An ant colony optimisation algorithm for scheduling in agile manufacturing

Producing customised products in a short time at low cost is one of the goals of agile manufacturing. To achieve this goal an assembly-driven differentiation strategy has been proposed in the agile manufacturing literature. In this paper, we address a manufacturing system that applies the assembly-driven differentiation strategy. The system consists of machining and assembly stages, where there is a single machine at the machining stage and multiple identical assembly stations at the assembly stage. An ant colony optimisation (ACO) algorithm is developed for solving the scheduling problem of determining the sequence of parts to be produced in the system so as to minimise the maximum completion time (or makespan). The ACO algorithm uses a new dispatching rule as the heuristic desirability and variable neighbourhood search as the local search to make it more efficient and effective. To evaluate the performance of heuristic algorithms, a branch-and-bound procedure is proposed for deriving the optimal solution to the problem. Computational results show that the proposed ACO algorithm is superior to the existing algorithm, not only improving the performance but also decreasing the computation time.     

Modelling for agile manufacturing systems

To address the challenges of a rapidly changing manufacturing market, a new type of manufacturing system with characteristics of reconfigurability, reusability and scalability, an agile manufacturing system (AMS) has to be developed. Reconfigurability is an essential feature of AMS. Such a system can use basic building blocks, both hardware and software, which can be reconfigured quickly and reliably. A fundamental early step in the reconfiguring process for an agile manufacturing system is to develop a model that adequately describes the proposed system, in order to be able to study and evaluate the impact of the reconfiguring decision on the system performance, before its construction. Therefore, the rapid modelling and reusable modelling capabilities are demanded. In this paper, an Object & Knowledge-based Interval Timed Petri-Net (OKITPN) approach is proposed, which provides an object-oriented and modular method of modelling manufacturing activities. It includes knowledge, interval time, modular and communication attributes. The features of object-oriented modelling allow the AMS to be modelled with the properties of classes and objects, and make the concept of software IC possible for rapid modelling of complex AMSs. Once all of the Interval Timed Petri-Net (ITPN) objects are well defined the developers need to consider only the interfaces and operations relating to the ITPN objects. In order to demonstrate the capability of the proposed OKITPN, it has been used to model rapidly AMSs that are reconfigured according to requirements.     

Complex assembly variant design in agile manufacturing. Part II: Assembly variant design methodology

In the first paper of this two-part series, the assembly variant design system architecture and complementary assembly methodology were presented. The general complementary assembly models, hierarchical assembly model and relational assembly model, are established which were further specified as the Assembly Variants Model (AVM) and the Assembly Mating Graphs (AMGs) respectively to cater for the needs for assembly variant design. This paper discusses the assembly variant design methodology which is based on these assembly models. The matching components are searched and retrieved from the AVM and then the constraint groups are identified by manipulating the AMGs. Then the assembly variant design process is formulated as a mixed-integer (linear or non-linear) programming problem which is solved using a standard solver or heuristic. This methodology provides a systematic approach to facilitate the variant design of complex assembly products in the agile manufacturing environment. Finally, a prototype system is developed and examples are presented.     

A review of agile manufacturing systems

About a decade ago, the agile manufacturing paradigm was formulated in response to the constantly changing 'new economy' and as a basis for returning to global competitiveness. While agility means different things to different enterprises under different contexts, the following elements capture its essential concept: agility is characterized by cooperativeness and synergism (possibly resulting in virtual corporations), by a strategic vision that enables thriving in face of continuous and unpredictable change, by the responsive creation and delivery of customer-valued, high quality and mass customized goods/services, by nimble organization structures of a knowledgeable and empowered workforce, and facilitated by an information infrastructure that links constituent partners in a unified electronic network. During this period, a significant amount of attention from both the academic and industrial communities has produced a large body of results in research and development related to this topic. Each contribution has tackled a different aspect of this large field. In this paper, we review a wide range of recent literature on agile manufacturing. About 73 papers from premier scientific journals and conferences have been reviewed, and a classification scheme to organize these is proposed. We critique these bodies of work and suggest directions for additional research and identify topics where fruitful opportunities exist.     

