scholarly journals A Heuristic Algorithm for Optimal Service Composition in Complex Manufacturing Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Yinan Wu ◽  
Gongzhuang Peng ◽  
Hongwei Wang ◽  
Heming Zhang
Keyword(s):  
Service Composition ◽  
Optimal Path ◽  
Cloud Manufacturing ◽  
Search Procedure ◽  
Manufacturing Networks ◽  
Composition Problem ◽  
Optimal Service ◽  
Manufacturing Platform ◽  
Manufacturing Network ◽  
Composition Path

Service composition in a Cloud Manufacturing environment involves the adaptive and optimal assembly of manufacturing services to achieve quick responses to varied manufacturing needs. It is challenged by the inherent heterogeneity and complexity of these services in terms of their diverse and complex functions, qualities of service, execution paths, etc. In this paper, a manufacturing network is constructed to explicitly identify and describe the relationships between individual services based on their attributes. On this basis, the service composition problem can be modeled as a multiple-constrained optimal path (MCOP) selection problem by taking into account different types of composition, namely, sequence, parallel, selection, and cycle. A novel Dual Heuristic Functions based Optimal Service Composition Path algorithm (DHA_OSCP) is proposed to solve the NP-Complete MCOP problem, which involves exploiting the backward search procedure with different search targets to obtain two heuristic functions for the forward search procedure. The proposed algorithm is evaluated through a set of computational experiments in which the proposed algorithm and other popular algorithms such as MFPB_HOSTP are applied to the same dataset, and the results obtained show that DHA_OSCP can efficiently find the optimal service composition path with better Quality of Service (QoS). The viability of DHA_OSCP is further proved in a case study of services composition on a Cloud Manufacturing platform.

QoS Correlation-based Service Composition Algorithm for Multi-constraint Optimal Path

2021 ◽  
Author(s):  
Jian Yu ◽  
Zhixing Lin ◽  
Qiong Yu ◽  
Xiangmei Xiao
Keyword(s):  
Service Composition ◽  
Optimal Path ◽  
Path Selection ◽  
Approximate Algorithm ◽  
Solution Path ◽  
Network Service ◽  
Integrated Network ◽  
Qos Guarantee ◽  
Optimal Service

Abstract With the development of network service integration, in order to obtain a better quality of service (QoS) guarantee, In this Paper,We consider the characteristics of integrated network service composition and correlation, this paper proposes a approximate algorithm based on multi-constraint optimal path selection (MCOPS). We analyses the QoS correlation criteria, correlation ratios, and Skyline algorithms to calculate the optimal path by dynamic programming, record the path nodes, and obtain the optimal service composition path that meets the user's demand. Simulation results demonstrate the good performance of the proposed algorithm in both the average calculation time and the solution path quality.

scholarly journals Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching

2021 ◽  
Vol 7 ◽  
pp. e461
Author(s):  
Seyed Ali Sadeghi Aghili ◽  
Omid Fatahi Valilai ◽  
Alireza Haji ◽  
Mohammad Khalilzadeh
Keyword(s):  
Service Composition ◽  
Large Scale ◽  
Business Models ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Architecture Model ◽  
Logistics Service ◽  
Composition Problem

Recently, manufacturing firms and logistics service providers have been encouraged to deploy the most recent features of Information Technology (IT) to prevail in the competitive circumstances of manufacturing industries. Industry 4.0 and Cloud manufacturing (CMfg), accompanied by a service-oriented architecture model, have been regarded as renowned approaches to enable and facilitate the transition of conventional manufacturing business models into more efficient and productive ones. Furthermore, there is an aptness among the manufacturing and logistics businesses as service providers to synergize and cut down the investment and operational costs via sharing logistics fleet and production facilities in the form of outsourcing and consequently increase their profitability. Therefore, due to the Everything as a Service (XaaS) paradigm, efficient service composition is known to be a remarkable issue in the cloud manufacturing paradigm. This issue is challenging due to the service composition problem’s large size and complicated computational characteristics. This paper has focused on the considerable number of continually received service requests, which must be prioritized and handled in the minimum possible time while fulfilling the Quality of Service (QoS) parameters. Considering the NP-hard nature and dynamicity of the allocation problem in the Cloud composition problem, heuristic and metaheuristic solving approaches are strongly preferred to obtain optimal or nearly optimal solutions. This study has presented an innovative, time-efficient approach for mutual manufacturing and logistical service composition with the QoS considerations. The method presented in this paper is highly competent in solving large-scale service composition problems time-efficiently while satisfying the optimality gap. A sample dataset has been synthesized to evaluate the outcomes of the developed model compared to earlier research studies. The results show the proposed algorithm can be applied to fulfill the dynamic behavior of manufacturing and logistics service composition due to its efficiency in solving time. The paper has embedded the relation of task and logistic services for cloud service composition in solving algorithm and enhanced the efficiency of resulted matched services. Moreover, considering the possibility of arrival of new services and demands into cloud, the proposed algorithm adapts the service composition algorithm.

