Service Composition And Optimal Selection Research Articles

A multi-objective service composition optimization method considering multi-user benefit and adaptive resource partitioning in hybrid cloud manufacturing

Cloud manufacturing (CMfg) is a new network manufacturing mode, where one of the essential issues is service composition and optimal selection (SCOS), attracting a wide range of scholars' attention and research. At present, scholars at home and abroad mainly focus on studying the SCOS model in a single type of cloud environment, which integrates and optimizes large group enterprises' internal resources and capabilities. However, since the characteristics and objectives of the SCOS problem are different in different cloud environments (private, public, and hybrid cloud), there are many dynamic switching scenarios of manufacturing resources in different cloud environments. At this time, SCOS models in a single type of cloud environment are no longer applicable. Thus, how to realize the maximization of multi-stakeholder benefits through the adaptive partitioning of manufacturing resources in different cloud environments is an urgent problem to be solved. To fill the gap, a multi-objective service composition optimization method considering multi-user benefit and adaptive resource partitioning (SCOS-MUB-ARP) is proposed for quickly obtaining the optimal service composition that maximizes the benefits of multi-stakeholder in hybrid cloud manufacturing. To address the SCOS-MUB-ARP, a multi-objective optimization algorithm based on a combination of the NSGA-II algorithm and the mayfly algorithm (MMA-NSGA2) with several optimization strategies is presented, which contains several problem-specific optimization strategies. Numerical experiments and application cases show the effectiveness of the proposed MMA-NSGA2 in optimizing and balancing multi-objectives, compared with some well-known algorithms.

An adaptive robust service composition and optimal selection method for cloud manufacturing based on the enhanced multi-objective artificial hummingbird algorithm

The smooth execution of tasks in the cloud manufacturing (CMfg) process can be significantly affected by various real-world uncertainties, resulting in inefficiencies or even failures of the composite manufacturing service (CMS). Nevertheless, current research primarily concentrates on post-exception emergency handling methods, heavily dependent on the planned CMS and real-time status of manufacturing resources, often leading to suboptimal temporary adjustments or even the inability to find a viable solution. Hence, there is an urgent necessity to enhance the robustness of the planned CMS during the service composition and optimal selection (SCOS) phase. In pursuit of this goal, the paper initially presents an adaptive robust service composition and optimization selection (ARSCOS) model. This model dynamically determines whether to await exception resolution or invoke an alternative service, considering the circumstances when a service exception arises and employing task delay time to characterize the robustness of a specific CMS. Subsequently, an enhanced multi-objective artificial hummingbird algorithm (EMOAHA) is devised for this model. Finally, the efficacy of EMOAHA is demonstrated by applying it to nine different scales of civilian unmanned aerial vehicle manufacturing SCOS problems and 17 benchmark functions. The results illustrate that the proposed EMOAHA exhibits superior convergence and diversity in comparison to the other 11 classical intelligent optimization algorithms.

Service Composition and Optimal Selection of Low-Carbon Cloud Manufacturing Based on NSGA-II-SA Algorithm

As a new model of networked manufacturing services, cloud manufacturing (CMfg) aims to allocate enterprise manufacturing resources, realize rational utilization of manufacturing resources, and adapt to increasingly complex user needs. However, previous studies on service composition and optimal selection (SCOS) in CMfg environments do not incorporate carbon emissions into the quality of service (QoS) evaluation indicators. Therefore, a SCOS model for CMfg under a low-carbon environment is firstly proposed in this paper. Secondly, based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) algorithm, a hybrid multi-objective evolutionary algorithm, named the NSGA-II-SA algorithm, is proposed to solve the model and obtain the Pareto optimal solution set. Then, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set, so as to make the final decision. Finally, taking natural gas cylinder head production as an example, the proposed algorithm is compared with other algorithms, and the results show that the proposed algorithm can obtain more non-dominated solutions, and the quality of the solutions in the four dimensions is better than the other. Therefore, it is proved that the proposed algorithm has better comprehensive performance in SCOS under a low-carbon environment.

