A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition

This paper proposes a multi-objective hybrid artificial bee colony (MOHABC) algorithm for service composition and optimal selection (SCOS) in cloud manufacturing, in which both the quality of service and the energy consumption are considered from the perspectives of economy and environment that are two pillars of sustainable manufacturing. The MOHABC uses the concept of Pareto dominance to direct the searching of a bee swarm, and maintains non-dominated solution found in an external archive. In order to achieve good distribution of solutions along the Pareto front, cuckoo search with Levy flight is introduced in the employed bee search to maintain diversity of population. Furthermore, to ensure the balance of exploitation and exploration capabilities for MOHABC, the comprehensive learning strategy is designed in the onlooker search so that every bee learns from the external archive elite, itself and other onlookers. Experiments are carried out to verify the effect of the improvement strategies and parameters’ impacts on the proposed algorithm and comparative study of the MOHABC with typical multi-objective algorithms for SCOS problems are addressed. The results show that the proposed approach obtains very promising solutions that significantly surpass the other considered algorithms.     

Cloud manufacturing service composition and optimal selection with sustainability considerations: a multi-objective integer bi-level multi-follower programming approach

The process of service composition and optimal selection (SCOS) is an important issue in cloud manufacturing (CMfg). However, the current studies on CMfg and SCOS have generally focused on optimising the allocation of resources against quality of service (QoS), in terms of e.g. cost, quality, and time. They have seldom taken the perspective of sustainability into discussion, although sustainability is indispensable in the CMfg environment. Addressing this gap, we aim to (1) propose a comprehensive method to assess the sustainability of cloud manufacturing (SoM) in terms of the economic, environmental, and social aspects; (2) establish a multi-objective integer bi-level multi-follower programming (MOIBMFP) model to simultaneously maximise SoM and QoS from the perspectives of both platform operator and multiple service demanders; and (3) design a hybrid particle swarm optimisation algorithm to solve the proposed MOIBMFP model. The experimental results show that the proposed algorithm is more feasible and effective than the typical multi-objective particle swarm optimisation algorithm when solving the proposed model. In other words, the proposed model and algorithm suggest better alternatives to meet the needs of the platform operator and service demanders in the CMfg environment.     

Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm

Cloud Manufacturing (CMfg) ambitions to create dedicated manufacturing clouds (i.e. virtual enterprises) for complex manufacturing demands through the association of various service providers’ resources and capabilities. In order to insure a dedicated manufacturing cloud to match the level of customer’s requirements, the cloud service selection and composition appear to be a decisive process. This study takes common aspects of cloud services into consideration such as quality of service (QoS) parameters but extend the scope to the physical location of the manufacturing resources. Unlike the classic service composition, manufacturing brings additional constraints. Consequently, we propose a method based on QoS evaluation along with the geo-perspective correlation from one cloud service to another for transportation impact analysis. We also insure the veracity of the manufacturing time evaluation by resource availability overtime. Since the composition is an exhaustive process in terms of computational time consumption, the proposed method is optimised through an adapted Artificial Bee Colony (ABC) algorithm based on initialisation enhancement. Finally, the efficiency and precision of our method are discussed furthermore in the experiments chapter.     

A novel model for optimisation of logistics and manufacturing operation service composition in Cloud manufacturing system focusing on cloud-entropy

In recent years, economic globalisation and manufacturing resource globalisation as two key factors have driven enterprises to transform their business processes to survive in competitive environments. This transformation is challenging as manufacturing enterprises should maintain their support for their customers with high-quality products, lower cost, product customisation capabilities, and quick delivery. Moreover, globalisation has resulted in geographically distributed suppliers across the globe. This challenge will turn into a major research topic when paradigms like Cloud manufacturing are introduced. Cloud manufacturing is a new paradigm which provides ubiquitous, convenient, on-demand network access to a shared pool of configurable manufacturing resources. In this paper, to achieve the ideal goal of Cloud manufacturing, the concept of supplier network logistics planning and manufacturing service composition is focused. Every production task and logistics operation would be defined as a service considering operation process chart flows. The paper has proposed a mathematical model which selects an optimal set of manufacturing and logistics service composition in order to lower operation and logistics costs in operational perspective while fulfilling a novel idea for configured cloud entropy of logistics and operation suppliers. Finally, the paper has presented a numerical example and concluded the remarks, and outlined future research.     

Flower pollination algorithm: A novel approach for multiobjective optimization

Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.     

