Supporting capacity sharing in the cloud manufacturing environment based on game theory and fuzzy logic

Argoneto, Pierluigi; Renna, Paolo

doi:10.1080/17517575.2014.928950

Cited by 53 publications

(23 citation statements)

References 28 publications

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“…At present, scholars have built various characteristic models for CMfg service selection and scheduling (SSS) problems [7] (or service composition [8], task scheduling [9,10] and service sharing [11,12]). Tao et.al.…”

Section: Introductionmentioning

confidence: 99%

“…Some scholars integrate these factors into a single objective through weight values and design effective algorithms, such as the: parallel method [8], workload-based method [9], two-level method [10], cooperative method [26], chaos control optimal algorithm [16] and improved niche immune algorithm [27]. Some focus on the game theory models [11,12]. Some others pay attention to multi-objective models and algorithms; for instance, Pareto group leader algorithm [14], cloud-entropy enhanced genetic algorithm [23], hybrid artificial bee colony algorithm [18,19], adaptive multi-population differential artificial bee colony algorithm [24], modified particle swarm optimization algorithm [25] and ε-dominance multi-objective evolutionary algorithm [28].In the above studies, novel objective functions attract more attention, and DM's preference for different objectives is rarely considered or simply expressed as a set of weight values [8,17].…”

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confidence: 99%

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Multi-Objective Cloud Manufacturing Service Selection and Scheduling with Different Objective Priorities

Jia

Zong³

et al. 2019

Sustainability

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In recent years, with the support of new information technology and national policies, cloud manufacturing (CMfg) has developed rapidly in China. About CMfg, scholars have conducted extensive and in-depth research, among which multi-objective service selection and scheduling (SSS) attracts increasing attention. Generally, the objectives of the SSS problem involve several aspects, such as time, cost, environment and quality. In order to select an optimal solution, the preference of a decision maker (DM) becomes key information. As one kind of typical preference information, objective priorities are less considered in current studies. So, in this paper, a multi-objective model is first constructed for the SSS with different objective priorities. Then, a two-phase method based on the order of priority satisfaction (TP-OPS) is designed to solve this problem. Finally, computational experiments are conducted for problems with different services and tasks/subtasks, as well as different preference information. The results show that the proposed TP-OPS method can achieve a balance between the maximum comprehensive satisfaction and satisfaction differences, which is conducive to the sustainable development of CMfg. In addition, the proposed method allows the preference information to be gradually clarified, which has the advantage of providing convenience to DM.Sustainability 2019, 11, 4767 2 of 24 common feature of these models is that multiple factors need to be considered in the objectives, for example, time [14][15][16], cost [14][15][16][17], reliability [14-16] and energy consumption [14,[17][18][19], risk [17], service [20], trust [21], availability [22], value [23], reputation [19,24] and workload [25]. Some scholars integrate these factors into a single objective through weight values and design effective algorithms, such as the: parallel method [8], workload-based method [9], two-level method [10], cooperative method [26], chaos control optimal algorithm [16] and improved niche immune algorithm [27]. Some focus on the game theory models [11,12]. Some others pay attention to multi-objective models and algorithms; for instance, Pareto group leader algorithm [14], cloud-entropy enhanced genetic algorithm [23], hybrid artificial bee colony algorithm [18,19], adaptive multi-population differential artificial bee colony algorithm [24], modified particle swarm optimization algorithm [25] and ε-dominance multi-objective evolutionary algorithm [28].In the above studies, novel objective functions attract more attention, and DM's preference for different objectives is rarely considered or simply expressed as a set of weight values [8,17]. In the decision-making process, preference information is critical due to the fact that only one solution needs to be selected as the final scheme. Therefore, considering preference information in the model or algorithm becomes a new research direction, which can provide convenience for DM to choose satisfactory solutions. So, in our previous paper [29], linguistic preference is taken into...

