Distributed Manufacturing Environment Research Articles

Memetic social spider optimization algorithm for scheduling two-stage assembly flowshop in a distributed environment

Abstract This paper studies the distributed two-stage assembly flowshop problem with separate setup times, which is a generalisation for the regular two-stage assembly flowshop problem in the distributed manufacturing environment. The optimization objective is to find a suitable job schedule such that the criterion of total completion time is minimized. To deal with such a problem, we propose a novel memetic algorithm (MA) based on a recently developed social spider optimization (SSO). To the best of our knowledge, it is the first effort to explore the SSO-based MA (MSSO) and to apply SSO in the field of combinational optimization. In the proposed MSSO algorithm, we first modify the original version of SSO to adapt to the distributed problems, and then integrate two improvement techniques, problem-special local search and self-adaptive restart strategy, within MA framework. In the numerical experiment, the parameters used in MSSO are calibrated and suitable parameter values are suggested based on the Taguchi method. Experimental results and comparisons with the existing algorithms validate the effectiveness and efficiency of the proposed MSSO for addressing the considered problem. In addition, the effect of problem scale parameters on MSSO and the effectiveness of the proposed improvement techniques are also investigated and demonstrated.

Ranked sequence positional weight heuristic for simultaneous balancing and scheduling jobs in a distributed manufacturing environment

In the current competitive and globalized manufacturing scenario Distributed Manufacturing Environments are increasing, and it turns mandatory to explore improved operational approaches. For enhanced simultaneous balancing and scheduling jobs in a Distributed Manufacturing Environment (DME) a mathematical model of Ranked Sequence Positional Weight (RSPW) is proposed. The model capabilities are analysed through a test problem and the results have demonstrated that the proposed RSPW heuristics mathematical model do perform better than other competitive approaches.

Telefacturing Based Distributed Manufacturing Environment for Optimal Manufacturing Service by Enhancing the Interoperability in the Hubs

Recent happenings are surrounding the manufacturing sector leading to intense progress towards the development of effective distributed collaborative manufacturing environments. This evolving collaborative manufacturing not only focuses on digitalisation of this environment but also necessitates service-dependent manufacturing system that offers an uninterrupted approach to a number of diverse, complicated, dynamic manufacturing operations management systems at a common work place (hub). This research presents a novel telefacturing based distributed manufacturing environment for recommending the manufacturing services based on the user preferences. The first step in this direction is to deploy the most advanced tools and techniques, that is, Ontology-based Protégé 5.0 software for transforming the huge stored knowledge/information into XML schema of Ontology Language (OWL) documents and Integration of Process Planning and Scheduling (IPPS) for multijobs in a collaborative manufacturing system. Thereafter, we also investigate the possibilities of allocation of skilled workers to the best feasible operations sequence. In this context, a mathematical model is formulated for the considered objectives, that is, minimization of makespan and total training cost of the workers. With an evolutionary algorithm and developed heuristic algorithm, the performance of the proposed manufacturing system has been improved. Finally, to manifest the capability of the proposed approach, an illustrative example from the real-time manufacturing industry is validated for optimal service recommendation.

Development of a novel cloud-based multi-tenant model creation service for automatic virtual metrology

