Virtual Cellular Manufacturing Systems Research Articles

Designing a virtual cellular manufacturing system with route selection and workers’ considerations: A multi-objective robust possibilistic model

In today’s economy, the necessity for responsiveness to variations in customers’ permanent requirements is increasing significantly. Achieving immediate response or success in the competitive market requires boosting flexibility, agility, and efficiency in utilizing available production equipment and workforce. Implementing a Virtual Cellular Manufacturing System (VCSM) prevents the reconfiguration costs and is suitable for many systems where the machines are not removable. Owing to the significant role of human resources in VCMS, this paper assigns the workers based on their skills, interest in cooperation, and workload balancing among cells. The considerable novelty is balancing physical and time workloads simultaneously. Another strength of this research is the consideration of various routes to process each part. A comprehensive linear multi-objective mathematical model is developed for a virtual cellular manufacturing system. This paper develops the model by stating social and technical dimensions, which minimize exceptional elements and voids, maximize the workers' interest in cooperation, and minimize workload deviation. Stemming from uncertain parameters, a robust possibilistic approach is addressed. Due to the multi-objective model, the multi-choice meta-goal programming with the utility function is applied. Afterward, a real-case study is presented to validate the model. Eventually, the outcomes and sensitivity analysis are brought up.

Dynamic virtual cellular reconfiguration for capacity planning of market-oriented production systems

<p style='text-indent:20px;'>Market-oriented production systems generally have the characteristics of multi-product and small-batch production. Dynamic virtual cellular manufacturing systems create virtual manufacturing cells periodically in a planning horizon to respond to changing demands flexibly and quickly, and thus are suitable for production planning problems of market-oriented production systems. In the current research, we propose a dynamic virtual cell reconfiguration framework under a dynamic environment with unstable demands and multiple planning cycles. In this framework, we formulate a two-phase dynamic virtual cell formation (DVCF) model. In the first phase, the proposed model aims to maximize processing similarity and balance the workload in the system. In the second phase, we consider the objective of reconfiguration stability based on the first phase model. To address the proposed model, we design a hybrid metaheuristic named Lévy-NSGA-Ⅱ, and perform various computational experiments to examine the performance of the proposed algorithm. Results of experiments indicate that the proposed Lévy-NSGA-Ⅱ based algorithm outperforms multi-objective cuckoo search (MOCS) and NSGA-Ⅱ in solution quality and optimal solution size.</p>

An Integrated Simulation and Virtual Cellular Manufacturing System Concept Approach for Maintenance Policy Selection

The main purpose of this study is to present a new hybrid approach to select the appropriate maintenance policy (NET) of machines utilizing analytic network process (ANP), computer simulation, and the concept of virtual cellular manufacturing system (VCMS). Since conventional methods select only one NET policy for all machines or production lines, the performance of machines that do not conform to the selected policy is reduced. In the proposed method, the information of the functional parameters of the machines is extracted by means of computer simulation and there is no need for expert opinion. Next, the appropriate net policy for each machine is selected using the ANP method. To reduce the diversity and complexity of NET applications, machines are grouped using the concept of virtual cellular manufacturing system based on the similarity of NET policies. A NET program is prepared for each group of machines. The proposed approach is used in a production unit with three widely used NET policies, and its efficiency was proved by comparing the results with conventional methods.

Modeling and Simulation on Error Spreading Over Partially and Directionally Interconnected Networks of Virtual Cellular Manufacturing System

Networks constituting virtual cellular manufacturing system (VCMS) cells are partially interconnected while the interlinks are directional and the interlink numbers between cells are unequal, which was not considered in previous studies. In this study, a model based on susceptible–infectious–removed–susceptible was developed to analyze error spreading over an interconnected network, and theoretical analysis was performed. Based on simulations, we found the following: (i) random interlinks between subnetworks insignificantly affected the interconnected network; (ii) a large interlinking propagation efficiency raised the infection probability of the interconnected network of VCMS; (iii) the topological structures of scale-free subnetworks shaped by cells could inhibit infection propagation. Using two real cases, we verified the conclusions drawn from the error spreading model. This study provides sound foundations to optimize the structural constitution of a VCMS during its cell formation and arrangement periods.