Bi-objective service composition and optimal selection for cloud manufacturing with QoS and robustness criteria

As an important part of cloud manufacturing, service composition and optimal selection (SCOS) has attracted many scholars’ attentions. Although there are a large number of researches devoted to this field, plenty of challenges still remain, such as the difficulties of coping with real-life uncertainties and improving efficiency in making decisions. To this end, a bi-objective service composition and optimal selection (BoSCOS) problem which takes both QoS and robustness into account is firstly studied in this paper, its mathematical model is established after the two criteria are constructed. Then, a strengthened multi-objective gray wolf optimizer (SMOGWO) is developed to solve the above model with high efficiency, in which three improvement strategies are introduced to the original MOGWO: (1) the opposition based learning (OBL) strategy is adopted to improve the quality of initial population; (2) a leader update strategy is designed to explore better search leaders and balance the exploitation and exploration abilities; (3) an improved archiving strategy is developed to retain all possible dominant individuals, so as to improve the population diversity based on the Pareto dominance. Finally, the effectiveness of the proposed algorithm is verified on 8 BoSCOS problems with different scales and 17 benchmark functions, results show that the improvement strategies are effective and SMOGWO is superior to its competitors in terms of convergence and population diversity.

A variable-length encoding genetic algorithm for incremental service composition in uncertain environments for cloud manufacturing

Service composition and optimal selection (SCOS) plays a crucial role in cloud manufacturing (CMfg). While the existing service composition methods are hard to address the changes and uncertainties of CMfg dynamic environment. Therefore, a variable-length encoding genetic algorithm for structure-varying incremental service composition (ISC-GA) is proposed in this paper. Specifically, a novel variable-length encoding scheme containing structural information is proposed to describe the uncertain and changing process model. And the improved crossover and mutation algorithm suitable for individuals with nonlinear varying structure and incremental service composition is designed. It is realized by optimizing both the process structure and service instance combinations, and overcomes the drawbacks resulted from single preset process structure. Due to the difficulty of fitness computation caused by uncertain process structures, novelty is introduced as a new evolutionary pressure, and a novel framework for ISC-GA is presented, which helps to find both novel and high-performance solutions. Experimental results indicate the effectiveness of the proposed approach.

A Manufacturing SCOS Model (MSCOS) Based on the Similarity of Parameter Sequences Between Tasks and Service Composition

Service composition and optimal selection (SCOS) is a core issue in cloud manufacturing (CMfg) when integrating distributed manufacturing services for complex manufacturing tasks. Generally, a set of recommended task parameter sequences (Tps) will be given when publishing manufacturing tasks. The similarity between the service composition parameter sequence (SCps) and Tps also reflects the rationality of the service composition. However, various evaluation models based on QoS have been proposed, ignoring the rationality between the Tps and SCps. Considering the similarity of the Tps and SCps in an evaluation model, we propose a manufacturing SCOS framework called MSCOS. The framework includes two parts: an evaluation model and an algorithm for both optimization and selection. In the evaluation model, based on the numerical proximity and geometric similarity between the Tps and SCps, improving the technique for order preference by similarity to an ideal solution (TOPSIS) with the grey correlation degree (GC), we propose the GC&TOPSIS (GTOPSIS). In the optimization and selection algorithm, an improved flower pollination algorithm (IFPA) is proposed to achieve optimization and selection based on polyline characteristics between the fitness values in the population. Experiments show that the MSCOS evaluation effect and optimal selection offer better performance than commonly used algorithms.

A discrete manufacturing SCOS framework based on functional interval parameters and fuzzy QoS attributes using moving window FPA

Manufacturing service composition and optimal selection (SCOS) is a key technology that improves resource utilization and reduces the cost in discrete manufacturing. However, the lack of evaluation of the service composition function and the unconformity of the actual composition vague characteristics, resulting in the incomplete evaluation of the service composition. Additionally, various optimization and selection algorithms have defects of premature convergence and low efficiency. At the same time, the fitness value distribution of the service composition has a non-linear characteristic. In this article, a framework called discrete manufacturing SCOS (DMSCOS) is proposed to overcome these issues. DMSCOS uses the functional interval parameter and fuzzy QoS attribute aware evaluation model (FIPFQA) to achieve composition evaluation and introduces a moving window flower pollination algorithm (MWFPA) to achieve optimization and selection for the non-linear characteristic population. Experiments show that DMSCOS has good performance for optimization and selection. The FIPFQA has a good effect on service composition evaluation. Furthermore, compared with two other extended algorithms, the proposed MWFPA performs better when addressing the optimal and selection problem.

Trust Enforced Cloud Service Composition Based on Teaching–Learning-Based Optimization – STORM

In the arena of Cloud Computing, the emergence of social networks and IoT increased the number of available services on the cloud platform, making service composition and optimal selection (SCOS) in Cloud Manufacturing (CMfg), NP-hard. The existing approaches for addressing SCOS often fail to offer assistance with maximized trust and satisfied QoS preferences. Hence, this research paper presents a novel TeachIng leaRning-based Optimization aLgorithm (TIROL) for achieving the optimal solution for truST enforced clOud seRvice coMposition (STORM) to assist CMfg for improving the trust value with satisfied QoS preference(s). The performance of the proposed framework has been validated using the synthetic dataset generated from different test-cases. Experimental results show that the proposed framework is reliable and outperforms the SOTA approaches in terms of trust value maximization.

