A stochastic model of a reconfigurable manufacturing system Part 1: A framework

Products required by customers are classified into several product families, each of which is a set of similar products. A reconfigurable manufacturing system (RMS) manages to satisfy customers, with each family corresponding to one configuration of the RMS. Then the products belonging to the same family will be produced by the RMS under the corresponding configuration. The manufacturing system possesses the reconfigurable function for different families. In an RMS there are three important issues: the optimal configurations in the design, the optimal selection policy in the utilization, and the performance measure in the improvement. This paper proposes a framework for a stochastic model of an RMS, which involves the above issues. Two optimization problems and the performance measure stemmed from the issues are formulated. An example is given for illustration. Some discussions are presented for future research work.     

A stochastic model of a reconfigurable manufacturing system Part 2: Optimal configurations

Various products required by customers are classified into several product families, each of which is a set of similar products. A reconfigurable manufacturing system (RMS) manages to satisfy customers, with each family corresponding to one configuration of the RMS. Then, the products belonging to the same family will be produced by the RMS under the corresponding configuration. The manufacturing system possesses the reconfigurable function for different families. In the design period of a RMS, there may exist several feasible configurations for each family. Then, an important issue in a RMS is the optimal configurations for the families. Based on a stochastic model, an optimization problem stemmed from the issue is formulated. Two algorithms are devised to solve the optimization problem. Numerical examples are presented for evaluating the efficiency of the algorithms.     

A stochastic model of a reconfigurable manufacturing system Part 3: Optimal selection policy

Products required by customers are classified into several product families, each of which is a set of similar products. A reconfigurable manufacturing system (RMS) satisfies customer requirements by ensuring that each family corresponds to one configuration of the RMS. Products belonging to the same family will be produced by the RMS under the corresponding configuration. The manufacturing system is reconfigurable for different families. To utilize the RMS, a selection policy that is an action rule is needed, by which the manufacturer selects a family to produce ordered products belonging to the selected family. Thus, an important issue for an RMS is the optimal selection policy. Based on a stochastic model, an optimization problem stemmed from the issue is formulated. Two solution procedures are devised to solve the optimization problem. Numerical examples are presented for evaluating the efficiency of the algorithms.     

Configuration design of scalable reconfigurable manufacturing systems for part family

Intense global competition, dynamic product variations, and rapid technological developments force manufacturing systems to adapt and respond quickly to various changes in the market. Such responsiveness could be achieved through new paradigms such as Reconfigurable manufacturing systems (RMS). In this paper, the problem of configuration design for a scalable reconfigurable RMS that produces different products of a part family is addressed. In order to handle demand fluctuations of products throughout their lifecycles with minimum cost, RMS configurations must change as well. Two different approaches are developed for addressing the system configuration design in different periods. Both approaches make use of modular reconfigurable machine tools (RMTs), and adjust the production capacity of the system, with minimum cost, by adding/removing modules to/from specific RMTs. In the first approach, each production period is designed separately, while in the second approach, future information of products’ demands in all production periods is available in the beginning of system configuration design. Two new mixed integer linear programming (MILP) and integer linear programming (ILP) formulations are presented in the first and the second approaches respectively. The results of these approaches are compared with respect to many different aspects, such as total system design costs, unused capacity, and total number of reconfigurations. Analyses of the results show the superiority of both approaches in terms of exploitation and reconfiguration cost.     

Grouping and selecting products: the design key of Reconfigurable Manufacturing Systems (RMSs)

A Reconfigurable Manufacturing System (RMS) is a new paradigm that focuses on manufacturing a high variety of products at the same system. Having specified a design strategy for an RMS as the first design step at the tactical level, products must be grouped to identify and allocate corresponding manufacturing facilities. An interface between market and manufacturing called reconfiguration link is presented to specify and arrange products for manufacturing. The reconfiguration link incorporates the tasks of determining the products in the production range, grouping them into families and selecting the appropriate family at each configuration stage. The proposed approach of (re)configuring products before manufacturing facilitates assigning product families to the required manufacturing facilities in terms of (re)configuring manufacturing systems. This paper contributes an overall approach of grouping products into families based on operational similarities, when machines are still not identified. Since the problem of product family selection consists of quantitative and qualitative objectives, the Analytical Hierarchical Process (AHP) is then used while considering both market and manufacturing requirements. The AHP model is verified in an industrial case study through using Expert Choice software. The solutions take advantage of monitoring sensitivity analysis while changing the priorities of manufacturing and/or market criteria. The concept of the proposed model is generic in structure and applicable to many firms. However, the model must be adapted according to the specific nature of the company under study. For instance, product family choices may differ from one company to another because of the available technology and the volume and type of existing products in the production range.     

