Game Theory Based Real-Time Shop Floor Scheduling Strategy and Method for Cloud Manufacturing

Zhang, Yingfeng; Wang, Jin; Liu, Sichao; Qian, Cheng

doi:10.1002/int.21868

Cited by 85 publications

(38 citation statements)

References 40 publications

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“…Form nonfunctional view, [29] developed two algorithms to select services, where one leverages a genetic algorithm and the other combines global optimization with local selection. In order to achieve the real-time data-driven optimization decision, [30] put forward a dynamic optimization model for flexible job shop scheduling based on game theory to provide a new real-time scheduling strategy and method. Focusing on the social collaboration feature of manufacturing services, [31] proposed a service selection model that maximizes the overall synergy effect based on collaboration requirement.…”

Section: Related Workmentioning

confidence: 99%

Bi-Level Programming Model of Cloud Manufacturing Services Based on Extension Theory

Zhao

Zhou

2018

Mathematical Problems in Engineering

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In order to select the proper cloud manufacturing services to satisfy both sides of supplier and demander, a bi-level programming model is proposed based on extension theory in this paper. Firstly, the cloud model is employed to convert qualitative concepts of language description into quantitative values. Then, according to multicriteria assessment information, the satisfactions of both providers and demanders are calculated by extension evaluation. Finally, respectively taking satisfactions of the demanders and providers as optimization goal of upper layer and lower layer, a mathematical model of bi-level programming is constructed which is solved by linear programming. Compared with traditional recommended methods, the proposed method takes full account of the interests of service providers which have long been neglected. Moreover, extension evaluation is applied to reflect the demand and preference of each subject in detail. Experimental results verify the effectiveness and applicability of the proposed model.

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Section: Related Workmentioning

confidence: 99%

Bi-Level Programming Model of Cloud Manufacturing Services Based on Extension Theory

Zhao

Zhou

2018

Mathematical Problems in Engineering

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“…In the second experiment, nine different sizes of the jobs n = (10,15,20,25,30,35,40,45,50) are used to test the proposed method, while five different sizes of the jobs n = (10, 20, 30, 40, 50) are used to do so in the third experiment. For each size of the jobs, the normal processing time for M 1 and M 2 is generated from the discrete uniform distributions [1,10], and the normal setup time for M 1 and M 2 is generated from the discrete uniform distributions [1,4]. For all the tests, t 0 = 0 is set, and the deterioration index are set to be three different values, that is, b = 0.1, 2, and 5 for small, medium, and large, respectively.…”

Section: Parameter Settingmentioning

confidence: 99%

Total completion time minimization for scheduling of two-machine flow shop with deterioration jobs and setup time

Qiao

et al. 2017

Advances in Mechanical Engineering

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This article studies a scheduling problem of a two-machine flow shop with both time-dependent deteriorating jobs and sequence-independent setup time. It is assumed that the deterioration of processing time and setup time is of a linear increasing function with respect to the starting time. The objective is to minimize the total completion time of all jobs. For such a scheduling problem, a mixed integer programming model is developed to find an optimal solution for smallsized problems. For medium-and large-sized problems, a modified variable neighborhood search algorithm is presented. To improve the performance of the algorithm, a greedy algorithm is proposed to find a good initial solution. Also, lower bounds are presented for performance evaluation. Numerical experiments are used to evaluate the effectiveness and efficiency of the proposed method, and the results show that an optimal or near-optimal solution can be obtained.

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“…For example, Zhang et al [33] put forward a dynamic optimization method for shop floor material handling (DOM-SMH) based on real-time and multi-source manufacturing data. Zhang et al [34] also proposed a dynamic optimization model for flexible job shop scheduling (DOM-FJSS) based on real-time data for cloud manufacturing (CMfg).…”

Section: Introductionmentioning

confidence: 99%

Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

Wang

Zhang

Liu

et al. 2019

IEEE Internet Things J.

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With the rapid advancement and widespread applications of information technology in the manufacturing shop floor, a huge amount of real-time data is generated, providing a good opportunity to effectively respond to unpredictable exceptions so that the productivity can be improved. Thus, how to schedule the manufacturing shop floor for achieving such a goal is very challenging. This work addresses this issue and a new multi-agent-based real-time scheduling (MARS) architecture is proposed for an Internet of Things (IoT)-enabled flexible job shop. Differing from traditional dynamic scheduling strategies, the proposed strategy optimally assigns tasks to machines according to their real-time status. A bargaining-game-based negotiation mechanism is developed to coordinate the agents so that the problem can be efficiently solved. To demonstrate the feasibility and effectiveness of the proposed architecture and scheduling method, a proof-of-concept prototype system is implemented with Java agent development framework (JADE) platform. A case study is used to test the performance and effectiveness of the proposed method. Through simulation and comparison, it is shown that the proposed method outperforms the traditional dynamic scheduling strategies in terms of makespan, critical machine workload, and total energy consumption. be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. Recently, investigations have been made by a number of scholars on multi-agent-based dynamic scheduling [22][23]. However, most of these researches mainly focus on the architectures of multi-agent systems (MAS) and negotiation protocols among the agents, as well as the application of distributed features of MAS for task allocation in a traditional manufacturing shop floor [24]. Few of them consider the real-time-data-based interaction between machines and other distributed resources in an IoT-enabled flexible job shop. As a result, often the performance of efficiency is degraded and

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Game Theory Based Real-Time Shop Floor Scheduling Strategy and Method for Cloud Manufacturing

Cited by 85 publications

References 40 publications

Bi-Level Programming Model of Cloud Manufacturing Services Based on Extension Theory

Bi-Level Programming Model of Cloud Manufacturing Services Based on Extension Theory

Total completion time minimization for scheduling of two-machine flow shop with deterioration jobs and setup time

Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

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