Reconfiguration of Virtual Cellular Manufacturing Systems via Improved Imperialist Competitive Approach

This paper constructs an integrated virtual reconfiguration model that can simultaneously group workstations, schedule virtual cells, and select energy consumption levels. If managers prefer the physical proximity of machines in a certain virtual cell, the material handling cost in it can be reduced. However, the distances among machines in other virtual cells are probably large, which may cause greater material handling cost. If a virtual cell for a certain product type has priority to be created, the backorder cost of that type can be decreased or even avoided. Nevertheless, the creation of virtual cells for other product types may be delayed, perhaps leading to the higher backorder cost of other product types. In addition, managers can choose a high energy consumption level of a machine to expedite its production to reduce backorder cost at the expense of more energy consumption cost. To minimize the total operational cost, we develop a novel Discrete Imperialist Competitive Algorithm with a Priority rule-based heuristic (DICAP). It includes a colony movement strategy, a competition strategy, a collapse mechanism, a development strategy, and a sufficient convergence policy. Numerical experiments and t-test are conducted to validate that the proposed DICAP outperforms genetic algorithm and simulated annealing. Note to Practitioners —Virtual cellular manufacturing systems can create an expectation of improving machine utilization and productivity, reducing reconfiguration cost, and adaptation to product specification changes in reality. However, managers often feel difficult to make appropriate decisions for three interrelated issues, i.e., workstation grouping, virtual cell creation and release, and energy consumption options. Workstation grouping depends on the availability of machines and workers, and the availability may be related to the creation and release time of some virtual cells. Whether a virtual cell is created in time has an influence on the selection of energy consumption levels, because managers need to consider both backorder cost and energy consumption cost. In addition, there often exists a bottleneck workstation in each virtual cell. Therefore, it is essential and worthwhile to select appropriate energy consumption levels of machines to smooth the production efficiency of grouped workstations. To effectively address the issues, this paper presents an integrated virtual reconfiguration model. A novel Discrete Imperialist Competitive Algorithm with a Priority rule-based heuristic is developed to minimize the total operational cost. Numerical experiments and t-test results indicate that the proposed algorithm outperforms two commonly used ones, i.e., genetic algorithm and simulated annealing in solution quality. It is suitable for virtual reconfiguration problem with industrial size in practice.

Robustness analysis of partially interdependent networks with different coupling preferences and multicluster functional nodes in VCMS

Abstract Networks shaped by different cells of a Virtual Cellular Manufacturing System (VCMS) are partially interdependent, and some nodes not belonging to the Global Giant Component (GGC) can still remain functional. In this case, a model of a VCMS network is partly different from or beyond the consideration of those in past studies. In this paper, the robustness of the interdependent network is explored, which is more similar to that of a VCMS. A demonstrative process is provided, and the cascading process of a network under random attacks is analyzed. Numerical simulations for three types of coupled networks [BA (Barabasi and Albert)–BA, BA–WS (Watts and Strogatz), and WS–WS] are performed to study the relationship of the robustness with the coupling strength and the GGC coefficient. Some findings are summarized as follows: (i) increasing the coupling strength between subnetworks may decrease the robustness of the interdependent network and lead to a transition from a second-order phase to a first-order phase; (ii) a small GGC coefficient can enhance the robustness of the interdependent network, and gradually increasing the GGC coefficient can result in a second-order phase transition, hybrid phase transition, and first-order phase transition; and (iii) the WS–BA coupled network is more vulnerable than the BA–BA network, but is more robust than the WS–WS network.

Scheduling of Virtual Cellular Manufacturing Systems: A Biogeography-Based Optimization Algorithm

ABSTRACTVirtual cellular manufacturing system (VCMS) is one of the modern strategies in the production facilities layout, which has attracted considerable attention in recent years. In this system, machines are located in different positions on the shop floor and virtual cells are a logical grouping of machines, jobs, and workers from the viewpoint of the production control system. These features not only enhance the system’s agility but also allow a dynamic reassignment of cells as demand changes. This paper addresses the VCMS scheduling problems where the jobs have different orders on machines and the objective is to simultaneously minimize the weighted sum of the makespan and total traveling distance in order to create a balance between criteria. The research methodology firstly consists of a mathematical programming model with regard to the production constraints in order to describe the characteristics of the VCMS. Secondly, a basic genetic algorithm (GA), a biogeography-based optimization (BBO) algorithm, an algorithm based on hybridization of BBO and GA, and the BBO algorithm accompanied by restart phase are developed to solve the VCMS scheduling problems. The developed algorithms have been compared to each other and their performance are evaluated in terms of their best solution and computational time as effectiveness and efficiency criteria, respectively. Consequently, the performance of the best algorithm has been evaluated by the state-of-the-art algorithm, GA, in the literature. The results show that the best algorithm based on BBO could find solutions at least as good as the last famous algorithm, GA, in the literature.