AVM (Automatic Virtual Metrology) is the highest-level technology for VM (Virtual Metrology) applications from the perspective of automation. Its various automatic capabilities could facilitate fast factory-wide deployment and operations of VM systems. AVM systems have been successfully applied to the semiconductor, TFT-LCD, solar-cell, and machining industries for on-line monitoring the production quality of workpieces. However, in its past industrial applications, the model creation (MC) functionality of the existing AVM system encountered several limitations, such as being a standalone application and confined to be used in situ in a factory, no support for multiuser model creation, wasting computing resources, etc., which could diminish the applicability of the existing AVM system in current global and distributed manufacturing environments. Thus, this paper is dedicated to tackling the problem of how to systematically and effectively overcome MC-related limitations of the existing AVM system so that it can robustly support multiple users across factories to create their VM models simultaneously in distributed manufacturing settings. By leveraging the advantages of cloud computing and several IT technologies (including virtualization software, XML, Web Service, Multi-tenancy technique, and HTML5), this paper proposes a novel cloud-based multi-tenant model creation service (i.e., CMMCS) for AVM. The proposed CMMCS contains a cloud-based system architecture, functional frameworks of its key components, several functional mechanisms, and HTML5-based Web GUIs. Testing results in an industrial case study that creates VM models using the CMMCS for CNC machine tools in machining wheel rims of automobiles in a factory in Taiwan demonstrate that the CMMCS can allow multiple users from different tenants to simultaneously create their VM models, while enabling the MC cloud services to be more robust for processing MC requests, having higher CPU-usage rates in the underlying virtual machines, and achieving better cross-platform usage, compared to the original MC functionality. This paper has provided a feasible solution to systematically and effectively remedying the MC-related limitations of the existing AVM system. The existing VM-related literature mainly focused on the development of VM models. To our knowledge, no papers have coped with issues addressed in this paper by leveraging cloud computing. The results of this paper can be a useful reference for industrial practitioners to construct AVM systems which support multi-tenant or multiuser model creation. A novel cloud-based multi-tenant model creation service (CMMCS) for automatic virtual metrology (AVM) has been proposed.Designs of key components and mechanisms have been presented to effectively address the existing AVM's MC limitations.CMMCS can support multi-tenant/user model creation for AVM, being applicable to the distributed manufacturing context.A case study of creating VM models for machine tools in machining wheel rims of autos demonstrates the efficacy of CMMCS.Results can be useful references for industrial practitioners to construct AVM systems supporting multi-tenant MC.

A time-aware hyperlink-induced topic search-based reputation evaluation method for optimal manufacturing service recommendation in distributed peer-to-peer networks

With the explosive growth of manufacturing services on a global scale, it has become an enormous challenge to recommend optimal manufacturing service in distributed manufacturing environments. Reputation evaluation is a key step to make optimal manufacturing service recommendation. However, the reputation values calculated through conventional reputation evaluation methods are static and local, and have a severe impact on the effectiveness of manufacturing service recommendation. This article presents a novel and effective reputation evaluation method for optimal manufacturing service recommendation in distributed peer-to-peer networks. The main contribution of the proposed approach is that it makes it possible to calculate the global and dynamic reputation values of enterprises by employing a time-aware hyperlink-induced topic search algorithm. This algorithm not only disseminates the reputation value of transaction enterprises, but also takes account of the temporal dimension in the process of computing reputation values. A concrete example demonstrates the feasibility of calculating reputation values of the proposed method, and an evaluation experiment reveals that both of recall rate and precision rate are improved, employing our proposed method for recommending manufacturing service.

An Extended Genetic Algorithm for Distributed Integration of Fuzzy Process Planning and Scheduling

The distributed integration of process planning and scheduling (DIPPS) aims to simultaneously arrange the two most important manufacturing stages, process planning and scheduling, in a distributed manufacturing environment. Meanwhile, considering its advantage corresponding to actual situation, the triangle fuzzy number (TFN) is adopted in DIPPS to represent the machine processing and transportation time. In order to solve this problem and obtain the optimal or near-optimal solution, an extended genetic algorithm (EGA) with innovative three-class encoding method, improved crossover, and mutation strategies is proposed. Furthermore, a local enhancement strategy featuring machine replacement and order exchange is also added to strengthen the local search capability on the basic process of genetic algorithm. Through the verification of experiment, EGA achieves satisfactory results all in a very short period of time and demonstrates its powerful performance in dealing with the distributed integration of fuzzy process planning and scheduling (DIFPPS).

Data mining based multi-level aggregate service planning for cloud manufacturing

Cloud manufacturing (CMfg) promotes a dynamic distributed manufacturing environment by connecting the service providers and manages them in a centralized way. Due to the distinct production capabilities, the service providers tend to be delegated services of different granularities. Meanwhile, users of different types may be after services of different granularities. A traditional aggregate production planning method is often incapable of dealing with type of problems. For this reason, a multi-level aggregate service planning (MASP) methodology is proposed. The MASP service hierarchy is presented, which integrates the services of different granularities into a layered structure. Based on this structure, one of data mining technologies named time series is introduced to provide dynamic forecast for each layer. In this way, MASP can not only deal with the services of multi-granularity, but also meet the requirements of all related service providers irrespective of their manufacturing capabilities. A case study has been carried out, showing how MASP can be applied in a CMfg environment. The results of the prediction are considered reliable as the order of magnitude of the production for each service layer is much greater than that of the corresponding mean forecast error.

Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms

In contrast to traditional job-shop scheduling problems, various complex constraints must be considered in distributed manufacturing environments; therefore, developing a novel scheduling solution is necessary. This paper proposes a hybrid genetic algorithm (HGA) for solving the distributed and flexible job-shop scheduling problem (DFJSP). Compared with previous studies on HGAs, the HGA approach proposed in this study uses the Taguchi method to optimize the parameters of a genetic algorithm (GA). Furthermore, a novel encoding mechanism is proposed to solve invalid job assignments, where a GA is employed to solve complex flexible job-shop scheduling problems (FJSPs). In addition, various crossover and mutation operators are adopted for increasing the probability of finding the optimal solution and diversity of chromosomes and for refining a makespan solution. To evaluate the performance of the proposed approach, three classic DFJSP benchmarks and three virtual DFJSPs were adapted from classical FJSP benchmarks. The experimental results indicate that the proposed approach is considerably robust, outperforming previous algorithms after 50 runs.

An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment

Global manufacturing companies have some pressing needs to improve production visibility and decision-making performance by implementing effective production monitoring and scheduling. This paper proposes a radio frequency identification (RFID)-based intelligent decision support system architecture to handle production monitoring and scheduling in a distributed manufacturing environment. A pilot implementation of the architecture is reported in a distributed clothing manufacturing environment. RFID and cloud technologies were integrated for real-time and remote production capture and monitoring. Intelligent optimization techniques were also implemented to generate effective production scheduling solutions. A prototype system with remote monitoring and production scheduling functions was developed and implemented in a distributed manufacturing environment, which demonstrated the effectiveness of the architecture. The proposed architecture has good extensibility and scalability, which can easily be integrated with production decision-making as well as production and logistics operations in the supply chain. Lastly, this paper discusses the difficulties encountered and lessons learned during system implementation and the managerial implications of the proposed architecture.

Self-Organized P2P Approach to Manufacturing Service Discovery for Cross-Enterprise Collaboration

The combination of service-oriented architecture (SOA) and peer-to-peer (P2P) architecture plays a promising role in distributed manufacturing environments in that the peer service can be used to facilitate the integration and discovery of distributed manufacturing resources and achieve communication and collaboration across distributed virtual enterprises. However, the large size, dynamic nature, and heterogeneous expression of distributed manufacturing resources bring forth a serious challenge in scalability and efficiency. This paper presents a self-organized P2P framework that supports scalable and efficient manufacturing service (MS) discovery for cross-enterprise collaboration by forming and maintaining autonomous enterprise peer groups (PG). Each enterprise exhibits as a peer that provides some sharable MSs that are represented comprehensively and formally with a generalized ontology. Each enterprise PG dynamically clusters a set of enterprise peers offering semantically similar MSs, and elects the most reputed peer through multicriteria trust evaluation as its core (i.e., super peer, SP). Then, a MS request can be first routed to the suitable SP and further to its leaf peer in a systematic way, thus supporting efficient service discovery. A prototype system is implemented on JXTA for real application and validated through an experimental case study.

Selection of Distributed Manufacturing Environments Based on Genetic Algorithm for Automobile Gears

Aiming at the optimization problem of distributed manufacturing environments, a mathematic model based on genetic algorithm (GA) was established. When establishing the mathematic model, the minimizing production cost and the minimizing production time were selected as the optimization objects; at the same time, the two-dimensional symbol string encoded by integer encoding was used to represent chromosome. To optimize the distributed manufacturing environments, the factors influencing production cost can be classified as four indices: machine change cost, tool and grinding wheel change cost, set-up change cost, and machining cost. The factors influencing production time can be classified as four indices: machine change time, tool and grinding wheel change time, set-up change time, and machining time. The processing of four-grade intermediate shaft gear of an automobile transmission was used as an example to prove the validity of the proposed model, and the analysis results of examples show that the optimization model can select the optimal manufacturing environment based on the different demands effectively and objectively.