A comprehensive approach to operation sequence similarity based part family formation in the reconfigurable manufacturing system

The industrial sector of the twenty-first century faces a highly volatile market in which manufacturing systems must be capable of responding rapidly to the market changes, while fully exploiting resources. The reconfigurable manufacturing system (RMS) is a state of the art technology offering the exact functionality and capacity needed, which is built around a part family. The configuration of an RMS evolves over a period to justify the needs of upcoming part families. The foundation for the success of an RMS, therefore, lies in the recognition of appropriate sets of part families. In the present work the authors have developed a novel operation sequence based BMIM (bypassing moves and idle machines) similarity coefficient using longest common subsequence (LCS) and the minimum number of bypassing moves and the quantity of idle machines. The effectiveness of the developed similarity coefficient has been compared with the existing best similarity/dissimilarity coefficients available in the existing literature. An example set of parts has been classified using the developed similarity coefficient and average linkage hierarchical clustering algorithm. The developed approach can also be used very effectively for part family formation in the cellular manufacturing system.     

Evaluation of the logistic model of the reconfigurable manufacturing system based on generalised stochastic Petri nets

To reveal the influence on system performance by the logistic model of reconfigurable manufacturing system (RMS), the generalised stochastic Petri nets (GSPN) modular modelling approach is presented in this paper. It is based upon the characteristics of a bottleneck service. According to this approach, the bottleneck service in the production process is found first. By corresponding different resources in the service to different modules of the GSPN, the module is reconfigured. The analysis of the model using the Markov chain is hereby presented, as is the average utilisation factor of RMS. Following this, the production capacity of different products and the average productivity of reconfigurable manufacturing cells (RMCs) are discussed.     

Reconfigurability consideration and scheduling of products in a manufacturing industry

Reconfigurable manufacturing system is a new type of manufacturing system which can change its capacity and functionality very easily and quickly whenever required. RMS (reconfigurable manufacturing system) has capacity and functionality exactly what is required. RMS is adjustable to the fluctuating demands and it can be easily upgraded with new process technology. Reconfigurability of a manufacturing system is measured in terms of cost, effort and time. It is the ability of a manufacturing system to be reconfigured quickly with low reconfiguration effort at low cost. In the present work, reconfigurability has been considered in Continental Automotive Components Pvt. Ltd. on the basis of RMS principles. A modified reconfigurable layout has been proposed for an assembly line and scheduling of the products has been done for the criteria reconfiguration effort, profit over cost and due date. Scheduling of the products has been done using the integrated approach of Shannon entropy and RIM (Reference Ideal Method). R program has been written for scheduling. Sensitivity analysis has been conducted for the problem.     

Design of delayed reconfigurable manufacturing system based on part family grouping and machine selection

To improve the convertibility of reconfigurable manufacturing system (RMS), the concept of delayed reconfigurable manufacturing system (D-RMS) was proposed. RMS and D-RMS are both constructed around part family. However, D-RMS may suffer from ultra-long system problem with unacceptable idle machines using generic RMS part families. Besides, considering the complex basic system structure of D-RMS, machine selection of D-RMS should be addressed, including dedicated machine, flexible machine, and reconfigurable machine. Therefore, a system design method for D-RMS based on part family grouping and machine selection is proposed. Firstly, a part family grouping method is proposed for D-RMS that groups the parts with more former common operations into the same part family. The concept of longest relative position common operation subsequence (LPCS) is proposed. The similarity coefficient among the parts is calculated based on LPCS. The reciprocal value of the operation position of LPCS is adopted as the characteristic value. The average linkage clustering (ALC) algorithm is used to cluster the parts. Secondly, a machine selection method is proposed to complete the system design of D-RMS, including machine selection rules and the dividing point decision model. Finally, a case study is given to implement and verify the proposed system design method for D-RMS. The results show that the proposed system design method is effective, which can group parts with more former common operations into the same part family and select appropriate machine types.     

Decision support for design of reconfigurable rotary machining systems for family part production

To remain competitive in currently unpredictable markets, the enterprises must adapt their manufacturing systems to frequent market changes and high product variety. Reconfigurable manufacturing systems (RMSs) promise to offer a rapid and cost-effective response to production fluctuations under the condition that their configuration is attentively studied and optimised. This paper presents a decision support tool for designing reconfigurable machining systems to be used for family part production. The objective is to elaborate a cost-effective solution for production of several part families. This design issue is modelled as a combinatorial optimisation problem. An illustrative example and computational experiments are discussed to reveal the application of the proposed methodology. Insight gained would be useful to the decision-makers managing the configuration of manufacturing systems for diversified products.     

