- p.104Chapter 6.2 --- Future works --- p.10

Design Methodologies Towards a Sustainable Manufacturing Enterprise

Sustainability is increasingly becoming a crucial concern in many aspects of life. Even though, there is a relatively growing interest from both academic researchers and practitioners in various design aspects of sustainability, one can see that design issues of sustainable manufacturing systems have not received adequate attention. Through an extensive literature review on design for sustainability and sustainability issues, it is observed that, attaining sustainability in manufacturing needs a huge amount of effort and needs to take into consideration many aspects from different perspectives. These include considering the sustainability in both the closed loop supply chain (CLSC) and the manufacturing system levels simultaneously, considering Cellular Manufacturing Systems (CMSs), considering reconfigurability for the production systems, considering Hybrid Manufacturing-Remanufacturing Systems as well as considering the recovery options such as recycling and remanufacturing. This research presents a simultaneous investigation of Reconfigurable Cellular Manufacturing Systems and Hybrid Manufacturing-Remanufacturing Systems (HMRSs), and proposes an integrated approach in design optimization, analysis, and process planning aspects as an attempt to address to a large number of design issues for Sustainable Manufacturing Systems, while the options of remanufacturing, recycling, and disposing are introduced. Four mathematical model have been developed. Third part cellular remanufacturing systems design are considered within the first model, which is initially formulated as a mixed integer non-linear program that incorporates multi-period production planning, dynamic system reconfiguration, and workforce management with deterministic production requirements. It consists the costs of machines maintenance and overhead, relocation costs for machines installation and removal, part holding cost, workers’ costs of salary, hiring, and firing, part intercellular movement cost, machine procurement cost, internal production cost, machine operating cost, the cost of acquiring the returned products, setup cost for disassembly operations, disassembly cost, the inventory cost of the returned products, parts disposal cost. Linearization procedures are proposed to convert it into a linearized mixed integer programming formulation. This linearized mixed integer program is solved using an exact solution (ES) procedure through the simplex-based branch and cut procedure of CPLEX software. The second model considered the design of cellular hybrid manufacturing-remanufacturing system, where manufacturing new products using an outsourced parts and remanufacturing using returned products are performed in the same facility by using shared resources. The overall objective of the model is to minimize the total cost of the three main categories of costs; 1) Machine cost: maintenance and overhead costs, relocation costs of installation and removal of machines, machine procurement costs, and machine operating costs, 2) Costs associated with manufacturing and remanufacturing: production costs for both new and remanufactured components, holding cost for new components, holding cost for remanufactured components, setup cost for new components, setup cost for remanufactured components, 3) Costs associated with returned products for remanufacturing: cost of acquiring the returned products, setup cost for disassembly operations, disassembly cost, and inventory cost of the returned products. Computational results and sensitivity analysis for an important design features are also reported. The third model addresses the same attributes as the second one but an important extension is the introduction of recycling (for the end-of-life parts) and disposing of the parts with no further use. In addition, the new parts production in the third model are totally depends on the recycled parts coming from the recycling center, wherein the second model it depends on the raw material purchasing from outsourcing. As the third model is the most comprehensive one, which considers a closed loop supply chain starts from a cellular hybrid manufacturing-remanufacturing system and ends with the customer zone, through the introducing of different centers like, collection, disassembly, and recycling centers, and in order to have one more step toward the design of sustainable closed loop supply chain, the fourth model are formulated. The fourth model is designed to minimize the carbon foot prints and the total cost which contains the opening costs for different centers and the transportation costs between these centers Keywords: Sustainability, Sustainable manufacturing system, cellular manufacturing systems design, Reconfigurable manufacturing system, mixed integer programming, Hybrid manufacturing-remanufacturing system, Closed loop supply chain, Reverse logistics, Carbon footprints, Facility location

Aplicación de la Búsqueda Armónica para el problema de formación de celdas de manufactura

Introduction: Cellular Manufacturing (CM) is an application of group technology that consists of grouping components in part families and machines into cells, via the decomposition of a complex manufacturing system into small systems, which attend the operations of entire part families. In this work, we developed a linear programming model that integrates production costs with costs for transfers between cells. Besides, using an approach algorithm method called Harmony Search is solved the mathematical model. Objective: Evaluate the performance of the alternative Harmony Search and its machine allocation strategies in a cellular manufacturing problem. Method: The mathematical model consists in a linear programming structure in which there are binary variables to determine the assignment of the operations of products to different machines in diverse cells, and integer variables to count the requirements of machines and the number of transfers between cells. In order to validate the model, we use modified instances based on the literature review and set in GAMS software using the CPLEX solver, also, is developed a metaheuristic algorithm in MATLAB in order to give an approximate solution. Results:  The proposed Harmony Search and its variants can provide highlighted results taking advantage of the exploitation approach of the search space. Conclusions: The Harmony Search and its variants can provide outstanding solutions in considerably short times; nevertheless, it is necessary to implement strategies to explore the search space in order to avoid falling into local optima.Introducción: La manufactura celular (MC) es una aplicación de la tecnología de grupos que consiste en la agrupación de familias de productos y la formación de familias de máquinas, mediante la descomposición de un sistema de manufactura complejo en subsistemas que atienden las operaciones de familias enteras de productos. Durante el presente trabajo se desarrolla un modelo de programación lineal entera que integra costos de producción con costos por transferencias entre celdas, además, se propone como método de solución un algoritmo denominado Búsqueda Armónica Objetivo: Determinar el desempeño de la Búsqueda Armónica modificada y las variantes de asignación de máquinas al problema de formación de celdas de manufactura. Metodología: Se desarrolla un modelo matemático de programación entera, el cual utiliza variables binarias para determinar la asignación de las operaciones de diversos productos a distintas máquinas en diferentes celdas, y variables enteras para cuantificar los requerimientos de máquinas y la cantidad de transferencias entre celdas. La validación del modelo se hace utilizando instancias modificadas de la literatura en el software GAMS usando el solver CPLEX y se desarrolla en MATLAB el algoritmo metaheurístico para dar solución aproximada. Resultados: Se encuentra que las variantes propuestas integradas en la Búsqueda Armónica logran buenos resultados aprovechando el enfoque de explotación del espacio de búsqueda. Conclusiones: A partir de las variantes aplicadas se logró encontrar muy buenas soluciones en tiempos considerablemente cortos; no obstante, es necesario implementar estrategias de exploración del espacio de búsqueda con el fin de evitar caer en óptimos locales

Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold: Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction

Bi-Level Mathematical Modelling and Heuristics for Cellular Manufacturing Facility Layout Problem

In this thesis, a bi-level mixed-integer non-linear programming continuous model has been, developed for both intra-cell and inter-cell layout design sequentially. Facilities are assumed unequal sizes, and operation sequences and part demands are considered. The model includes overlap elimination, aisle, and block constraints. Since the model is nonlinear, the model has been linearized and solved exact. However, the facility layout problem is NP-hard; hence, novel heuristics and a meta-heuristic have been designed and implemented to solve the problem in a similar manner- both at intra- and inter-cellular levels. A real case study from the metal cutting inserts industry has been used where multiple families of inserts have been formed each with its distinguished master plan. C++ has been used for implementation of the algorithms. For mathematical programming, the model is being solved by the Xpress optimization tool using a branch-and-bound method to illustrate the performance of the model