Scheduling manufacturing systems for delayed product differentiation in agile manufacturing

Production of customized products to respond to changing markets in a short time and at a low cost for agile manufacturing can be implemented with delayed product differentiation in a manufacturing system. The successful implementation of delayed product differentiation lies in efficient scheduling of the manufacturing system. Scheduling problems in implementing delayed product differentiation in a general flexible manufacturing system are defined, formulated and solved here. The manufacturing system consists of two stages: machining and assembly. At the machining stage, a single machine is used to produce standard component parts for assembly products. These parts are then assembled at the assembly stage by multiple identical assembly stations to form customized products. The products to be produced in the system are characterized by their assembly sequences represented by digraphs. The scheduling problem is to determine the sequence of products to be produced in the system so that the maximum completion time (makespan) is minimized for any given number of assembly stations at the assembly stage. Based on the representation of assembly sequence of the products, three production modes are defined: production of a single product with a simple assembly sequence ; production of a single product with a complex assembly sequence ; and production of N products . According to the three defined production modes, the associated scheduling problems are defined as G s scheduling problems, G c scheduling problems and N-product scheduling problems, respectively. Optimal and heuristic methods for solving the scheduling problems are developed. The computational experiment shows that the heuristics provide good solutions to the scheduling problems.     

Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty

In this study, a general comprehensive model is proposed for strategic closed-loop supply chain network design under interval data uncertainty. The proposed model considers various assumptions such as multiple periods, multiple products, and multiple supply chain echelons as well as uncertain demand and purchasing cost. In addition, bill of materials for each product is considered via a new approach in management of forward and reverse flows of products for producing new products and reusing or disassembling returned products. Uncertainty of parameters in the proposed model is handled via an interval robust optimisation technique. The model assumptions are well matched with decision making environments of food and high-tech electronics manufacturing industries. The factors that make these two industries similar are time-dependent properties of products such as prices and warehousing lifetime period. The computational results of solving the proposed model via LINGO 8 demonstrate efficiency of the proposed model in dealing with uncertainty in an agile manufacturing context.     

Systemic criterion of sustainability in agile manufacturing

Agile manufacturing systems work in a constantly changing global market, particularly assembly systems at the last stage of product differentiation. Meanwhile, sustainability is becoming a key issue for manufacturing strategy. This paper formulates a systemic criterion of sustainability in agile manufacturing and computes it through flexibility and complexity. It is defined as a ratio of utility and entropy as a sustainability measurement. Under a unified framework, utility allows one to quantify the contributions to agility, in particular system flexibility. Complexity is measured by entropy. Thus, an original complementary role of flexibility and the complexity of the system are proposed. Developed from the distribution of system states, the systemic approach to sustainability in terms of output evolution is enriched. Based on a simple assembly line integer model simulation, a first quantitative analysis illustrates the concepts introduced.     

A distributed virtual factory in agile manufacturing environment

There is a growing recognition that current manufacturing enterprises must be agile, that is, capable of operating profitably in a competitive environment of continuously changing customer demands. The use of a virtual enterprise (VE) is becoming increasingly prevalent, and that has been made possible, in part, due to the significant advances in communication and information technology in recent years. A manufacturing system is one of the competitive factors that forms an effective VE. Therefore, for manufacturers wishing to obtain a contract in VE, it is crucial to present attractive and competitive offers to other coalition members. There are several criteria to these offers, such as cycle time, fulfilment of due date or quick shipping date, cost, and quality assurance for the ordered products. Manufacturing system simulation could endorse the basic estimates of the criteria for the strategic offer. The distributed simulation model concept provides practical solutions to facilitate such a large-scaled precise simulation model in the VE environment, because it is constructed as the integration of several manufacturing simulation models of each production module in the factory. As a solution to realize this integration, we propose a Distributed Virtual Factory (DVF) concept that consists of distributed precise simulation models connected by several synchronization mechanisms named Time Bucket algorithms. A DVF enables precise evaluations of the whole manufacturing system, especially in terms of the material flows. In this study, we introduce an Activity Based Costing (ABC) method into the DVF architecture to estimate the detailed cost analysis of the products. The methodology facilitates strategic enterprise management to prepare the request for the bids in the VE environment. The effectiveness of the proposed concept in agile manufacturing is discussed with simulation experiments.     

Two-stage simulation optimization for agile manufacturing capacity planning

Capacity planning involves the selection of manufacturing technologies and the allocation of budget to specific equipment acquisitions. In today's highly volatile manufacturing world, an agile capacity-planning tool is required. This tool must provide the mechanism for a company to thrive in an environment of uncertainty. Uncertain future demands make capacity planning and technology selection difficult tasks, whether they are caused by variations in forecasts of direct demand or by upstream variability in a supply chain. In this paper, a practical modelling technique for minimizing the required investment in capacity planning for discrete manufacturing sites under an uncertain demand stream is presented. The method consists of a two-stage stochastic integer program. The first stage characterizes the optimal response of the system under uncertainty. The second stage selects a tool set based on the characterization from the first stage, with the addition of budget constraints. The model is scalable, allowing for multiple products, multiple operations, multiple flow paths including re-entrant flow, and multiple tool types. A simple example is introduced to explain the methodology, followed by the results of a large-scale real-world application in the semiconductor industry.     