Natural Language Processing for comprehensive service composition in cloud manufacturing systems

Cloud manufacturing (CMfg) is a relatively new manufacturing paradigm which promotes easier collaboration among geographically-dispersed manufacturers. Various manufacturing resources can be shared via cloud manufacturing platform as services. The efficacy of such resource sharing and thus collaboration in this service-oriented mode of manufacturing is highly dependent to the efficiency of underlying mechanisms used for processing these services. Activities such as service matching, service retrieval, service composition and optimal selection, and service scheduling are among the most critical of those service processing mechanisms. In this paper, we introduce and implement a comprehensive service composition and optimal selection (SCOS) technique that takes advantage of a real-world manufacturing capability dataset, deep learning models as well as Word2Vec technique to retrieve appropriate candidate sets for each submitted manufacturing subtask in the cloud manufacturing platform. Then, a genetic algorithm is implemented to obtain a near-optimal composite service that achieves the highest Quality of Service (QoS). A series of experiments were conducted to prove the feasibility and efficacy of the approach and results were presented.

A preference-based multi-objective algorithm for optimal service composition selection in cloud manufacturing

ABSTRACT Aiming at addressing several conflicting criteria of quality of service (QoS) that should be trade-off optimized during service composition and optimal selection (SCOS) in cloud manufacturing (CMfg), the improved non-dominated sorting genetic algorithm III (NSGA-III) is proposed and employed to address the SCOS issue. This is the first time that a preference-based multi-objective algorithm has been used to address the SCOS problem. In this paper, a novel K-layer preference reference point set approach is proposed for generating a reference point set with this algorithm to guide the search towards the interesting parts of the Pareto optimal region based on customer preferences, which improves the efficiency of the algorithm and the convergence of the obtained solution. A new fitness assignment strategy and environment selection scheme is developed accordingly to balance the relationship of diversity and convergence of preserved individuals in each generation. Additionally, the memetic algorithm is integrated into the evolutionary mechanism of the algorithm to address the insufficiency of local search. To validate the performance of the proposed algorithm, several test cases are conducted. The results demonstrate that the proposed algorithm is more competitive than other considered algorithms.

A HRGO approach for resilience enhancement service composition and optimal selection in cloud manufacturing.

Cloud manufacturing (CM) establishes a collaborative manufacturing services chain among dispersed producers, which enables the efficient satisfaction of personalized manufacturing requirements. To further strengthen this effect, the manufacturing service composition and optimal selection (SCOS) in CM, as a NP-hard combinatorial problem, is a crucial issue. Quality of service (QoS) attributes of manufacturing services, as the basic criterion of functions and capabilities, are decisive criterions of SCOS. However, most traditional QoS attributes of CM ignore the dynamic equilibrium of manufacturing services and only rely on initial static characterizations such as reliability and availability. In a high uncertainty and dynamicity environment, a major concern is the equilibrium of manufacturing services for recovering their functions after dysfunctional damage. Therefore, this paper proposes a hybrid resilience-aware global optimization (HRGO) approach to address the SCOS problem in CM. This approach helps manufacturing demanders to acquire efficient, resilient, and satisfying manufacturing services. First, the problem description and resilience measurement method on resilience-aware SCOS is modeled. Then, a services filter strategy, based on the fuzzy similarity degree, is introduced to filter redundant and unqualified candidate services. Finally, a modified non-dominated sorting genetic algorithm (MNSGA-III) is proposed, based on diversity judgment and dualtrack parallelism, to address combination optimization step processing in SCOS. A series of experiments were conducted, the results show the proposed method is more preferable in optimal services searching and more efficient in scalability.

Service Composition and Optimal Selection in Cloud Manufacturing: State-of-the-Art and Research Challenges

Increasing interest in the field of Cloud Manufacturing (CMfg) has been witnessed over the last few years. This study aims to identify current and state-of-the-art techniques and to synthesize quality attributes, objectives, and evaluation methodologies for service composition and optimal selection (SCOS) in the field of CMfg. We used a systematic literature review (SLR) methodology for a thorough analysis of 46 shortlisted primary studies, from a total of 5872 accumulated studies from ten electronic databases. NVivo analysis software was used for data coding and qualitative analysis. A review scope was primarily devised based on research goals, and to uncover potential search strings; a pilot study was formulated. Secondarily, research identification, key data extraction, and deductive coding-based data analysis were performed. Multi-variant distribution approaches were adopted for data categorization. We found that the research in this domain has increased due to the rapid manufacturing urge. Although a few studies were based on industrial evaluations; however, scientific and empirically validated methodologies are still needed in this domain. This study lays an overview of SCOS in the field of CMfg and enlightens the identified future research areas.