A novel cloud manufacturing service composition platform enabled by Blockchain technology

In the near future, manufacturing industries will be mostly recognised with characteristics like IoT, and massive data transactions. To fulfil these characteristics, paradigms like Cloud manufacturing, Industry 4.0 and smart factory have passed their preliminary steps to become the primary inspirations. Considering the nature of Cloud manufacturing which consists of a vast number of service providers and Service demanders being introduced to the manufacturing cloud, service composition problem is introduced. However, there is a big challenge for fulfilling the dynamic behaviour of parameters which change rapidly over time in the service composition problem. This paper challenges the centralised mechanism of service composition problem and introduces a novel platform entitled Blockchain-based service composition model (Block-SC) based on the Blockchain technology. Block-SC as a novel manufacturing architecture conquers the centralised mechanism by dividing the original service composition problem into multiple sub-problems each of which contains a small fraction of the service/task pool. The capabilities of the proposed platform are remarkable from two perspectives; first, it provides an effective mechanism for collaboration of service composition service providers with a service-oriented approach and from the second perspective, the optimality of service composition problem is profoundly affected considering the dynamic behaviour of Cloud manufacturing.     

One universal method of complex system reliability,maintainability, supportability,testability quotas design and trade-off based on improved flower pollination algorithm

For modern complex system, quality has become a serious and difficult problem that restricts the generation of combat effectiveness. Withal, based on domestic and foreign research and analysis, this paper puts forward a trade-off method for complex system reliability, maintainability, supportability, testability (RMST) design, which is suitable for product design stage. First, aiming at the uncertain problem of complex system constrained optimization, the good-point set population initialization, Deb feasibility comparison method and ε constraint method are integrated into the flower pollination algorithm, to construct the algorithm for RMST quotas design and trade-off. Second, taking the typical series-parallel repairable system as an example, one general model for complex system RMST quotas design and trade-off based on effectiveness is constructed. Finally, take an unmanned nuclear radiation detection system as a typical case, to verify its feasibility, advantages, and disadvantages.     

Cumulative manufacturing capabilities: an extended model and new empirical evidence

There has been intense debate in the manufacturing strategy literature on the way in which firms work on different manufacturing capabilities, with two opposing approaches considered – the trade-off model and the sand cone model. Analysis of these models has essentially been based on study of the links amongst four classic manufacturing capabilities (quality, delivery, flexibility, and cost efficiency) and has obviated the need to consider environmental protection as an important manufacturing capability. This study analyses the theoretical arguments and the prior empirical evidence on the two models, and proposes and tests an extended sand cone model which includes the environmental protection objective alongside the four traditional ones. The research uses structural equation modelling and data from a sample of 274 manufacturers to contribute additional empirical evidence on the existence of cumulative effects amongst manufacturing capabilities. It is observed that the predominant strategic model in these firms is one of multiple, non-incompatible capabilities with cumulative effects according to the following sequence: quality, delivery, flexibility, environmental protection, and cost efficiency.     

Performance computation methods for composition of tasks with multiple patterns in cloud manufacturing

Task composition in cloud manufacturing involves the selection of appropriate services from the cloud manufacturing platform and combining them to process the task with the purpose of achieving its expected performance. Calculation methods for achieving the performance expected by customers when the task has two or more composition patterns (e.g. sequential and switching pattern) are necessary because most tasks have multiple composition patterns in cloud manufacturing. Previous studies, however, have focused only on a single composition pattern. In this paper, we regard a task as a directed acyclic graph, and propose graph-based algorithms to obtain cost, execution time, quality and reliability of a task having multiple composition patterns. In addition, we model the task composition problem by introducing cost and execution time as performance attributes, and quality and reliability as basic attributes in the Kano model. Finally, an experiment to compare the performances of three metaheuristic algorithms (namely, variable neighbourhood search, genetic, and simulated annealing) is conducted to solve the problem. The experimental result shows that the variable neighbourhood search algorithm yields better and more stable solutions than the genetic algorithm and simulated annealing algorithms.     

Research on resources optimisation deployment model and algorithm for collaborative manufacturing process

Agility is the competitive advantage in the global manufacturing environment. It is believed that agility can be realised by networked manufacturing resource optimisation deployment. However, this is a challenge to us now. To solve this question, logical manufacturing unit and logical manufacturing process are proposed to decompose and model the networked manufacturing task, and networked manufacturing resources are organised and modelled based on physical manufacturing unit. During the deployment of manufacturing resources to the task, many factors should be taken into consideration. Of these, manufacturing cost, time and quality are the most important factors. In this paper, before these factors are considered, networked manufacturing resources pre-deployment is carried out to find the candidate manufacturing resources on the basis of manufacturing abilities. Then, resources optimisation deployment is modelled as a multi-objectives optimisation. This optimisation problem is solved based on genetic algorithm after transforming the multi-objectives optimisation problem to a single objectives optimisation problem. Although we may not find the optimal solution for the problem by genetic algorithm, the better and feasible solution is produced. Thus, this algorithm is efficient and can be applicable to practical problem. At last, an illustrative example is presented to show the application of the proposed algorithm.     