Availability consideration in the optimal selection of multiple-aspect RMS configurations

Machine availability has a profound influence on the performance of manufacturing systems. This paper extends a model for optimizing reconfigurable manufacturing systems (RMS) configurations with multiple-aspects to incorporate the effect of machine availability using the universal generating function (UGF). Two powerful meta-heuristic optimization techniques, namely genetic algorithms (GAs) and tabu search (TS), are used for optimizing the capital cost and system availability of the RMS configurations. The optimized configurations can handle multiple-parts and their structure is that of flow lines allowing paralleling of identical machines in each production stage. The various aspects considered in the RMS configurations include arrangement of machines, equipment selection and assignment of operations. A case study is presented and implementation of the optimization model is carried out using MATLAB software. The results of using both GAs and TS to solve the problem are then reported and compared for validation. Analysis of different cases of availability consideration including infinite and no buffer capacity is performed and results are compared to those obtained when machine availability is not considered. It has been shown that considering availability affects the optimal configuration selection and increases the required equipment. This increases the costs of the near-optimal configurations obtained especially in the case without buffers. The presented model can support the manufacturing systems configuration selection decisions at both the initial design and reconfiguration stages.     

RMS capacity utilisation: product family and supply chain

The paper contributes to development of RMS through linkage with external stakeholders such as customers and suppliers of parts/raw materials to handle demand fluctuations that necessitate information sharing across the supply chain tiers. RMS is developed as an integrated supply chain hub for adjusting production capacity using a hybrid methodology of decision trees and Markov analysis. The proposed Markov Chain model contributes to evaluate and monitor system reconfigurations required due to changes of product families with consideration of the product life cycles. The simulation findings indicate that system productivity and financial performance in terms of the profit contribution of product-process allocation will vary over configuration stages. The capacity of an RMS with limited product families and/or limited model variants becomes gradually inoperative whilst approaching upcoming configuration stages due to the end of product life cycles. As a result, reconfiguration preparation is suggested quite before ending life cycle of an existing product in process, for switching from a product family to a new/another product family in the production range, subject to its present demand. The proposed model is illustrated through a simplified case study with given product families and transition probabilities.     

Timed event graph-based cyclic reconfigurable flow shop modelling and optimization

The manufacturing process of a part involves sequential steps and each step could be viewed as the part being manufactured by a process module with some specific function. The module must be placed on a machine and connected to the machine via standard interfaces. The machine considered here is a carrier or general platform that can hold one or several different modules simultaneously. Based on the idea that modules are independent of machines and different combinations of modules and machines result in different configurations, the cyclic reconfigurable flow shop is proposed for the new manufacturing paradigm—Reconfigurable Manufacturing System (RMS). The cyclic reconfigurable flow shop can be modelled as a timed event graph. Different cases of cyclic reconfigurable flow shops are discussed and the optimal configuration can be obtained by solving the corresponding mixed-integer program derived from the timed event graph model.     

Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL)

The dynamic nature of today’s manufacturing industry, which is caused by the intense global competition and constant technological advancements, requires systems that are highly adaptive and responsive to demand fluctuations. Reconfigurable manufacturing systems (RMS) enable such responsiveness through their main characteristics. This paper addresses the problem of RMS configuration design, where the demand of a single product varies throughout its production life cycle, and the system configuration must change accordingly to satisfy the required demand with minimum cost. A two-phased method is developed to handle the primary system configuration design and the necessary system reconfigurations according to demand rate changes. This method takes advantage of Reconfigurable Machine Tools in RMS. In fact, by adding/removing modules to/from a specific modular reconfigurable machine, its production capability could be increased, with lower cost. A novel mixed integer linear programming formulation is presented in the second phase of the method to optimise the process of selecting the best possible transformation for the existing machine configurations. Two different cases are designed and solved by implementing the established method. The results of these cases in terms of capital cost, capacity expansion cost, unused capacity and number of transformations, are compared with two hypothetical scenarios. Analyses of the obtained results indicate the efficiency of the proposed approach and offer a promising outlook for further research.     

基于公理设计的可重构制造系统设计

可重构制造系统是面向客户定制的一种新的制造模式.先阐述了可重构制造系统的概念及其特点,并对目前关于可重构制造系统的设计研究进行了总结.然后,以快速响应市场变化为目标,提出了基于公理设计理论的设计方法及设计步骤,对可重构制造系统进行设计,降低了设计的复杂性.层次化设计和分解方法保证了在设计过程中以适当的顺序做出合理决策.最后给出了可重构制造系统的工作流程图.     

Optimal concurrent product design and process planning based on the requirements of individual customers in one-of-a-kind production

One-of-a-kind production is a new manufacturing paradigm for producing customised products based on the requirements of individual customers while maintaining the quality and efficiency of mass production. This research addresses the issues in optimal concurrent product design and process planning based on the requirements of individual customers. In this work, a hybrid AND-OR graph is developed to model the variations of design configurations/parameters and manufacturing processes/parameters in a generic product family. Since different design configurations and parameters can be created from the same customer requirements, and each design can be further achieved through alternative manufacturing processes and parameters, co-evolutionary genetic programming and numerical optimisation are employed to identify the optimal product design configuration/parameters and manufacturing process/parameters. A case study is introduced to identify the optimal design configuration/parameters and manufacturing process/parameters of custom window products of an industrial company to demonstrate the effectiveness of the developed method.     