Integrating multi-dynamic virtual cellular manufacturing systems into multi-market allocation and production planning

This article presents a two-stage approach to solve a multi-dynamic virtual cellular manufacturing system in multi-market allocation and production planning. The first stage identifies the plant locations and grouping parts–machines–workers’ assignment to virtual cells, while the second stage determines the production volume for each plant, the allocated amounts to each market and the operations’ allocation to the virtual cells. The goal of the proposed mathematical model is to minimize total expected cost consisting of holding, outsourcing, inter-cell material handling, external transportation, fixed cost for producing each part in each plant as well as machine and labour salaries. It is assumed that the parts’ processing times on each machine with workers as well as the products’ demands are stochastic and described by discrete scenario enrichment using the sample average approximation and Latin hypercube sampling techniques. To solve such a stochastic model, an efficient method based on the genetic alg...

Cell Forming and Cell Balancing of Virtual Cellular Manufacturing Systems with Alternative Processing Routes Using Genetic Algorithm

Cellular manufacturing (CM) is one of the most important subfields in the design of manufacturing systems and as a recently emerged field of study and practice, virtual cellular manufacturing (VCM) inherits the importance from CM. One type of VCM problems is VCM with alternative processing routes from which the route for processing each part should be selected. In this research, a bi-objective mathematical programming model is designed in order to obtain optimal routing of parts, the layout of machines and the assignment of cells to locations and to minimize the production costs and to balance the cell loads. The proposed mathematical model is solved by multi-choice goal programming (MCGP). Since CM models are NP-Hard, a genetic algorithm (GA) is utilized to solve the model for large-sized problem instances and the results obtained from both methods are compared. Finally, a conclusion is made and some visions for future works are offered.Cellular manufacturing (CM) is one of the most important subfields in the design of manufacturing systems and as a recently emerged field of study and practice, virtual cellular manufacturing (VCM) inherits the importance from CM. One type of VCM problems is VCM with alternative processing routes from which the route for processing each part should be selected. In this research, a bi-objective mathematical programming model is designed in order to obtain optimal routing of parts, the layout of machines and the assignment of cells to locations and to minimize the production costs and to balance the cell loads. The proposed mathematical model is solved by multi-choice goal programming (MCGP). Since CM models are NP-Hard, a genetic algorithm (GA) is utilized to solve the model for large-sized problem instances and the results obtained from both methods are compared. Finally, a conclusion is made and some visions for future works are offered.

A genetic algorithm for a bi-objective mathematical model for dynamic virtual cell formation problem

Nowadays, with the increasing pressure of the competitive business environment and demand for diverse products, manufacturers are force to seek for solutions that reduce production costs and rise product quality. Cellular manufacturing system (CMS), as a means to this end, has been a point of attraction to both researchers and practitioners. Limitations of cell formation problem (CFP), as one of important topics in CMS, have led to the introduction of virtual CMS (VCMS). This research addresses a bi-objective dynamic virtual cell formation problem (DVCFP) with the objective of finding the optimal formation of cells, considering the material handling costs, fixed machine installation costs and variable production costs of machines and workforce. Furthermore, we consider different skills on different machines in workforce assignment in a multi-period planning horizon. The bi-objective model is transformed to a single-objective fuzzy goal programming model and to show its performance; numerical examples are solved using the LINGO software. In addition, genetic algorithm (GA) is customized to tackle large-scale instances of the problems to show the performance of the solution method.

Revised multi-choice goal programming for incorporated dynamic virtual cellular manufacturing into supply chain management: A case study

In this paper, a bi-objective optimization model is proposed for a dynamic virtual cellular manufacturing system and supply chain design. The presented model considers important manufacturing features thoroughly such as multi-plants and facility locations, multi-markets allocations, multi-period production planning under uncertain demand of products and capacities of resources. The proposed model is converted into an equivalent auxiliary crisp model. Then, to solve the model, a revised multi-choice goal programming approach is applied for finding a favorite solution. The aims of the proposed model are to minimize the total cost of supply chain design which includes holding cost, outsourcing cost, maintenance and overhead cost of machines, fixed cost, external transportation cost and minimizing the total number of exceptional elements and movements of the labors between active plants. The Computational results are explained in terms of a real case study as well as the validity of the proposed model solution method.

