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.     

Recent advances in research on reconfigurable machine tools: a literature review

A recent trend in research on reconfigurable manufacturing systems is the development of reconfigurable machine tools (RMTs). A RMT can be used as a group of machines through change of its configuration to satisfy different manufacturing requirements. A literature review is provided in this paper to demonstrate the state-of-the-art advances and challenges on research and development of RMTs from the perspectives of architecture design, configuration design and optimisation, and system integration and control. In architecture design, semi-open and open architectures based on modular design approach are often selected to allow different modules of the machine to be added and removed. In configuration design and optimisation, operations of reconfiguration processes are analysed and optimised to achieve variety of configurations with the minimum reconfiguration effort. In system integration and control, transfers of motion, energy and data among different modules of the RMT are carried out. The challenges in research on RMTs are also discussed.     

Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches

Achieving competitiveness in nowadays manufacturing market goes through being cost and time-efficient as well as environmentally harmless. Reconfigurable manufacturing system (RMS) is a paradigm that is able to meet these challenges due to its scalability and integrability. In this paper, we aim to solve the multi-objective sustainable process plan generation problem in a reconfigurable environment. In addition to the total production cost and the completion time, we use the amount of greenhouse gases (GHG) emitted during the manufacturing process as a sustainability criterion. We propose an iterative multi-objective integer linear programming (I-MOILP) approach and its comparison with adapted versions of the two well-known evolutionary algorithms, respectively, the Archived Multi-Objective Simulated Annealing (AMOSA) and the Non-dominated Sorting Genetic Algorithm (NSGA-II). Moreover, we study the influence of the probabilities of genetic operators on the convergence of the adapted NSGA-II. To illustrate the applicability of the three approaches, an example is presented and obtained numerical results analysed.     

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.     

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.     

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 4: Performance measure

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. A performance measure is defined as service levels for the families. A semi-Markov process is formulated for obtaining the performance measure. When a larger fluctuation in the market happens, the manufacturer can adjust the system to improve the performance measure. An optimization of a reassigning problem is discussed, which reassigns the maximum numbers of orders to the families. Two solution approaches are proposed to solve the problem. Numerical examples are given for illustrating the methodologies.     

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 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.     

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.     

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.     

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.     

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.     

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.     

Combining MPC and integer operators for capacity adjustment in job-shop systems with RMTs

With today's worldwide competition, manufacturing companies are faced with challenges to respond to volatile market demands quickly and flexibly while maintaining a cost-effective level of production. Capacity adjustment is one of the major approaches to cope with such uncertain fluctuations, balance capacity and load and improve the effectiveness of manufacturing control. Instead of flexible staffs, working time and outsourcing, in this paper, we consider a machinery-based capacity adjustment via Reconfigurable Machine Tools (RMTs) to compensate for unpredictable events. To include these tools effectively on the operational and tactical layer, we propose a complementing feedback approach using model predictive control (MPC) to identify the potential of RMTs for a better compliance with logistics objectives and a sustainable demand oriented capacity allocation. To this end, we formulate a reconfiguration rule for the determination of the triggered RMTs and propose three strategies for resolving the integer assignment of RMTs: floor operator, genetic algorithm as well as branch and bound. Utilising simulation, we demonstrate the effectiveness of the proposed method for a four-workstation job-shop system.     

Multi-objective multi-unit process plan generation in a reconfigurable manufacturing environment: a comparative study of three hybrid metaheuristics

Low costs, high reactivity and high quality products are necessary criteria for industries to achieve competitiveness in nowadays market. In this context, reconfigurable manufacturing systems (RMSs) have emerged to fulfil these requirements. RMS is one of the latest manufacturing paradigms, where machines components, software or material handling units can be added, removed, modified or interchanged as needed and when imposed by the necessity to react and respond rapidly and cost-effectively to changing. This research work addresses the multi-objective single-product multi-unit process plan generation problem in a reconfigurable manufacturing environment where three hybrid heuristics are proposed and compared namely: repetitive single-unit process plan heuristic (RSUPP), iterated local search on single-unit process plans heuristic (LSSUPP) and archive-based iterated local search heuristic (ABILS). Single-unit process plans are generated using the adapted non-dominated sorting genetic algorithm (NSGA-II). Moreover, in addition to the minimisation of the classical total production cost and the total completion time, the minimisation of the maximum machines exploitation time is considered as a novel optimisation criterion, in order to have high quality products. To illustrate the applicability of the three approaches, examples are presented and the obtained numerical results are analysed.     

Optimal cost design of flow lines with reconfigurable machines for batch production

Modular reconfigurable machines offer the possibility to efficiently produce a family of different parts. This paper formalises a cost optimisation problem for flow lines equipped with reconfigurable machines which carry turrets, machining modules and single spindles. The proposed models take into account constraints related to: (i) design of machining modules, turrets, and machines, (ii) part locations, and (iii) precedence relations among operations. The goal is to minimise equipment cost while reaching a given output and satisfying all the constraints. A mixed integer programming model is developed for the considered optimisation problem. The approach is validated through an industrial case study and extensive numerical experiments.     

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.     

Energy-oriented bi-objective optimisation for a multi-module reconfigurable manufacturing system

This paper investigates a multi-module reconfigurable manufacturing system for multi-product manufacturing. The system consists of a rotary table and multiple machining modules (turrets and spindles). The production plan of the system is divided into the system design phase and the manufacturing phase, where the installation cost and the energy consumption cost correspond to the two phases, respectively. A mixed-integer programming model for a more general problem is presented. The objectives are to minimise the total cost and minimise the cycle time simultaneously. To solve the optimisation problem, the ε-constraint method is adopted to obtain the Pareto front for small size problems. Since the ε-constraint method is time consuming when problem size increases, we develop a multi-objective simulated annealing algorithm for practical size problems. To demonstrate the efficiency of the proposed algorithm, we compare it with a classic non-dominated sorting genetic algorithm. Experimental results demonstrate the efficiency of the multi-objective simulated annealing algorithm in terms of solution quality and computation time.     

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.