Game theory–based multi-task scheduling in cloud manufacturing using an extended biogeography-based optimization algorithm

2019 ◽  
Vol 27 (4) ◽  
pp. 314-330 ◽  
Author(s):  
Jiuhong Xiao ◽  
Wenyu Zhang ◽  
Shuai Zhang ◽  
Xiaoyu Zhuang
Keyword(s):  
Game Theory ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Cloud Manufacturing ◽  
Experimental Simulation ◽  
Scheduling Problem ◽  
Manufacturing Networks ◽  
Proposed Model ◽  
Customer Perspective ◽  
Manufacturing Platform

Cloud manufacturing is an emerging paradigm of global manufacturing networks. Through centralized management and operation of distributed manufacturing services, it can deal with different requirement tasks submitted by multiple customers in parallel. Therefore, the cloud manufacturing multi-task scheduling problem has attracted increasing attention from researchers. This article proposes a new cloud manufacturing multi-task scheduling model based on game theory from the customer perspective. The optimal result for a cloud manufacturing platform is derived from the Nash equilibrium point in the game. As the cloud manufacturing multi-task scheduling problem is known as an NP-hard combinatorial optimization problem, an extended biogeography-based optimization algorithm that embeds three improvements is presented to solve the corresponding model. Compared with the basic biogeography-based optimization algorithm, genetic algorithm, and particle swarm optimization, the experimental simulation results demonstrate that the extended biogeography-based optimization algorithm finds a better schedule for the proposed model. Its benefit is to provide each customer with reliable services that fulfill the demanded manufacturing tasks at reasonable cost and time.

scholarly journals Modelling of cross organizational manufacturing resource service chain based on service supply-demand dynamic matching network

2019 ◽  
Vol 277 ◽  
pp. 01005
Author(s):  
Qingqing Yang ◽  
Jia Liu ◽  
Kewei Yang
Keyword(s):  
Network Theory ◽  
Service Composition ◽  
Manufacturing Systems ◽  
Dynamic Scheduling ◽  
Cloud Manufacturing ◽  
Cloud Services ◽  
Matching Network ◽  
Dynamic Matching ◽  
Composition Problem ◽  
Resource Service

In the cloud manufacturing systems, both manufacturing tasks and manufacturing services are in a dynamic environment. How could cloud manufacturing platform optimizes manufacturing cloud services based on QoS, matching an optimal service composition for manufacturing tasks has become an urgent problem at present. In view of this problem, we study the matching of manufacturing tasks and manufacturing services from the perspective of complex network theory. On the basis of manufacturing task network and manufacturing service network, a dynamic matching network theory model of manufacturing task-service is constructed. And then, we take a dynamic assessment of QoS. Finally, we use load and dynamic QoS as the optimization objectivities, transform the optimal manufacturing service composition problem into the shortest path problem, and the dynamic scheduling of manufacturing services is realized.

Construction of a Cloud Manufacturing Platform for Service Sharing in Manufacturing Industry Park

2015 ◽  
Vol 789-790 ◽  
pp. 1258-1263 ◽  
Author(s):  
Ming Zhi Tan ◽  
Shu Ping Yi ◽  
Rui Zeng ◽  
Zong Lin Guo
Keyword(s):  
Manufacturing Industry ◽  
Cloud Manufacturing ◽  
Utilization Rate ◽  
Trust Evaluation ◽  
Operational Model ◽  
Service Allocation ◽  
Industrial Parks ◽  
Optimal Service ◽  
Key Technologies ◽  
Manufacturing Platform

Industrial Parks thrive in China in the last few decades, but they face the problems of weak accumulative effect, low utilization rate of idle resources and loose regional bonds. To solve these problems, a cloud manufacturing platform is proposed to share services in Industrial Parks. Specially, the principles, architecture and operational model of CMPSS are discussed and investigated to facilitate the inter-enterprises collaboration. Furthermore, four key technologies to implement CMPSS including resource servitization, task decomposition, optimal service allocation and service trust evaluation are conducted to lay a foundation for further research and applications.

On Web Service Composition with QoS Constraint

2011 ◽  
Vol 268-270 ◽  
pp. 1838-1843 ◽  
Author(s):  
Wen Tao Liu
Keyword(s):  
Genetic Algorithm ◽  
Web Service ◽  
Service Composition ◽  
Large Scale ◽  
Optimal Path ◽  
Web Service Composition ◽  
Large Scale Problems ◽  
Optimal Service ◽  
Construct System ◽  
The Web

The software architecture based on web service has become the critical technique to construct system in the distributed environments. The web service composition is the most important method to find the correct service in the complicated application circumstance. The key question is to find service based on the QoS and how to guarantee the quality. This thesis focuses the web service composition in order to get dynamic business cooperation and integration. The key component of web service is discussed and the method of web service composition is analyzed including the formalization verification and service composition architecture and the QoS-aware composition methods. Aimed at the application of web service composition, a method based on approved genetic algorithm is put forward. The simple genetic algorithm based web service composition has many problems such as slow convergence rate and non-optimal service composition. In this paper a genetic algorithm based on niche is provided for the Qos-aware composition and it can get more accurate service composition result and can get the optimal path quickly especially in the large scale problems according to the experiment.

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