Design Policies in Virtual Cellular Manufacturing Systems by Multi-Domain Simulation Environment

In today’s competitive market, customer demand is characterized by high fluctuations and it is more difficult to forecast. Therefore, the classical cellular manufacturing systems need to be revised in order to stay competitive. Among the different approaches to support the cellular manufacturing systems, the concept of virtual cellular manufacturing systems can improve the performance in turbulent environment. This paper proposes two policies to support the problem of the reconfiguration of machines in a virtual cellular manufacturing system. The policies proposed are tested developing a multi-domain simulation environment in ExtendSim®. The experiments conducted show how the proposed approach improves the performance of the manufacturing systems especially when some disturbances occur as demand fluctuations, machine failures and processing time are uncertain.

Coordination mechanism in real-time scheduling of Virtual Cellular Manufacturing Systems

In recent years, Virtual Cellular Manufacturing Systems, which combine virtual cells to construct new manufacturing systems in response to changing market dynamics, have attracted considerable attention. Especially, the real-time scheduling of Virtual Cellular Manufacturing Systems has been of significant importance. In this article, a hybrid control architecture based on multi-agent technology is introduced to support the coordination mechanism to schedule tasks and resources in real-time. Using the revised resource element approach with the pheromone concept, the real-time scheduling problem is divided into two sub-problems, that is, task ranking and resource load-balancing, which can be solved by explicit and implicit coordination mechanisms, respectively. Finally, the proposed coordination mechanism is proven in illustrative examples to perform more efficiently with reduced processing and holding costs.

An application of analytic network process for the selection of cellular manufacturing systems

The ongoing research in the field of cellular manufacturing systems (CMS) is progressing on diverse fronts such as dynamic CMS, virtual CMS, etc. However, the following fundamental issues – ‘how an operations manager in an organisation would make a decision of choosing/selecting CMS, when he/she is confronted with different alternatives?’, ‘how would the manager make a decision, if the decision factors/criteria/attributes (for the sake of simplicity, it will be called as ‘elements’) are inter-related?’ are not given adequate importance. To address the above-mentioned issues, a hypothetical case study is utilised to demonstrate how the managers’ can make such a strategic decision using the analytic network process (ANP), one of the recently developed multi-criteria decision-making (MCDM) models that has the capability to model the relationships that exist within and between different categories comprising of various decision elements. The results obtained revealed that CMS is a better alternative under the given case situation.

Virtual Cellular Multi-period Formation under the Dynamic Environment

Virtual cellular manufacturing is an innovative way of production organization which both in the production of flexibility and efficient to meet today's rapid development of science and technology and replacement of products. The key process of the design of virtual cellular manufacturing system—cell formation is the focus of research. In order to meet the characteristics of small batch and dynamically changing market demand, this paper studies the problems of virtual cellular multi-period dynamic reconfiguration. A reconfigurable system programming model is developed. The model incorporates parameters of the problems of product dynamic demand, machine capacity, operation sequence, balanced workload, alternative routings and batch setting. The objective of mixed integer programming model is to minimize the total costs of operation, moving raw materials, inventory holding and process routes setup. Though a case study, demonstrates the feasibility and validity of the model in reality.

A Methodology Based on Resource Elements for Equipment Capability Analysis Oriented Virtual Cellular Manufacturing Systems

This paper analyzed the demand for equipment capability analysis in virtual cellular manufacturing systems, and then recognized that the traditional methodologies for equipment capability analysis can no longer present unique and shared capability boundaries of equipments. A new methodology for equipment capability analysis termed resource elements was reported. Moreover, the concrete realization process of resource elements was proposed. After did clustering analysis on the distribution of form generating schema among the equipments using SAS analysis software, an equipment resource model was constructed based on resource elements. The reported results show that the use of resource elements makes description about the functional characteristics among the equipments more clearly. Furthermore, the proposed method laid a foundation of recognition for virtual formation.

