Resource-Aware Aggregate Planning for the Distributed Manufacturing Enterprise

Maropoulos, Paul; McKay, K R; Bramall, David

doi:10.1016/s0007-8506(07)61537-6

Cited by 25 publications

(18 citation statements)

References 10 publications

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“…Aggregation addresses also the various uncertainties of production: it does not allow generating detailed plans for a future that will certainly be different from what we anticipate now. Note that this principle applies well not only in PPS but also in the early stage of product design [1] and engineering [2].…”

Section: Introductionmentioning

confidence: 88%

“…In [11], we suggested polynomial-time bottom-up algorithms for solving such problems. For weight functions 1 W and W ∞ , the exact minimization of height and cardinality is possible using a dynamic programming approach. For the weight function sch W , the suggested algorithm for the exact minimization of height calls the ( log ) O n n complexity scheduler ( ) O n 1 P 2 P times in the worst case, and it can be extended by a greedy algorithm to decrease cardinality.…”

Section: Generating the Optimal Activity Modelmentioning

confidence: 99%

“…In contrast, W ∞ typically overloaded the factory: it often turned out that works planned for some week required even 10-20% more time after detailed scheduling. We did not apply 1 W because there were many discrete resources and the project trees had a balanced tree structure, with several parallel branches. Setup and transportation times had an important role; without them the production plans were too optimistic, consequently, the schedules generated were too tight and practically not executable.…”

Section: Industrial Applicationmentioning

confidence: 99%

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Aggregation - the key to integrating production planning and scheduling

2004

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In this paper we suggest an integrated planning and scheduling framework with a special emphasis on the link between these control levels. Our planning model is generated automatically by performing aggregation on de facto standard product and technology related data in the dimensions of time, resource capacities and operations. The method addresses make-to-order production environments. An industrial case study is also presented, demonstrating how our algorithms work on large-scale problem instances.

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Section: Introductionmentioning

confidence: 88%

Section: Generating the Optimal Activity Modelmentioning

confidence: 99%

Section: Industrial Applicationmentioning

confidence: 99%

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Aggregation - the key to integrating production planning and scheduling

2004

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“…References [14][15][16][17][18][19][20] evaluated some ERP models and their success and failure factors. Tan, etc.…”

Section: Technology Backgroundmentioning

confidence: 99%

“…Moreover, Maropoulos, etc. [19] stated that the integration of the supply chain should focus on the early stages of product development where the majority of product lifecycle cost is decided. Sun, etc.…”

Section: Technology Backgroundmentioning

confidence: 99%

Intelligent Laboratory Resource Supply Chain Conceptual Network Model with Process and Information Integration, Visibility and Flexibility

Hsu

Chao

2009

Algorithms and Architectures for Parallel Processing

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Abstract. This paper proposes a web-based laboratory resource supply chain conceptual model for an educational institution to increase the process and information integration, visibility and flexibility. The proposed model utilizes a reasoning engine with fuzzy, parallel fuzzy rules, and de-fuzzy processes to decide the optimal purchase ordering quantity and the best constant stocks in the laboratory. The fuzzy takes the crisp input data through the characteristic function and maps the input data into its corresponding membership degree. The fuzzy rules are processed with different degree of membership and all rules in the system are processed before triggering an action. The de-fuzzy takes each item's purchase ordering degree of membership through the singleton output membership function and generates corresponding crisp data. The model allows users keying in their required experimental materials via the Web Site, uses the database management system to integrate all related information, and applies the fuzzy reasoning engine to generate the final purchase order reports to support the executor making the optimal decisions.

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Applying Gaia and AUML for the development of multiagent‐based control software for flexible manufacturing systems: addressing methodological and implementation issues

2015

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SUMMARYIn this article, we present the development of a simple multiagent-based system for the control of a flexible manufacturing system. We followed the stages of a methodology specially conceived for the development of agent-based system, which is an integration of the classical methodology for agent-oriented analysis and design Gaia, and AUML (Agent-Unified Modeling Language). We adopted as study case the CIMUBB Laboratory at the University of Bio-Bio, which has a flexible manufacturing system including three flexible manufacturing cells interconnected by a conveyor belt. In the analysis stage, we identified roles involved, and we design models representing roles and protocols. In the design stage, we applied Gaia agent, services, and acquaintance models from Gaia, and we complemented with AUML as the adopted methodology suggests.With the developed models, we constructed a fully functional system where each agent was built as an independent process tree. Agents communicate by passing messages through the Ethernet network with socket interfaces. Various tests executed in our laboratory scale manufacturing system show the effectiveness of our implementation.

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Resource-Aware Aggregate Planning for the Distributed Manufacturing Enterprise

Cited by 25 publications

References 10 publications

Aggregation - the key to integrating production planning and scheduling

Aggregation - the key to integrating production planning and scheduling

Intelligent Laboratory Resource Supply Chain Conceptual Network Model with Process and Information Integration, Visibility and Flexibility

Applying Gaia and AUML for the development of multiagent‐based control software for flexible manufacturing systems: addressing methodological and implementation issues

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