Complex assembly variant design in agile manufacturing. Part I: System architecture and assembly modeling methodology

In the distributed and horizontally integrated manufacturing environment found in agile manufacturing, there is a great demand for new product development methods that are capable of generating new customized assembly designs based on mature component designs that might be dispersed at geographically distributed partner sites. To cater for this demand, this paper addresses the methodology for complex assembly variant design in agile manufacturing. It consists in fundamental research in two parts: (i) assembly modeling; and (ii) assembly variant design methodology. This paper, the first of a two-part series, presents the assembly variant design system architecture and the assembly modeling methodology. First, a complementary assembly modeling concept is proposed with two kinds of assembly models, the hierarchical assembly model and the relational assembly model. The first explicitly captures the hierarchical and functional relationships between constituent components whereas the second explicitly captures the mating relationships at the form-feature-level. These models are complementary in the sense that each of them models only a specific aspect of assembly-related information but together they include the required assembly-related information. They are further specialized to accommodate the features of assembly variant design. As a result, two kinds of assembly models, the assembly variants model and the assembly mating graph are generated. These assembly models serve as the basis for assembly variant design which is discussed in the companion paper.     

Genetic algorithms and simulated annealing for scheduling in agile manufacturing

In this paper, genetic algorithms and simulated annealing are applied to scheduling in agile manufacturing. The system addressed consists of a single flexible machine followed by multiple identical assembly stations, and the scheduling objective is to minimize the makespan. Both genetic algorithms and simulated annealing are investigated based on random starting solutions and based on starting solutions obtained from existing heuristics in the literature. Overall, four new algorithms are developed and their performance is compared to the existing heuristics. A 2³ factorial experiment, replicated twice, is used to compare the performance of the various approaches, and identify the significant factors that affect the frequency of resulting in the best solution and the average percentage deviation from a lower bound. The results show that both genetic algorithms and simulated annealing outperform the existing heuristics in many instances. In addition, simulated annealing outperforms genetic algorithms with a more robust performance. In some instances, existing heuristics provide comparable results to those of genetic algorithms and simulated annealing with the added advantage of being simpler. Significant factors and interactions affecting the performance of the various approaches are also investigated.     

Automated retrieval and ranking of similar parts in agile manufacturing

The environment considered is an agile enterprise where manufacturing partners share product related data to come up with new, customized, and high quality products at minimal leadtimes. Given this context, this paper addresses the problem of identifying existing parts that are similar, in one or many characteristics, to a new part at the design stage. The proposed method is based on the principles of group technology (GT), and on the definition of the neighborhood of similarity of critical design attributes. A two-step procedure is proposed: (1) a search procedure, which acquires and processes the designer's search intent to retrieve similar parts; and (2) a sorting procedure, which ranks these parts in order of their similarity to the candidate part. Both procedures are based on GT codes, while the second can employ more detailed critical design information. This information is assumed available in the product databases of distributed partners, and can be generated via an existing GT design processor. The approach employs a systematic procedure to combine independent similarity indexes to a unique measure for sorting. A software system using object-oriented technology has been developed to implement the procedure.     

Axiomatic modelling of agile production system design

The manufacturing organisations are witnessing a transformation in the manufacturing paradigm due to increasing competition. Agile manufacturing (AM) is a contemporary manufacturing paradigm which enables the organisations to survive in this competitive scenario. Design engineering is a vital technological enabler of AM. The research on axiomatic design in the field of AM is found to be feeble. In this context, this paper reports an axiomatic model of agile production system design using process variables. A hierarchical structure has been developed to model the design process of an agile production system composed of functional requirements, design parameters and process variables. In the theory of axiomatic design, process variables are created by mapping the design parameters in the process domain. This article serves as an efficient guideline for the design process to clarify the tools, methods and resources of designing agile production system of Indian electronic switches manufacturing organisation.     