A Virtual Cellular Manufacturing System Design Model Based on Axiomatic Design Theory

This paper presents a virtual cellular manufacturing system building model based on axiomatic design theory. By now most discussions of virtual manufacturing cellular focus on how to format a virtual cell and how to optimize a virtual cell in the execution layer. The attention to “system cost” and “efficiency” mostly were given only after the system-generated. In this paper we build a complete virtual cellular manufacturing system, and pay attention to “system cost” and “efficiency” in the conceptual design phase to make sure the techniques and resource which we choose is low-cost and non-redundant. Meanwhile in Process Domain of the model, we blend the logistics balance idea of TOC theory, waste elimination philosophy of lean manufacturing and the existing conclusions of the virtual cell literature to make sure the tools which we choose are more effective. The model will clearly set out the concept, principle and technology hierarchy of virtual cellular manufacturing system, and can be an effective map for manufacturing enterprises with low-volume and high-variety to apply the virtual cellular production.

English

In an environment with fluctuating demand and unpredictable parts mix compositions, the efficiency of cellular systems necessitates the use of virtual cells. Virtual cellular manufacturing systems (VCMSs) are based on a highly flexible manufacturing concept designed to improve the performance of classical cellular manufacturing systems (CMSs) and job shop manufacturing environment by creating virtual grouping the resources temporarily in the production planning and control system. The underlying concept is particularly valuable during high demands variety and variability and with increase in machines types required by parts for processing. Whereas researches on VCMSs are still in a preliminary stage, this concept has gained momentum during the last decade and a wide and diverse variety of solution techniques have been applied for solving part-machine cell formation problems in these systems. The aim of this paper is to provide a review on recent efforts and developments in the VCMSs area to make required comparisons among the published works and determine future research issues and high-impact research scopes to search more. In this study, the overview was on various criteria in detail. While already a few researchers tended to make surveys over VCMSs, some important criteria were neglected to review and consider, which this paper tried to cover them as a complementary and an updated version.   Key words: Cell formation, survey, virtual cellular manufacturing system.

Workforce Assignment into Large-Sized Virtual Cells Using Learning Vector Quantization (LVQ) Approach

Much has been reported in literature on virtual cell formation problems while a limited work is reported on worker assignments. Virtual Cellular Manufacturing Systems (VCMS) have come into existence, replacing traditional Cellular Manufacturing Systems (CMS), to meet highly dynamic production conditions in terms of demand, production lots, processing times, product mix and production sequences. Although the problem of worker assignment and flexibility in cell based manufacturing environments has been studied and analyzed in plenty using various heuristics/mathematical models, application of Artificial Neural Networks (ANN), adapted from the biological neural networks, is the recent development in this field exploiting the ability of ANN to work out mathematically-difficult-to-solve problems. In this attempt, the previous work of the author has been further developed and an attempt has been made to apply Learning Vector Quantization (LVQ) approach into worker assignment problems for higher order virtual cells i.e., three cells configurations and analyze the suitability of LVQ approach in terms of successful classification rate and simulation parameters for a number of VCMS periods.

Virtual cellular manufacturing system based on resource element approach and analyzing its performance over different basic layouts

Article history: Received 25 Feb 2010 Received in revised form June, 5, 2011 Accepted 7 July 2011 Available online 9 July 2011 This research aims to present how choosing a suitable layout can improve the performance of virtual cellular manufacturing systems (VCMSs), especially minimizing the material flow between machines required by each family group. To present the efficacy of basic layouts on performances of VCMSs, a multi-objective mathematical model with a goal programming (GP) approach is developed to generate VCMSs based on resource-elements (REs). The formulated model is coded in Lingo software and is run over functional and distributed arrangements of the same machines. The performance and the validity of the developed model are checked by a numerical example taken from the literature. The objective function of the mathematical model is measured for that example over two mentioned layout to compare the performance of the generated systems. Moreover, because of the material handling costs importance, material flows are measured to find the best option as a basic layout for VCMSs. To compare the performance of the generated system with the classical cellular manufacturing system (CMS), cell capacity utilization (CCU) is employed as an independent criterion to evaluate each system. The result illustrates the priority of distributed layouts for generating RE-based VCMSs because of its flexibility, minimizing the objective function for the mathematical model, and smaller material flow by the components. In addition, the generated VCMSs outperforms the classical CMS from the CCU point of view. © 2012 Growing Science Ltd. All rights reserved