Collaboration of large equipment complete service under cloud manufacturing mode

This paper focuses on the need of the large equipment manufacturing industry to adapt collaborative operation to transform the industry to cloud manufacturing services and to solve the new problem of federal resources coordination in complete service operation. We systematically study federal resources cooperation under cloud manufacturing mode to complete a large complex project. The primary research contents are divided into four points. First, a system structure of cloud manufacturing service mode is presented. Second, a synergy logic framework from the global system perspective is designed based on generalised partial global planning. Third, a multi-level system coordination mechanism is established by integrating various methods, including the bidding game mechanism for enterprise external resources, the planning control mechanisms for enterprise internal resource and the global collaborative optimisation mechanism for enterprise global federal resources. Finally, a cloud manufacturing service platform for a typical enterprise is developed by combining theory with practice. The results can realise collaborative management in resource selection and configuration, service process planning control and service information feedback in cloud manufacturing service, as well as achieve overall synergy effect for the system.     

Cellular manufacturing system design using grouping efficacy-based genetic algorithm

This paper presents an algorithm for the design of manufacturing cells and part families. This algorithm is suitable for arriving at a good block diagonal structure for a cellular manufacturing design problem with part machine incidence matrix as input. The objective of this algorithm is the maximisation of grouping efficacy (GE), which is one of the most widely used measures of quality for cellular configurations. Assignment of machines to cells is using genetic algorithm, and part assignment heuristic is based on an effective customised rule. A comparison of the proposed algorithm is made with seven other methods of cell formation by taking 36 problems from the literature and found that the proposed algorithm is performing much better than the others. Finally, the algorithm is extended to form configurations with good GE when there are alternative routes.     

一种QoS感知的语义Web服务组合群决策算法

针对QoS信息不确定和存在多个决策者的语义Web服务组合问题,基于多属性群决策理论给出了一个自治的语义Web服务组合群决策算法(AGSC).该算法能够对以实数型、区间型和语言型数据描述的复杂的QoS信息进行综合评估,从而为多客户提供正确、高效的决策支持,为其优选出最佳的组合服务执行计划.利用真实Web服务的质量对该算法进行了实验验证,结果表明该算法具有优秀的决策灵敏性和稳定性,并能有效地反映决策群中个体角色的变化.     

支持服务关联的全局最优服务选择算法

针对面向服务的军事综合电子信息系统候选服务的服务质量往往依赖于其他候选服务的问题,提出了一种支持服务关联的全局最优服务选择方法.该方法将基于QoS的军事信息服务选择问题建模为带QoS约束的多目标组合优化问题,采用基于独立成分分析(ICA)的多目标分布估计算法同时优化多个目标函数,最终产生一组满足约束条件的Pareto最优解服务组合集.对比实验结果表明,基于ICA的多目标分布估计算法通过概率图模型可以描述服务间的相互关系,可以更好地解决存在服务关联的组合服务选择问题,具有良好的有效性和可行性.     

Optimal part orientation in layered manufacturing using evolutionary stickers-based DNA algorithm

Over recent years, layered manufacturing (LM) has been one of the most important emerging research areas, as well as practice perspective, owing to its capability to reduce the product development time, and therefore time-to-market. In LM, owing to the significant role played by the part orientation in the successful and efficient reduction of the staircase effect, the determination of optimal part orientation is a matter of paramount importance. In this research, the dual parameters problem has been modelled, taking into consideration the constraints pertaining to the rotation of the computer aided design (CAD) model about two axes, while aiming to optimize the objective function that involves layered process error as well as build time. The current paper presents an advanced stickers-based DNA algorithm (SDNA) inspired by the characteristics of deoxyribonucleic acid (DNA) as a tool to achieve the optimal orientation during fabrication of a part. The salient feature of the proposed algorithm is the use of stickers along with DNA memory strand, which are responsible for the representation of information. Moreover, fundamental operations are applied to manipulate the positions of the stickers in essentially all the possible ways. The performance of SDNA has been tested on two standard case studies and the comparisons are made with results obtained from genetic algorithm (GA). The results clearly demonstrate the efficacy of proposed algorithm over GA when applied to the underlying problems.     

