Agent-based supply chain management—1: framework

Julka, Nirupam; Srinivasan, Rajagopalan; Karimi, Iftekhar A.

doi:10.1016/s0098-1354(02)00150-3

Cited by 193 publications

(78 citation statements)

References 29 publications

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“…To overcome this weakness, Julka, etc. [12] proposed a unified framework which integrates different ERP elements such as the production processes and the associated business data and knowledge to model, monitor, manage, and analyze the business polices. Moreover, in order to rapidly response to a dynamic global market and satisfy frequently changing customer demands, Ni, 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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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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“…[7][8][9] Multiagent-based approaches have also been applied for simulating distributed systems with somewhat decentralized decision-making (especially for short-term decisions). 10,11 In all the cases, the different players in the SC are represented by agents who are able to make autonomous decisions based on the information they have available and the messages they receive. The agents include warehouses, customers, plants and logistics functions.…”

Section: Simulation-based Approachesmentioning

confidence: 99%

Addressing the scheduling of chemical supply chains under demand uncertainty

2006

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A multistage stochastic optimization model is presented to address the scheduling of supply chains with embedded multipurpose batch chemical plants under demand uncertainty. In order to overcome the numerical difficulties associated with the resulting large-scale stochastic mixed-integer-linear-programming (MILP) problem, an approximation strategy comprising two steps, and based on the resolution of a set of deterministic and two-stage stochastic models is presented. The performance of the proposed strategy regarding computation time and optimality gap is studied through comparison with other traditional approaches that address optimization under uncertainty. Results indicate that the proposed strategy provides better solutions than stand-alone two-stage stochastic programming and two-stage shrinking-horizon algorithms for similar computational efforts and incurs much lower computation times than the rigorous multistage stochastic model. © 2006 American Institute of Chemical Engineers AIChE J, 2006Peer ReviewedPostprint (published version

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“…The first line of attack for studying supply networks as CAS has been agent-based modeling, where researchers modeled system characteristics such as decentralized data, asynchronous decisions, and impartial knowledge to devise better planning and coordination policies (e.g., [16], [25], [35]). Due to a chronic lack of empirical studies on emergent supply networks, such models were typically small scale and carry simplistic assumptions on emergent system structure.…”

Section: Introductionmentioning

confidence: 99%

The Nested Structure of Emergent Supply Networks

Brintrup

Barros

Tiwari

2018

IEEE Systems Journal

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Abstract-Inspired by studies in ecological networks, we look for a nested pattern in a large-scale data set describing the global automotive industry, including more than 18 000 firms, their clients, and products. Two bipartite networks are formed, namely, supplier-product distribution and supplier-manufacturer relations. Both networks are found to be significantly nested. The pattern means that suppliers produce proper subsets of what other suppliers produce and rare products are produced only by those suppliers that already produce high numbers of product types. In addition, the manufacturers that procure from few suppliers procure from those that supply to most other manufacturers in the network. Similarly, suppliers that supply to few supply to those manufacturers that procure from most others. A nested structure is more robust than a nonnested structure as disrupted suppliers can be substituted, but nestedness also means that small suppliers face more competition as their production can be redundant. Our finding is contrary to conventional wisdom that associates large diversified firms with efficiency and small specialist firms with rare products, showing that large-scale complex system analysis can lead to the discovery of important systemic characteristics, which are obscured when viewed from local points of view. We then propose a multiagent model that creates more realistic nested structures to study systemic outcomes influenced by topology.

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Agent-based supply chain management—1: framework

Cited by 193 publications

References 29 publications

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

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

Addressing the scheduling of chemical supply chains under demand uncertainty

The Nested Structure of Emergent Supply Networks

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