Evolutionary algorithms for many-objective cloud service composition: Performance assessments and comparisons

Service composition and optimal selection (SCOS) concerns the building of optimal composite service by integrating existing services with the aim of performing complex task. Due to a plethora of affordable cloud services providing similar functionalities while differing in quality of service (QoS), how to determine suitable candidates to orchestrate the best composite service, also known as QoS-aware SCOS problem, becomes more complicated. A number of evolutionary optimizers have been developed to resolve SCOS. Unfortunately, a large majority of these optimizers carry out the optimization by aggregating many diverse QoS attributes into a single objective or simply considering two or three representative QoS attributes. SCOS, particularly, from the perspective of many-objective optimization, has not received an appropriate attention. As more factors come into play, SCOS is strictly a many-objective problem. This study explores the scalability of recently state-of-the-art evolutionary many-objective optimization (EMaO) algorithms in addressing SCOS. Comparative results reveal that these EMaO algorithms, never before applied to many-objective SCOS, exhibit distinct search abilities with respect to the objective space dimensionality and problem scale. Based on the empirical observation, useful suggestions and insights for choosing suitable EMaO algorithms pertaining to different SCOS problems are given.

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.

Exploring the state-of-the-art service composition approaches in cloud manufacturing systems to enhance upcoming techniques

Cloud manufacturing (CMfg) is a new paradigm that has been known as a promising integrated technology in which distributed resources of manufacturing are integrated and transformed into manufacturing services and handled centrally. It permits multiple users to request services simultaneously by submitting required tasks to a manufacturing platform. The service composition and optimal selection (SCOS) is the fundamental issue of CMfg that aims to select appropriate services from available manufacturing cloud resources to complete the manufacturing task and satisfy the users. Surveying and analyzing the available studies on this NP-hard problem is highly desirable. Therefore, as far as we know, this paper is the first research that tries to investigate and discuss the CMfg-SCOS methods in a systematic way. In this regard, the selected studies have been classified into two distinct groups comprising multi-objective and single-objective techniques. Furthermore, this research provides a comprehensive investigation of the current articles and compares them from several aspects based on various factors such as QoS parameters, other criteria, their adopted datasets, simulation tools, and algorithms. Moreover, hot papers, journals, and authors in this field have been revealed. In addition, the open challenging and the lacks within this issue have been discussed which can be applied to upcoming research.

SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition

Cloud manufacturing (CMfg) is a new service-oriented manufacturing paradigm in which shared resources are integrated and encapsulated as manufacturing services. When a single service is not able to meet some manufacturing requirement, a composition of multiple services is then required via CMfg. Service composition and optimal selection (SCOS) is a key technique for creating an on-demand quality of service (QoS)-optimal efficient manufacturing service composition to satisfy various user requirements. Given the number of services with the same functionality and a similar level of QoS, SCOS has been seen as a key challenge in CMfg research. One effective approach to solving SCOS problems is to use service domain features (SDF) through investigating the probability of services being used for a specific requirement from multiple perspectives. The approach can result in a division of the service space and then help streamline the service space with large-scale candidate services. The approach can also search for optimal subspaces that most likely contribute to an overall optimal solution. Accordingly, this paper develops an SDF-oriented genetic algorithm to effectively create a manufacturing service composition with large-scale candidate services. Fine-grained SDF definitions are developed to divide the service space. SDF-based optimization strategies are adopted. The novelty of the proposed algorithm is presented based on Bayes’ theorem. The effectiveness of the proposed algorithm is validated by solving three real-world SCOS problems in a private CMfg.

Optimal composition of tasks in cloud manufacturing platform: a novel hybrid GWO-GA approach

Abstract Emerging cloud manufacturing paradigm aims towards providing highly integrated solutions via enabling cooperation between distributed manufacturing resources and capabilities. Implementation of this groundbreaking idea, however, is facing serious challenges springing from the currently centralized industrial structures. In order to help it make its way out, researchers have to address a number of pivotal issues in this regard. Service composition and optimal selection (SCOS), which tackles the problem of optimally selecting and combining available resources into a composite service is one of them. To deal with this NP-hard problem, we developed a novel hybrid algorithm based on the recently-introduced grey wolf optimizer (GWO) in which evolutionary operators are also embedded into the hunting mechanism of the basic algorithm. This approach not only makes it possible to adapt an algorithm with continuous structure such as GWO to solve a combinatorial problem such as SCOS, but also empowers it with providing higher exploration through crossover and mutation operators. Experiments conducted clearly proves the superior performance of the proposed algorithm over existing discrete variations of GWO and genetic algorithm, especially in large-scale SCOS problems.