A heuristic algorithm for the distributed and flexible job-shop scheduling problem

The distributed manufacturing takes place in a multi-factory environment including several factories, which may be geographically distributed in different locations, or in a multi-cell environment including several independent manufacturing cells located in the same plant. Each factory/cell is capable of manufacturing a variety of product types. An important issue in dealing with the production in this decentralized manner is the scheduling of manufacturing operations of products (jobs) in the distributed manufacturing system. In this paper, we study the distributed and flexible job-shop scheduling problem (DFJSP) which involves the scheduling of jobs (products) in a distributed manufacturing environment, under the assumption that the shop floor of each factory/cell is configured as a flexible job shop. A fast heuristic algorithm based on a constructive procedure is developed to obtain good quality schedules very quickly. The algorithm is tested on benchmark instances from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic is computationally efficient and promising for practical problems.

A hybrid Tabu sample-sort simulated annealing approach for solving distributed scheduling problem

The distributed scheduling problem has been considered as the allocation of a task to various machines in such a way that these machines are situated in different factories and these factories are geographically distributed. Therefore distributed scheduling has fulfilled various objectives, such as allocation of task to the factories and machines in such a manner that it can utilise the maximum resources. The objective of this paper is to minimise the makespan in each factory by considering the transportation time between the factories. In this paper, to address such a problem of scheduling in distributed manufacturing environment, a novel algorithm has been developed. The proposed algorithm gleans the ideas both from Tabu search and sample sort simulated annealing. A new algorithm known as hybrid Tabu sample-sort simulated annealing (HTSSA) has been developed and it has been tested on the numerical example. To reveal the supremacy of the proposed algorithm over simple SSA and Tabu search, more computational experiments have also been performed on 10 randomly generated datasets.

A service- and multi-agent-oriented manufacturing automation architecture: An IEC 62264 level 2 compliant implementation

This paper describes an approach for developing and implementing a service- and multi-agent-oriented manufacturing automation architecture, being particularly focused on functional features of the IEC 62264 L2 standard. The proposed SoA architecture facilitates managing and controlling networked smart automation components in a distributed manufacturing environment. The services, generated and exposed by automation components located on the levels 1 and 2 of the ISA’95-compliant enterprise architecture, are essential part of an “Automation Service Cloud”, which is the result of the virtualization of the physical production environment. Moreover, these services can be accessed, requested and used by other components of these and upper levels, i.e., MES (Manufacturing Executing System) distributed components like a collaborative multi-agent-based manufacturing scheduling and dispatching system. Using the main characteristics of the service-orientation, the automation services can be composed and orchestrated in the cloud, creating emergent behaviors, which is a direct result of the virtualization, i.e., transforming the mechatronics/production layout into an automatically collaborative network.

A reputation-based peer-to-peer architecture for semantic service discovery in distributed manufacturing environments

The rapidly increasing volume of today’s global manufacturing resources is demanding new approaches for resource location or discovery in distributed manufacturing environments. This article presents an ontology-based service-oriented peer-to-peer architecture for semantic discovery of manufacturing services across virtual enterprises in distributed manufacturing environments. Each enterprise exhibits as a peer that publishes sharable manufacturing services through its private service registry. The peers sharing similar ontological interests through ontological directory are clustered together, and the most reputed peer is elected as the super peer through multicriteria decision making among related trust parameters. All super peers form a reputed distributed hash table–based structured peer-to-peer overlay, and the leaf peers are communicated with and controlled by respective super peers. Then, a manufacturing service request can be first routed to the suitable super peer through structured indexing and further to its leaf peer through client/server communication, thus supporting efficient service discovery. The case study of the prototype system is presented to validate the applicability of the proposed approach for industrial application.