Research on optimal decision-making of cloud manufacturing service provider based on grey correlation analysis and TOPSIS

It is of great practical significance to optimise the decision-making of cloud manufacturing service providers, which can ensure the efficient operation of the cloud manufacturing services. In order to effectively optimise, this paper constructs the evaluation index system, sorts out 7 important evaluation indexes. A method is proposed on the basis of the TOPSIS method and Grey Correlation Analysis (GRA), which is a decision-making method combining static distance and dynamic trend, and make the evaluation results more reasonable. In addition, in order to reflect the true weight of the high dimensional index, the evaluation model of projection pursuit index is established. The improved Particle Swarm Optimisation (PSO) algorithm is used to optimise the projection index function and model parameters, and to obtain the objective weights of evaluation indexes. Then the objective weights combine with the subjective weights obtained by the Analytic hierarchy process (AHP), which makes the weights of the evaluation indexes achieve the unity of subjective and objective. Finally, 20 sets of simulation examples are given to illustrate the feasibility and effectiveness of the proposed method.     

A robust cellular manufacturing system design for dynamic part population using a genetic algorithm

As changing conditions prevail in the manufacturing environment, the design of cellular manufacturing systems, which involves the formation of part families and machine cells, is difficult. This is due to the fact that the machines need to be relocated as per the requirements if adaptive designs are used. This paper presents a new approach (robust design) for forming part families and machine cells, which can handle all the changes in demands and product mixes without any relocations. This method suggests fixed machine cells for the dynamic nature of the production environment by considering a multi-period forecast of product mix and demand. A genetic algorithm based solution procedure is adopted to solve the problem. The results thus obtained were compared with the adaptive design proposed by Wicks and Reasor (1999 Wicks, EM and Reasor, RJ. 1999. Designing cellular manufacturing systems with dynamic part populations. IIE Trans., 31: 11–20. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). It is found that the robust design performs better than the adaptive design for the problems attempted.     

GA-BHTR: an improved genetic algorithm for partner selection in virtual manufacturing

An evolutionary genetic algorithm maintained using the binary heap and transitive reduction (GA-BHTR) method for addressing the partner selection problem (PSP) in a virtual enterprise is proposed. In order to reduce the time complexity of PSP, an algorithm for simplifying the directed acyclic graph that represents the precedence relationship among the subprojects in PSP is first designed. Different from the traditional regular GA, in order to avoid solutions from converging to a constant value early during evolution, multiple communities are used instead of a single community in GA-BHTR. The method and algorithms to distribute the individuals to the multiple communities while maximising the differences among the different communities are proposed. The concept of the catastrophe is introduced in the proposed GA-BHTR in order to avoid the solutions from converging to a local best solution too early after several generations of evolution. In order to maintain the capacity of the community (i.e. the number of individuals existing in a community) at a constant value while enhancing the diversity of the proposed GA-BHTR, an algorithm using the binary heap to maintain the data is designed. Simulation and experiments are conducted to test the effectiveness and performance of the proposed GA-BHTR for addressing PSP.     

Efficient optimization of process parameters in shadow mask manufacturing using NNPLS and genetic algorithm

A shadow mask, the primary component of a cathode ray tube (CRT), is used to prevent the outer edges of electron beams from hitting incorrect phosphor dots. It is fabricated by means of a photo-etching process consisting of a few hundred/thousand process parameters. A primary concern in the management of the process is to determine the optimal process parameter settings necessary to sustain the desired levels of product quality. The characteristics of the process, including a large number of process parameters and collinear observed data, make it difficult to accomplish the primary concern. To cope with the difficulties, a two-phase approach is employed that entails the identification of a few critical process parameters, followed by determination of the optimal parameter settings. The former is obtained through the operator's domain knowledge and the NNPLS-based prediction model built between process parameters and quality defects. The latter is obtained by solving an optimization problem using a genetic algorithm (GA). A comparative study shows that the proposed approach improves product quality greatly in the shadow-mask manufacturing process.     

Optimal resource allocation on grid systems for maximizing service reliability using a genetic algorithm

Grid computing system is different from conventional distributed computing systems by its focus on large-scale resource sharing and open architecture for services. The global grid technologies and the Globus Toolkit in particular, are evolving toward an open grid service architecture (OGSA) with which a grid system provides an extensible infrastructure so that various organizations can offer their own services and integrate their resources. Hence, this paper aims at solving the problem of optimally allocating services on the grid to maximize the grid service reliability. Since no existing study has analyzed the grid service reliability, this paper develops initial modeling and evaluation algorithms to evaluate the grid service reliability. Based on the grid service reliability evaluation, we present an optimization model for the grid service allocation problem and develop a genetic algorithm (GA) to effectively solve it. A numerical example is given to show the modeling procedures and efficiency of the GAs.