A hybrid grey wolf optimizer algorithm with evolutionary operators for optimal QoS-aware service composition and optimal selection in cloud manufacturing

Cloud manufacturing (CMfg), as a new service-oriented technology, is aiming towards delivering on-demand manufacturing services over the internet by facilitating collaboration among different producers with distributed manufacturing resources and capabilities. To this end, addressing service composition and optimal selection (SCOS) problem has been the pivotal challenge. This NP-hard combinatorial problem deals with selecting and combining the available resources into a composite service to meet the user’s requirements while keeping up the optimal quality of service. This study proposes a new hybrid approach based on the recently developed grey wolf optimizer (GWO) algorithm and evolutionary operators of the genetic algorithm. The embedded crossover and mutation operators carry out a twofold functionality: (1) they make it possible to adapt the continuous structure of GWO to a combinatorial problem such as SCOS, and (2) they help to avoid the local optimal stagnation at the hunting process by providing more exploration strength. A series of experiments were designed and conducted to prove the effectiveness of the proposed algorithm, and the experimental results demonstrated that the proposed algorithm delivers superior performance compared with that of both existing discrete variations of GWO and genetic algorithm, especially in large-scale SCOS problems.

Service composition and optimal selection in cloud manufacturing: landscape analysis and optimization by a hybrid imperialist competitive and local search algorithm

Cloud manufacturing as an emerging service-oriented manufacturing paradigm integrates and manages geographically distributed manufacturing resources such that complex and highly customized manufacturing tasks can be performed cooperatively. The service composition and optimal selection (SCOS) problem, in which manufacturing cloud services are optimally selected for performing subtasks, is one of the key issues for implementing a cloud manufacturing system. In this paper, we propose a new mixed-integer programming model for solving the SCOS problem with sequential composition structure. Unlike the majority of previous research on the problem, in the proposed model, the transportation between distributed resources and its effects on quality of services are considered. Although a wide variety of metaheuristics have been tailored for solving the SCOS problem, no consistent and comprehensive conclusion has been reached so far on the superiority of a specific algorithm. Therefore, for the first time, solution space landscape of the problem was analyzed through several statistical criteria which demonstrated that the landscape is rugged and local optima are clustered in a small region of the search space. Therefore, to find good solutions, a metaheuristic algorithm needs to perform both proper exploitation and exploration of the search space. According to the landscape analysis, the basic imperialist competitive algorithm (ICA) was hybridized with a local search (LS) algorithm resulting in the hybrid ICA (HICA). To examine the performance of the proposed HICA, an example of online motorcycle production in the USA as well as four randomly generated large-scale instances, were solved through the LS, ICA, and HICA. Computational results showed that transportation consideration is indispensable for obtaining more realistic solutions in cloud manufacturing. The results also revealed that the HICA outperformed the LS and basic ICA in terms of the value of cost objective function, the stability of solutions and convergence speed. Hence, not only statistically but also analytically, it was proved that algorithms incorporating both exploitation and exploration are able to solve the SCOS problem more efficiently.

An adaptive multi-population differential artificial bee colony algorithm for many-objective service composition in cloud manufacturing

Several conflicting criteria must be optimized simultaneously during the service composition and optimal selection (SCOS) in cloud manufacturing, among which tradeoff optimization regarding the quality of the composite services is a key issue in successful implementation of manufacturing tasks. This study improves the artificial bee colony (ABC) algorithm by introducing a synergetic mechanism for food source perturbation, a new diversity maintenance strategy, and a novel computing resources allocation scheme to handle complicated many-objective SCOS problems. Specifically, differential evolution (DE) operators with distinct search behaviors are integrated into the ABC updating equation to enhance the level of information exchange between the foraging bees, and the control parameters for reproduction operators are adapted independently. Meanwhile, a scalarization based approach with active diversity promotion is used to enhance the selection pressure. In this proposal, multiple size adjustable subpopulations evolve with distinct reproduction operators according to the utility of the generating offspring so that more computational resources will be allocated to the better performing reproduction operators. Experiments for addressing benchmark test instances and SCOS problems indicate that the proposed algorithm has a competitive performance and scalability behavior compared with contesting algorithms.