Optimum machine capabilities for reconfigurable manufacturing systems (original) (raw)
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Multiple Objective Optimization of Reconfigurable Manufacturing System
Reconfigurable Manufacturing System (RMS) is the state of art technology offering the functionality and capacity that is needed, when it is needed. The Reconfigurable Machine Tools (RMT) the sole of RMS, can perform variety of operations and can further be reconfigured to change its operational capabilities. In such environment a part can be processed by many feasible configurations of the RMS. In the present work a methodology is proposed for multiple objective optimization of RMS configuration based on convertibility, machine utilization and cost by applying nondominated sorting genetic algorithm II.
Computers & Industrial Engineering, 2013
This paper deals with a problem of reconfigurable manufacturing systems (RMSs) design based on products specifications and reconfigurable machines capabilities. A reconfigurable manufacturing environment includes machines, tools, system layout, etc. Moreover, the machine can be reconfigured to meet the changing needs in terms of capacity and functionality, which means that the same machine can be modified in order to perform different tasks depending on the offered axes of motion in each configuration and the availability of tools. This problem is related to the selection of candidate reconfigurable machines among an available set, which will be then used to carry out a certain product based on the product characteristics. The selection of the machines considers two main objectives respectively the minimization of the total cost (production cost, reconfiguration cost, tool changing cost and tool using cost) and the total completion time. An adapted version of the non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve the problem. To demonstrate the effectiveness of the proposed approach on RMS design problem, a numerical example is presented and the obtained results are discussed with suggested future research.
Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics, 2012
The concept of Reconfigurable Manufacturing Systems (RMSs) was formulated due to the global necessity for production systems that are able to economically evolve according to changes in markets and products. Technologies and design methods are under development to enable RMSs to exhibit transformable system layouts, reconfigurable processes, cells and machines. Existing manufacturing design systems do not encapsulate concepts of reconfigurability in design mechanisms to obtain optimal RMS configurations. This paper presents a framework for a resource allocation and shop floor design system within the context of RMSs. The framework focuses on the automated generation of shop floor configurations for systems with high product variety and shared resources. The DEVS, (Discrete Event System Specification), formalism is used to model reconfigurable equipment and simulate manufacturing processes. The "design engine" in the proposed framework, implements a genetic algorithm for the assembly, evaluation and optimisation of candidate shop floor configurations and their corresponding DEVS models.
Configuration design of scalable reconfigurable manufacturing systems for part family
International Journal of Production Research, 2019
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.
Review of Computer Engineering Research, 2016
Manufacturing System has been evolved over the years to accommodate major design variations. To respond to these high frequency variations and to stay competitive, there is a need of having such type of manufacturing system that could cope with market trends and design changes efficiently. Product's design and its manufacturing capabilities are closely related, thus the manufacturing system should be customized to cater all the design changes with suitable manufacturing capabilities. Reconfigurable Manufacturing system has been recommended for the turbulent market conditions because of its flexible and changeable nature. This research work is based on the co-generated model in which optimal machine configurations are generated through the application of optimization technique. Based on these configurations, system is tested for reconfiguration in case of production changeovers. Considering the relevant change drivers the degree of reconfigurability in any case of application can be achieved through proposed algorithm. A case study has been presented to illustrate the application of proposed model based on the technological constraints.
7th IFAC Conference on Manufacturing Modelling, Management, and Control, 2013, 2013
Reconfigurable Manufacturing Systems (RMSs) are a new paradigm of manufacturing able to customize its capacity and functionality when needed, to be reactive to market changes and uncertainties. In this paper, to take full advantage of the reconfigurability of RMSs, we propose a new approach using genetic algorithms and a simulation based optimization for process planning for a single product type. The proposed approach copes with market uncertainty and demands fluctuation in order to satisfy demands within their deadlines and with a minimum total cost. The total cost consists of five major costs: machine using cost, machine changing cost, configuration changing cost, tool using cost and tool changing cost. An illustrative example is presented to show the applicability of the approach.
IEEE Access
The need for automated production plans has evolved over the years due to internal and external drivers like developed products, new enhanced processes and machinery. Reconfigurable manufacturing systems focus on such needs at both production and process planning level. The age of Industry 4.0 focused on mass customization requires computer aided planning techniques that are able to cope with custom changes in products and explores intelligent algorithms for efficient scheduling solutions to reduce lead time. This problem has been categorized as NP-Hard in literature and is addressed by providing intelligent heuristics that focus on reducing machining time of the products at hand. However, as 70% of the lead time is consumed in non-value added tasks, it is fundamental to provide modular solutions that can reduce this time and handle part variety. To address the subject, this paper focuses on the generation of automated process plans for a single machine problem while focusing on reducing time lead time. Two evolutionary algorithms (EAs) have been proposed and compared to answer complex problem of process planning. A modified genetic algorithm (GA) has been proposed in addition to cuckoo search (CS) heuristic for this discrete problem. On testing with selected benchmark part ANC101, significant improvement was seen in terms of convergence with proposed EAs. Moreover, a novel Precedence Group Algorithm (PGA) is proposed to generate quality input for heuristics. The algorithm produces a set of initial population which significantly effects the performance of proposed heuristics. For the discrete constrained process planning problem, GA outperforms CS providing 10% more feasible scheduling options and three times lesser run time as compared to CS. The proposed technique is flexible and responsive in order to accommodate part variety, a necessary requirement for reconfigurable systems.
Method for synthetic modeling of the reconfigurable machine tools using genetic algorithms
This paper presents a method of synthetic modeling which can be applied by the manufacturing companies in order to rapidly evaluate the production costs, under the technical restrictions imposed by the client requests. Using the proposed approach the company can make an appropriate price quotation for its products and thus establish its profit margin. In the case of manufacturing processes, the key idea is to online model, the costs occurring during the machining and consumption of time both in relation to the chips flow. After this, using the genetic algorithms technique the optimal cutting conditions are determined as well as the minimum cost for the manufacturing task completion under the restrictions imposed by the technical specifications and by the negotiation process. The minimum cost is considered to be a reference against which the price quotation is made in accordance with the commercial policy of the company. Using the proposed approach the company can set a price for a p...