A time-aware Bayesian approach for optimal manufacturing service recommendation in distributed manufacturing environments

With the growing emergence of distributed collaborative manufacturing systems, the utilization of service-oriented manufacturing to offer seamless access to a variety of complex, distributed and dynamic manufacturing resources has become a challenging issue. This paper presents a novel time-aware probabilistic Bayesian approach for recommending a few optimal manufacturing services based on the user preference for an initial manufacturing service. The Bayesian approach operates over a comprehensive, formal representation of manufacturing services, which adds the time-aware probability of satisfied service execution in manufacturing service ontology to take into account the statistical nature of the dynamic manufacturing environment. The Bayesian approach is useful because it infers a few top ranked hypotheses of manufacturing service that have the largest probability as the optimal selection in a specified context of user preference. A prototype system is built and validated with an illustrative example from manufacturing industry to demonstrate the feasibility of the proposed approach for optimal service recommendation.

ManuHub: A Semantic Web System for Ontology-Based Service Management in Distributed Manufacturing Environments

This correspondence paper presents a prototype Semantic Web system called ManuHub (i.e., Manufacturing Hub) that administers distributed manufacturing services provided by ubiquitous virtual enterprises and allows a function of exploration with friendly graphical user interfaces. ManuHub is practical and useful in creating local manufacturing service hubs via ontology- and constraint-based modeling of the distributed manufacturing services and semantic annotation of the acquired manufacturing services. In particular, ManuHub facilitates efficient, accurate, and automatic retrieval of the required manufacturing services derived from the semantic matchmaking of manufacturing service capabilities. In other words, ManuHub functions as the “binding glue” between collaborative enterprises in order to achieve seamless manufacturing interoperability. The prototype system was built and evaluated with an example illustrated from manufacturing industries so as to demonstrate the feasibility of the proposed approach.

A Multi-Agent Self Correcting Architecture for Distributed Manufacturing Supply Chain

Nowadays the supply chain for distributed manufacturing is gaining attention of the researchers worldwide. Realizing its significance this article proposes a self correcting multi-agent architecture for the supply chain for the distributed manufacturing environment. The main aim of the proposed architecture is to generate an effective manufacturing plan while exploring the algorithm portfolio concept to minimize the manufacturing and supply chain costs. This architecture focuses on automatic selection of best techniques and suppliers while making the tradeoff between the cost, availability, reliability, distance and quality of the products supplied. When the new order arrives, the proposed architecture explores the delicacy of the skill exploitation algorithm to simultaneously incorporate the new and old orders. This will help manufacturing firms to execute their manufacturing processes efficiently.

Developing manufacturing ontologies for knowledge reuse in distributed manufacturing environment

The use of ontologies for knowledge sharing and distributed collaboration has been widely recognised in the knowledge modelling community, but the lack of a systematic and constructive methodology for developing manufacturing ontologies has impeded their wide usage for knowledge reuse in distributed manufacturing environments. This paper presents a constructive, two-level knowledge modelling approach to systematically develop manufacturing ontologies using both software engineering and Semantic Web paradigms. The UML/OCL (Unified Modeling Language/Object Constraint Language)-based object modelling is used first to serve as a graphical and structured basis for conceptual communication between domain experts and knowledge engineers. The OWL/SWRL (Web Ontology Language/Semantic Web Rule Language)-based ontology modelling then extends the UML/OCL-based object models with added semantics using a progressive, semantics-oriented knowledge acquisition method. An illustrative example for manufacturing ontology development in the manufacturing industry for producing electronic connectors is used to demonstrate the practicality of the proposed approach.

A new approach for integrating process planning with scheduling

Integrated process planning with scheduling is a core activity in the manufacturing chain. Modern manufacturing processes require higher flexibility and capability of supporting distributed activities. In this paper, a novel STEP-NC Compliant Process Planning and Scheduling Algorithm (STEP-NCPPSA) is proposed to meet such requirements. The STEP-NCPPSA utilises STEP-NC compliant data models to represent the related manufacturing information. STEP-NCPPSA aims to achieve an Integrated, Intelligent, Informative and Interoperable (I4) manufacturing in a distributed manufacturing environment. Three phases, STEP-NC initial process planning, initial bidding, and Tree-based Genetic Algorithm optimisation are naturally integrated by the supporting STEP-NC concepts. STEP-NCPPSA is also capable of handling shop-floor information and re-process planning and scheduling to adapt to shop floor changes.