Optimisation for multi-part flow-line configuration of reconfigurable manufacturing system using GA

To facilitate the configuration selection of reconfigurable manufacturing systems (RMS) at the beginning of every demand period, it needs to generate K (predefined number) best configurations as candidates. This paper presents a GA-based approach for optimising multi-part flow-line (MPFL) configurations of RMS for a part family. The parameters of the MPFL configuration comprise the number of workstations, the number of paralleling machines and machine type as well as assigned operation setups (OSs) for each workstation. Input requirements include an operation precedence graph for each part, relationships between operations and OSs as well as machine options for each OS. The objective is to minimise the capital cost of MPFL configurations. A 0-1 nonlinear programming model is developed to handle sharing machine utilisation over consecutive OSs for each part which is ignored in the existing approach. Then a novel GA-based approach is proposed to identify K economical solutions within a refined solution space comprising the optimal configurations associated with all feasible OS assignments. A case study shows that the best solution found by GA is better than the optimum obtained by the existing approach. The solution comparisons between the proposed GA and a particle swarm optimisation algorithm further illustrate the effectiveness and efficiency of the proposed GA approach.     

Multi-objective design optimization of reconfigurable machine tools: a modified fuzzy-Chebyshev programming approach

A reconfigurable manufacturing system (RMS) is designed for rapid adjustment of functionalities in response to market changes. A RMS consists of a number of reconfigurable machine tools (RMTs) for processing different jobs using different processing modules. The potential benefits of a RMS may not be materialized if not properly designed. This paper focuses on RMT design optimization considering three important yet conflicting factors: configurability, cost and process accuracy. The problem is formulated as a multi-objective model. A mechanism is developed to generate and evaluate alternative designs. A modified fuzzy-Chebyshev programming (MFCP) method is proposed to achieve a preferred compromise of the design objectives. Unlike the original fuzzy-Chebyshev programming (FCP) method which imposes an identical satisfaction level for all objectives regardless of their relative importance, the MFCP respects their priority order. This method also features an adaptive satisfaction-level-dependent process to dynamically adjust objective weights in the search process. A particle swarm optimization algorithm (PSOA) is developed to provide quick solutions. The application of the proposed approach is demonstrated using a reconfigurable boring machine. Our computational results have shown that the combined MFCP and PSOA algorithm is efficient and robust. The advantages of the MFCP over the original FCP are also illustrated based on the results.     

Flexibility-based multi-objective approach for machines selection in reconfigurable manufacturing system (RMS) design under unavailability constraints

The reconfigurable manufacturing system (RMS) is a recent manufacturing paradigm driven by the high responsiveness and performance efficiencies. In such system, machines, material handling units or machines components can be added, modified, removed or interchanged as needed. Hence, the design of RMS is based on reconfigurable machines capabilities and product specification. This paper addresses the problem of machines selections for RMS design under unavailability constraints and aims to develop an approach to ensure the best process plan according to the customised flexibility required to produce all parts of a given product. More specifically, we develop a flexibility-based multi-objective approach using an adapted version of the well-known non-dominated sorting genetic algorithm to select adequate machines from a set of candidate (potential) ones, in order to ensure the best responsiveness of the designed system in case of unavailability of one of the selected machines. The responsiveness is based on the flexibility of the designed system and a generated process plan, which guarantees the management of machines unavailability. It is defined as the ability and the capacity to adapt the process plan in response to machines unavailability. Two objectives are considered, respectively, the maximisation of the flexibility index of the system and the minimisation of the total completion time. To choose the best solution in the Pareto front, a multi-objective decision-making method called technique for order of preference by similarity to ideal solution is used. To demonstrate the applicability of the proposed approach, a simple example is presented and the numerical results are analysed.     

A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems

Integrated process planning and scheduling (IPPS) is a manufacturing strategy that considers process planning and scheduling as an integrated function rather than two separated functions performed sequentially. In this paper, we propose a new heuristic to IPPS problem for reconfigurable manufacturing systems (RMS). An RMS consists mainly of reconfigurable machine tools (RMTs), each with multiple configurations, and can perform different operations with different capacities. The proposed heuristic takes into account the multi-configuration nature of machines to integrate both process planning and scheduling. To illustrate the applicability and the efficiency of the proposed heuristic, a numerical example is presented where the heuristic is compared to a classical sequential process planning and scheduling strategy using a discrete-event simulation framework. The results show an advantage of the proposed heuristic over the sequential process planning and scheduling strategy.