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Multi-Objective Cloud Manufacturing Service Selection and Scheduling with Different Objective Priorities

Jia

Zong³

et al. 2019

Sustainability

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“…It builds an extra computation for private clouds that allows users to perform workloads, that is, how to offload workloads from users to both public and private clouds [5]. Based on the concerns of public cloud service reliability and security, a hybrid cloud solution is proposed, which can make full use of public cloud resources to supplement the local private cloud resources [6]. Game theory of thought modeling can well analyze each player's strategy.…”

Section: Related Workmentioning

confidence: 99%

Public Cloud Cost Analysis Based on Game Theory

Chen¹

2018

Proceedings of the 2018 International Conference on Sports, Arts, Education and Management Engineering (SAEME 2018)

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Abstract. In a hybrid cloud computing environment, how to divide workload reasonably is a problem that every rational user needs to consider. The game model of hybrid cloud computing is constructed. The cost is also taken into consideration when the user usually considers the response time. The function which takes the response time and the cost as the variable is the utility function. By solving the Nash equilibrium method, the user's strategy behavior is analyzed, and the optimal strategy of the user is determined. Through the simulation, the influence of different response time and expense ratio on the user equilibrium strategy is given and compared.

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“…DF i,j , CS i,j , BS i,j 0.8540 C 10 DF i,j , CS i,j , QU i,j 0.7149 C 11 DF i,j , BS i,j , ME i,j 0.4255 C 12 NP i,j , MS i,j , SD i,j 0.9206 C 13 NP i,j , MS i,j , TC i,j 0.8757 C 14 NP i,j , MA i,j 0.4294 C 15 NP i,j , CS i,j , TC i,j 0.8521 C 16 NP i,j , ME i,j 0.8038 C 17 MS i,j , SD i,j , RU i,j 0.9946 C 18 MS i,j , RS i,j , TC i,j 0.1193 C 19 MS i,j , QU i,j 0.8209 C 20 MS i,j , TC i,j , RU i,j 0.5309 C 21 MA i,j , PE i,j 0.9671 C 22 MA i,j , RU i,j 0.6952 C 23 OD i,j , RU i,j 0.2307 C 24 SD i,j , TE i,j 0.7174 C 25 BS i,j , ME i,j , TE i,j 0.6907 C 26 RS i,j , ME i,j , TE i,j 0.8250 C 27 ME i,j , PE i,j 0.9906 …”

Section: Cliquementioning

confidence: 99%

Probabilistic Graphical Framework for Estimating Collaboration Levels in Cloud Manufacturing

2017

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Cloud manufacturing (CM) is an emerging manufacturing model based on collaboration among manufacturing enterprises in a cloud computing environment. Naturally, collaboration is one of main factors that impacts performance in a variety of ways such as quality, lead time, and cost. Therefore, collaboration levels should be considered when solving operational issues in CM. However, there has been no attempt to estimate these levels between enterprises participating in CM. The collaboration level among enterprises in CM is defined as the ability to produce a manufacturing service that satisfies a customer by means of collaborative production amongst enterprises. We measure it as the conditional probability that collaborative performances are high given collaborative performance factors (e.g., resource sharing, information sharing, etc.). In this paper, we propose a framework for estimating collaboration levels. We adopt a probabilistic graphical model (PGM) to develop the framework, since the framework includes a lot of random variables and complex dependencies among them. The framework yields conditional probabilities that two enterprises will reduce the total cost, improve resource utilization or quality through collaboration between them given each enterprise's features, collaboration possibility, and collaboration activities. The collaboration levels the proposed framework yields will help to handle diverse operational problems in CM.

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Supporting capacity sharing in the cloud manufacturing environment based on game theory and fuzzy logic

Cited by 53 publications

References 28 publications

Multi-Objective Cloud Manufacturing Service Selection and Scheduling with Different Objective Priorities

Multi-Objective Cloud Manufacturing Service Selection and Scheduling with Different Objective Priorities

Public Cloud Cost Analysis Based on Game Theory

Probabilistic Graphical Framework for Estimating Collaboration Levels in Cloud Manufacturing

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