Distributed Dynamic Scheduling for Cyber-Physical Production Systems Based on a Multi-Agent System

Jiang, Zhenan; Jian-hui, Yang; Mingcheng, E; Li, Qi

doi:10.1109/access.2017.2780321

Cited by 53 publications

(19 citation statements)

References 26 publications

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“…Karner et al [16] use real-time machine condition data to improve production planning. Jiang et al [17] and Dafflon et al [18] at least include decision making of systems in their publications. The former developed a decision model for a multi-agent system that can dynamically adapt the planning.…”

Section: Basics and Related Researchmentioning

confidence: 99%

Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber-Physical Systems

Freier¹,

Schümann²

2020

WI2020 Zentrale Tracks

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The use of cyber-physical systems in production promises great potential for production scheduling since a larger information base is available for the scheduling of production orders. However, the mere acquisition of realtime data does not inherently lead to improvements. On the contrary, a targeted preparation of the data is required in order to prevent an information overload. Decision support systems that support decision makers in production scheduling can perform this task. However, the design of such systems in combination with cyber-physical systems has hardly been investigated so far. In this paper, we therefore design and implement a corresponding decision support system in a design science approach. For this, we identify meta-requirements based on a literature analysis and an interview study. Finally, we evaluate the created meta-artifact in a laboratory setting in order to obtain generalizable knowledge about building such a decision support system.

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Section: Basics and Related Researchmentioning

confidence: 99%

Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber-Physical Systems

Freier¹,

Schümann²

2020

WI2020 Zentrale Tracks

View full text Add to dashboard Cite

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“…And this approach can work under a real-time environment and generate feasible schedules using negotiation/bidding mechanisms between agents. Jiang et al [34] focused on distributed optimal scheduling based on multi-agent systems including the goals and constraints of the system, a two-layer decision model and the required indicators, the roles and functions of different agents, the dynamic decision cycle and the multistage negotiation mechanism, and a rescheduling algorithm. Tao and Qi [35] proposed a framework-New IT driven service-oriented smart manufacturing, combining the Internet of Things, cloud computing, big data, mobile Internet and cyber-physical systems, which facilitated the visions of smart manufacturing.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Scheduling Framework of the Flexible Mixed-Model Assembly Line Based on the Internet of Manufacturing Things

Shi¹,

Guo²

2020

Preprint

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To satisfy the needs of market customization, the traditional manufacturing is gradually transforming and upgrading into the intelligent manufacturing. In mass customization (MC), the assembly operations of modular products tend to be organized as the form of flexible mixed-model assembly line (FMMAL). The dynamic scheduling problem of FMMAL is quite complex with three issues of the product sequencing, station allocation and material delivery. At the same time, the disruption events of station failure, product inserting and product reworking are also considered. To solve this problem, a comprehensive framework combining the architecture of Internet of Manufacturing Things (IoMT) with the dynamic scheduling algorithms is proposed. Firstly, the IoMT-based FMMAL are constructed via the multi-agent system (MAS) and ubiquitous environment. Secondly, a mathematical model of FMMAL are formulated with the decision variables, optimization objectives and constraint conditions. Thirdly, the IoMT-oriented algorithms for dynamic scheduling are proposed including the fuzzy analytic hierarchy process (FAHP) for normalization, weighted sum of properties-improved genetic algorithm (WSP-IGA) for prescheduling, priority weights search-simulated annealing (PWS-SA) for rescheduling. Lastly, a discrete event simulation of a numerical case is conducted to demonstrate the practicality and validity of proposed theories and algorithms. The results show that the proposed hyper-heuristics (WSP-IGA and PWS-SA) can respectively realize the prescheduling and rescheduling of FMMAL in four modes including the synthesized mode, time-efficient mode, just-in-time mode and energy-saving mode, which are superior to the four referenced meta-heuristics.

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“…to cope with complex problems, MAM has become an effective and popular paradigm penetrating into the field of supply chain. With respect to MAM, many problems on supply chain have been studied, such as platform supply chain networks [38], [39], production scheduling [40], [41], and products management [42], [43].…”

Section: Literature Reviewmentioning

confidence: 99%

Backup or Reliability Improvement Strategy for a Manufacturer Facing Heterogeneous Consumers in a Dynamic Supply Chain

Jiang

2019

IEEE Access

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This paper examines a manufacturer's supply management strategies for mitigating yield risk in a complex dynamic supply chain. Two strategies can be adopted for the manufacturer: backup and reliability improvement. Consumers may select to leave (instant consumers) or wait (delaying consumers) when they confront the manufacturer's insufficient inventory. Utilizing the method of multi-agent modeling, a manufacturer and a supplier are modeled as the intelligent agents with the reinforcement learning behavior. The study shows that: 1) when the number of instant consumers is small, reliability improvement strategy should be selected; otherwise, the manufacturer should adopt a backup strategy; 2) only when mean yield is large enough, reliability improvement strategy is the optimal choice; and 3) if yield uncertainty is small, the manufacturer should choose reliability improvement strategy; otherwise, it is suitable to use a backup strategy. In addition, when the main supplier can determine its own wholesale price, it is found that: 1) when the mean yield is small, a lower wholesale price should be designed for the main supplier, to induce higher order quantity under backup strategy; and 2) the impact of yield uncertainty on the manufacturer's supply management strategy can be changed by the main supplier's adaptive pricing behavior. INDEX TERMS Consumer behavior, multi-agent modeling, supply chain management, yield risk.

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Distributed Dynamic Scheduling for Cyber-Physical Production Systems Based on a Multi-Agent System

Cited by 53 publications

References 26 publications

Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber-Physical Systems

Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber-Physical Systems

Dynamic Scheduling Framework of the Flexible Mixed-Model Assembly Line Based on the Internet of Manufacturing Things

Backup or Reliability Improvement Strategy for a Manufacturer Facing Heterogeneous Consumers in a Dynamic Supply Chain

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