Design and implementation of a virtual information system for agile manufacturing

In the new and emerging agile manufacturing paradigm, where multiple firms cooperate under flexible virtual enterprise structures, there exists a great need for a mechanism to manage and control information flow among collaborating partners. In response to this pressing need, this paper addresses the design and implementation of an agile manufacturing information system integrating manufacturing databases dispersed at various partner sites. We propose a framework in which: (1) information is modeled in a hierarchical fashion using object-oriented methodology (OOM); (2) information transactions are specified by the workflow hierarchy consisting of partner workflows; (3) information flow between partners is controlled by a set of distributed workflow managers (WM) interacting with partner knowledge bases, which reflect partner specific information control rules on internal data exchange, as well as inter-partner mutual protocols for joint partner communications; (4) the prototype system is accomplished using the World Wide Web based on a client-server architecture. The overall approach and system provides within a dynamic environment, where virtual partnerships are synthesized in response to specific business initiatives, a dynamic and flexible mechanism to support partner information exchange and to keep the dispersed information consistent.     

一种支持网络协议的嵌入式敏捷化智能维护单元的实现

基于敏捷制造对制造过程管理与维护的需求,研究并建立了制造系统智能维护单元的原型.针对CNC机床类设备在线监测与诊断系统应用中的问题,提出了支持网络协议的敏捷化多参数集成智能维护单元解决方案,设计了基于智能维护单元的旋转机械监视诊断与维护系统,该系统可在嵌入式处理器ARM和DSP处理器的控制下完成故障诊断和远程维护功能.     

Investigation of the reconfigurable control system for an agile manufacturing cell

A new type of manufacturing cell, with characteristics of reconfigurability, reusability and scalability, needs to be developed. To achieve the agile reconfiguration of a manufacturing cell, the cell control system must be rapidly and efficiently generated or modified. In this paper, a multi-agent based architecture is defined that supports the design and implementation of highly reconfigurable control systems for agile manufacturing cells, which are comprised of resource agents (material processing agents, material handling agents, and material storage agents), a control agent, and an information agent, in order to reduce costs and increase the control system's agility with respect to the changing environment. Different agents in the cell control system can be organized dynamically, communicate with each other through messages, and cooperate with each other to perform flexibly the task in the cell control system. The structure of the agents is proposed and the message-passing between agents is discussed in detail.     

Flexibility and manufacturing system design: An experimental investigation

Manufacturing industries today are faced with steady and unrelenting changes to the environment in which they operate. In order to survive and profit, manufacturing facilities must be designed such that they exhibit desirable system-level flexibility characteristics. The relationships between flexibility and manufacturing system design, however, remain largely unexplored. This paper investigates the effects of manufacturing system design on product, mix, production, and volume flexibilities, and on trade-offs between these flexibility types, for different product environments. Of particular concern is the determination of whether or not flexibility trade-offs can be avoided, and if so, how. Simulation experiments are performed to determine flexibility values for 16 different manufacturing system design 'approaches' and two levels of part processing flexibility. A total of 800 different manufacturing system/product set combinations are investigated. The results indicate that the effects of manufacturing system design on flexibility are not always intuitive, and that they can change depending upon the level of part processing flexibility present. In addition, however, they show that flexibility trade-offs are not inevitable: multiple flexibility types can be increased in value simultaneously through proper selection of the design approach.     

An expert system based approach to manufacturing cell design

The design of cellular systems is a complex, multi-criteria and multi-step process which can have significant implications for the entire organization. Most research in this area focuses on the formation of pan families and associated machine groups, one step in the cell design process. Numerous quantitative techniques have been developed to address this part-family/machine group formation problem. Existing approaches include math programming, algorithms for matrix diagonalization, the application of network modelling and the use of similarity coefficients. These mathematically-oriented techniques can handle a relatively limited set of quantitative objectives and. in addition, require many simplifying assumptions. For this reason, the solutions generated by these techniques are of limited usefulness in actual cell design. This paper proposes an expert system approach to cell system design. The starting point for the expert system is the initial solution generated by traditional mathematical techniques. Based on a flexible set of user-driven quantitative and qualitative factors, the expert system evaluates these preliminary solutions for feasibility and quality. If the solutions are not satisfactory (infeasible or of low quality), the system suggests modifications.     

Combinatorial process and plant design for agile manufacture

The concept of combinatorial process, equipment and plant design is introduced and developed for the specific examples of fluid separations and crystallisation. It is shown that traditional methods of process design may miss options that are identified using the combinatorial approach. New options may lead to novel types of processes and equipment. Application of this methodology is suggested in terms of scanning the multi-dimensional space describing the process, equipment and plant attributes. The new approach is particularly appropriate for the design of agile plants for families of products and where decisions have to be made as how best to re-configure an existing facility to manufacture a new product.